IBM Fundamentals of Applying Tivoli Service Delivery and Process Automation Solutions V3

Fundamentals of Applying Tivoli Service Delivery and Process Automation Solutions V3
000-591 Exam Dumps | Real Exam Questions | 000-591 VCE Practice Test

000-591 Exam Dumps Contains Questions From Real 000-591 Exam

Read and Memorize these 000-591 braindumps
At, they provide Latest, Valid and Updated 000-591 000-591 dumps that are the most effective to pass 000-591 exam. It is a best to boost up your position as a professional within your organization. They have their reputation to help people pass the 000-591 exam in their first attempt. Performance of their braindumps remain at top within last two years. Thanks to their 000-591 dumps customers that trust their PDF and VCE for their real 000-591 exam. is the best in 000-591 real exam questions. They keep their 000-591 dumps valid and updated all the time.

000-591 000-591 exam is not too easy to prepare with only 000-591 text books or free PDF dumps available on internet. There are several tricky questions asked in real 000-591 exam that cause the candidate to confuse and fail the exam. This situation is handled by by collecting real 000-591 question bank in form of PDF and VCE exam simulator. You just need to download 100% free 000-591 PDF dumps before you register for full version of 000-591 question bank. You will satisfy with the quality of braindumps.

Features of Killexams 000-591 braindumps

-> Instant 000-591 Dumps download Access
-> Comprehensive 000-591 Questions and Answers
-> 98% Success Rate of 000-591 Exam
-> Guaranteed Real 000-591 exam Questions
-> 000-591 Exam Updated on Regular basis.
-> Valid 000-591 Exam Dumps
-> 100% Portable 000-591 Exam Files
-> Full featured 000-591 VCE Exam Simulator
-> Unlimited 000-591 Exam Download Access
-> Great Discount Coupons
-> 100% Secured Download Account
-> 100% Confidentiality Ensured
-> 100% Success Guarantee
-> 100% Free Dumps Questions for evaluation
-> No Hidden Cost
-> No Monthly Charges
-> No Automatic Account Renewal
-> 000-591 Exam Update Intimation by Email
-> Free Technical Support

Simply memorize these 000-591 braindumps Questions give latest and valid Pass4sure 000-591 Practice Test with Actual 000-591 Exam Questions and Answers for candidate to pass IBM 000-591 Exam. Practice their 000-591 Real Questions and Answers for the improvement of your topic knowledge. They 100% guarantee that you will pass 000-591 exam with no trouble. You will feel accuracy of your decision when you choose

000-M20 | 000-484 | LOT-409 | 000-m240 | C2170-008 | 000-N27 | 000-370 | 000-208 | 000-705 | M2020-618 | C2090-461 | C9520-911 | 000-068 | 000-375 | A2070-580 | 000-887 | BAS-004 | C4090-453 | M2050-243 | C5050-408 |

Accelerating the discovery of materials for clear energy within the era of smart automation

  • 1.

    Dunn, B., Kamath, H. & Tarascon, J.-M. electrical energy storage for the grid: a battery of choices. Science 334, 928–935 (2011).

  • 2.

    She, X., Huang, A. Q. & Burgos, R. assessment of solid-state transformer technologies and their utility in power distribution techniques. IEEE J. Emerg. Sel. exact. vigor Electron. 1, 186–198 (2013).

  • 3.

    Mahlia, T. M. I., Saktisahdan, T. J., Jannifar, A., Hasan, M. H. & Matseelar, H. S. C. A evaluation of attainable methods and building on power storage; know-how update. Renew. maintain. energy Rev. 33, 532–545 (2014).

  • four.

    Chabot, V. et al. A review of graphene and graphene oxide sponge: cloth synthesis and applications to energy and the atmosphere. energy Environ. Sci. 7, 1564–1596 (2014).

  • 5.

    Ferreira, A. D. B., Nóvoa, P. R. & Marques, A. T. Multifunctional cloth systems: a state-of-the-artwork review. Compos. Struct. 151, three–35 (2016).

  • 6.

    Werber, J. R., Osuji, C. O. & Elimelech, M. materials for subsequent-era desalination and water purification membranes. Nat. Rev. Mater. 1, 16018 (2016).

  • 7.

    Maine, E. & Garnsey, E. Commercializing widely wide-spread know-how: the case of advanced substances ventures. Res. coverage 35, 375–393 (2006).

  • eight.

    Linton, J. D. & Walsh, S. T. From bench to enterprise. Nat. Mater. 2, 287–289 (2003).

  • 9.

    Sabatier, M. & Chollet, B. Is there a primary mover knowledge in science? Pioneering habits and scientific construction in nanotechnology. Res. policy forty six, 522–533 (2017).

  • 10.

    Jackson, R. B. in New U.S. leadership, next Steps on local weather alternate (ed. Hayes, D. J.) 129–one hundred thirty five (Stanford Woods Institute for the ambiance, Stanford, CA, usa, 2016).

  • 11.

    Georgeson, L., Maslin, M. & Poessinouw, M. clean up energy innovation. Nature 538, 27–29 (2016).

  • 12.

    Bernstein, A. et al. Renewables want a grand-challenge method. Nature 538, 30 (2016).

  • 13.

    [No authors listed.] the primary 5 years of the substances genome initiative: accomplishments and technical highlights. substances Genome Initiative (2016).

  • 14.

    eco-friendly, M. L. et al. fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Appl. Phys. Rev. 4, 011105 (2017).

  • 15.

    UNFCCC. Adoption of the Paris contract. document No. FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015).

  • 16.

    Northrop, E., Biru, H., Lima, S., Bouyé, M. & music, R. analyzing the alignment between the intended nationally decided contributions and sustainable construction desires. World supplies Institute (2016).

  • 17.

    Knight, W. The dark secret on the coronary heart of AI. MIT know-how overview heart-of-ai/ (2017).

  • 18.

    Ley, S. V., Fitzpatrick, D. E., Ingham, R. J. & Myers, R. M. biological synthesis: march of the machines. Angew. Chem. Int. Ed. 54, 3449–3464 (2015).

  • 19.

    Schrage, M. four models for the use of AI to make selections. Harvard business overview usage of-ai-to-make-choices (2017).

  • 20.

    Geysen, H. M., Meloen, R. H. & Barteling, S. J. Use of peptide synthesis to probe viral antigens for epitopes to a decision of a single amino acid. Proc. Natl Acad. Sci. united states of america 81, 3998–4002 (1984).

  • 21.

    Doyel, P. M. Combinatorial chemistry in the discovery and building of drugs. J. Chem. Technol. Biotechnol. 64, 317–324 (1995).

  • 22.

    Borman, S. Combinatorial chemistry. Chem. Eng. information seventy six, 47–sixty seven (1998).

  • 23.

    Nikolaev, P. et al. Autonomy in substances analysis: a case analyze in carbon nanotube boom. Comput. Mater. 2, 16031 (2016).

  • 24.

    Wigley, P. B. et al. quickly desktop-discovering on-line optimization of extremely-bloodless-atom experiments. Sci. Rep. 6, 25890 (2016).

  • 25.

    Xue, D. et al. Accelerated look for substances with targeted homes through adaptive design. Nat. Commun. 7, 11241 (2016).

  • 26.

    Houben, C. & Lapkin, A. A. automated discovery and optimization of chemical methods. Curr. Opin. Chem. Eng. 9, 1–7 (2015).

  • 27.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep discovering. Nature 521, 436–444 (2015).

  • 28.

    Shahriari, B., Swersky, okay., Wang, Z., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016).

  • 29.

    Allen, ok. How a Toronto professor’s analysis revolutionized synthetic intelligence. (2015).

  • 30.

    Gibney, E. Google AI algorithm masters historic game of Go. Nature 529, 445–446 (2016).

  • 31.

    Cisco Public. Encrypted traffic analytics. Cisco (2018).

  • 32.

    Basuchoudhary, A., Bang, J. T. & Sen, T. laptop-gaining knowledge of techniques in Economics. (Springer, Berlin, 2017).

  • 33.

    Mullainathan, S. & Spiess, J. computing device learning: an utilized econometric strategy. J. Econ. Perspect. 31, 87–106 (2017).

  • 34.

    Rao, A. Digital twins past the industrials. PWC (2017).

  • 35.

    Magoulas, G. D. & Prentza, A. in computing device studying and Its purposes. ACAI 1999. Lecture Notes in laptop Science Vol 2049 (eds Paliouras, G., Karkaletsis, V. & Spyropoulos, C. D.) 300–307 (Springer, Berlin, 2001).

  • 36.

    Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C. & Ng, A. Y. cardiologist-degree arrhythmia detection with convolutional neural networks. Preprint at arXiv, 1707.01836 (2017).

  • 37.

    Esteva, A. et al. Dermatologist-level classification of epidermis melanoma with deep neural networks. Nature 542, 115–118 (2017).

  • 38.

    Shen, G., Horikawa, T., Majima, okay. & Kamitani, Y. Deep photograph reconstruction from human brain undertaking. Preprint at bioRxiv, 240317 (2017).

  • 39.

    Goh, G. B., Hodas, N. O. & Vishnu, A. Deep studying for computational chemistry. J. Comput. Chem. 38, 1291–1307 (2017).

  • 40.

    Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at drive container computational cost. Chem. Sci. 8, 3192–3203 (2017).

  • forty one.

    Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. Preprint at arXiv, 1704.01212 (2017).

  • forty two.

    Matlock, M. k., Dang, N. L. & Swamidass, S. J. discovering a native-variable model of aromatic and conjugated techniques. ACS Cent. Sci. 4, 52–sixty two (2018).

  • 43.

    Jiménez, J., Škalic, M., Martinez-Rosell, G. & De Fabritiis, G. KDEEP: protein–ligand absolute binding affinity prediction by way of 3D-convolutional neural networks. J. Chem. Inf. model. fifty eight, 287–296 (2018).

  • 44.

    Wang, H. & Yeung, D.-Y. in opposition t Bayesian deep learning: a framework and a few current strategies. IEEE Trans Knowl. information Eng 28, 3395–3408 (2016).

  • 45.

    Ehsan Abbasnejad, M., Shi, Q., Abbasnejad, I., van den Hengel, A. & Dick, A. Bayesian conditional generative adverserial networks. Preprint at arXiv, 1706.05477 (2017).

  • 46.

    Häse, F., Roch, L. M., Kreisbeck, C. & Aspuru-Guzik, A. PHOENICS: a customary deep Bayesian optimizer. Preprint at arXiv, 1801.01469 (2018).

  • 47.

    Hansen, ok. et al. evaluation and validation of laptop gaining knowledge of strategies for predicting molecular atomization energies. J. Chem. Theor. Comput. 9, 3404–3419 (2013).

  • 48.

    Brockherde, F. et al. Bypassing the Kohn–Sham equations with computing device studying. Nat. Commun. 8, 872 (2017).

  • 49.

    Li, Z., Kermode, J. R. & De Vita, A. Molecular dynamics with on-the-fly computing device learning of quantum-mechanical forces. Phys. Rev. Lett. 114, 096405 (2015).

  • 50.

    Schütt, okay. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, okay.-R. SchNet — a deep learning architecture for molecules and materials. Preprint at arXiv, 1712.06113 (2017).

  • fifty one.

    Gómez-Bombarelli, R. et al. automatic chemical design the use of a knowledge-pushed continuous illustration of molecules. ACS Cent. Sci. 4, 268–276 (2018).

  • 52.

    Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J. & Chen, H. software of generative autoencoder in de novo molecular design. Mol. Inf. 37, 1700123 (2018).

  • 53.

    Sánchez-Lengeling, B., Outeiral, C., Guimaraes, G. L. & Aspuru-Guzik, A. Optimizing distributions over molecular house. An objective-reinforced generative adversarial community for inverse-design chemistry (biological). Preprint at ChemRxiv (2017).

  • fifty four.

    Kadurin, A., Nikolenko, S., Khrabrov, k., Aliper, A. & Zhavoronkov, A. druGAN: an advanced generative adversarial autoencoder mannequin for de novo technology of latest molecules with preferred molecular residences in silico. Mol. Pharm. 14, 3098–3104 (2017).

  • fifty five.

    Grover, A., Dhar, M. & Ermon, S. circulation-GAN: combining optimum likelihood and adversarial studying in generative models. Preprint at arXiv, 1705.08868 (2017).

  • 56.

    Duros, V. et al. Human versus robots within the discovery and crystallization of colossal polyoxometalates. Angew. Chem. Int. Ed. fifty six, 10815–10820 (2017).

  • 57.

    Zhou, Z., Li, X. & Zare, R. N. Optimizing chemical reactions with deep reinforcement gaining knowledge of. ACS Cent. Sci. 3, 1337–1344 (2017).

  • 58.

    King, R. D. et al. The automation of science. Science 324, eighty five–89 (2009).

  • 59.

    Trancik, J. E. Renewable power: lower back the renewables boom. Nature 507, 300–302 (2014).

  • 60.

    Naims, H. Economics of carbon dioxide catch and utilization — a give and demand perspective. Environ. Sci. Pollut. Res. 23, 22226–22241 (2016).

  • 61.

    Muratori, M. et al. Carbon catch and storage across fuels and sectors in energy device transformation pathways. Int. J. Greenhouse gas handle fifty seven, 34–forty one (2017).

  • 62.

    Tzimas, E. et al. CO2 utilisation nowadays: record 2017. DepositOnce (2017).

  • sixty three.

    Kuhl, k. P., Cave, E. R., Abram, D. N. & Jaramillo, T. F. New insights into the electrochemical discount of carbon dioxide on metallic copper surfaces. energy Environ. Sci. 5, 7050–7059 (2012).

  • sixty four.

    Kuhl, ok. P. et al. Electrocatalytic conversion of carbon dioxide to methane and methanol on transition steel surfaces. J. Am. Chem. Soc. 136, 14107–14113 (2014).

  • 65.

    Roberts, F. S., Kuhl, ok. P. & Nilsson, A. excessive selectivity for ethylene from carbon dioxide discount over copper nanocube electrocatalysts. Angew. Chem. Int. Ed. 127, 5268–5271 (2015).

  • sixty six.

    Reymond, H., Vitas, S., Vernuccio, S. & von Rohr, P. R. response system of resin-catalyzed methyl formate hydrolysis in biphasic continual movement. Ind. Eng. Chem. Res. fifty six, 1439–1449 (2017).

  • sixty seven.

    Behrens, M. Heterogeneous catalysis of CO2 conversion to methanol on copper surfaces. Angew. Chem. Int. Ed. 53, 12022–12024 (2014).

  • 68.

    Kattel, S., Ramírez, P. J., Chen, J. G., Rodriguez, J. A. & Liu, P. lively sites for CO2 hydrogenation to methanol on Cu/ZnO catalysts. Science 355, 1296–1299 (2017).

  • sixty nine.

    U.S. power information agency. Manufacturing energy Consumption Survey (MECS) 2014 (U.S. energy information agency, 2014).

  • 70.

    Reymond, H., Amado-Blanco, V., Lauper, A. & Rudolf von Rohr, P. interaction between reaction and part behaviour in carbon dioxide hydrogenation to methanol. ChemSusChem 10, 1166–1174 (2017).

  • seventy one.

    Kondratenko, E. V., Mul, G., Baltrusaitis, J., Larrazabal, G. O. & Perez-Ramirez, J. repute and views of CO2 conversion into fuels and chemicals with the aid of catalytic, photocatalytic and electrocatalytic procedures. energy Environ. Sci. 6, 3112–3135 (2013).

  • seventy two.

    Olah, G. A. beyond oil and fuel: the methanol economy. Angew. Chem. Int. Ed. forty four, 2636–2639 (2005).

  • 73.

    Lal, R. Soil carbon sequestration to mitigate local weather exchange. Geoderma 123, 1–22 (2004).

  • 74.

    Lal, R., Negassa, W. & Lorenz, ok. Carbon sequestration in soil. Curr. Opin. Environ. preserve. 15, 79–86 (2015).

  • 75.

    Williamson, P. Emissions reduction: scrutinize CO2 removing strategies. Nature 530, 153–a hundred and fifty five (2016).

  • seventy six.

    Marshall, C. In Switzerland, a giant new computer is sucking carbon at once from the air. Science (2017).

  • seventy seven.

    Man, I. C. et al. Universality in oxygen evolution electrocatalysis on oxide surfaces. ChemCatChem three, 1159–1165 (2011).

  • 78.

    Montoya, J. H., Tsai, C., Vojvodic, A. & Nørskov, J. ok. The problem of electrochemical ammonia synthesis: a brand new point of view on the function of nitrogen scaling members of the family. ChemSusChem eight, 2180–2186 (2015).

  • 79.

    Studt, F. et al. Discovery of a Ni–Ga catalyst for carbon dioxide reduction to methanol. Nat. Chem. 6, 320–324 (2014).

  • 80.

    Benck, J. D., Hellstern, T. R., Kibsgaard, J., Chakthranont, P. & Jaramillo, T. F. Catalyzing the hydrogen evolution response (HER) with molybdenum sulfide nanomaterials. ACS Catal. 4, 3957–3971 (2014).

  • 81.

    Montoya, J. H. et al. materials for photo voltaic fuels and chemical substances. Nat. Mater. 16, 70–81 (2017).

  • 82.

    Ma, X., Li, Z., Achenie, L. E. k. & Xin, H. laptop-researching-augmented chemisorption mannequin for CO2 electroreduction catalyst screening. J. Phys. Chem. Lett. 6, 3528–3533 (2015).

  • eighty three.

    Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. okay. To handle floor reaction network complexity the usage of scaling relations laptop getting to know and DFT calculations. Nat. Commun. eight, 14621 (2017).

  • eighty four.

    Montoya, J. H. & Persson, k. A. A high-throughput framework for picking adsorption energies on strong surfaces. Comput. Mater. 3, 14 (2017).

  • 85.

    Lysgaard, S., Landis, D. D., Bligaard, T. & Vegge, T. Genetic algorithm procreation operators for alloy nanoparticle catalysts. excellent. Catal. fifty seven, 33–39 (2014).

  • 86.

    Vilhelmsen, L. B. & Hammer, B. A genetic algorithm for first concepts international structure optimization of supported nano constructions. J. Chem. Phys. 141, 044711 (2014).

  • 87.

    Rosenbrock, C. W., Homer, E. R., Csányi, G. & Hart, G. L. W. Discovering the building blocks of atomic programs the usage of computer discovering: utility to grain boundaries. Comput. Mater. 3, 29 (2017).

  • 88.

    Jinnouchi, R. & Asahi, R. Predicting catalytic exercise of nanoparticles by a DFT-aided machine-studying algorithm. J. Phys. Chem. Lett. eight, 4279–4283 (2017).

  • 89.

    Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I. & Nørskov, J. okay. Computational excessive-throughput screening of electrocatalytic substances for hydrogen evolution. Nat. Mater. 5, 909–913 (2006).

  • ninety.

    García-Mota, M., Vojvodic, A., Abild-Pedersen, F. & Nørskov, J. ok. electronic origin of the surface reactivity of transition-steel-doped TiO2(110). J. Phys. Chem. C 117, 460–465 (2013).

  • 91.

    Hummelshøj, J. S., Abild-Pedersen, F., Studt, F., Bligaard, T. & Nørskov, J. okay. CatApp: an internet software for surface chemistry and heterogeneous catalysis. Angew. Chem. Int. Ed. 51, 272–274 (2012).

  • 92.

    Tran, R. et al. floor energies of elemental crystals. Sci. information three, 160080 (2016).

  • ninety three.

    Kalidindi, S. R., Medford, A. J. & McDowell, D. L. vision for data and informatics in the future substances innovation ecosystem. JOM sixty eight, 2126–2137 (2016).

  • ninety four.

    green, M. A. business growth and challenges for photovoltaics. Nat. energy 1, 15015 (2016).

  • ninety five.

    Haegel, N. M. et al. Terawatt-scale photovoltaics: trajectories and challenges. Science 356, 141–143 (2017).

  • ninety six.

    Kojima, A., Teshima, k., Shirai, Y. & Miyasaka, T. Organometal halide perovskites as seen-gentle sensitizers for photovoltaic cells. J. Am. Chem. Soc. 131, 6050–6051 (2009).

  • ninety seven.

    Tan, H. et al. effective and reliable solution-processed planar perovskite photo voltaic cells by way of contact passivation. Science 355, 722–726 (2017).

  • 98.

    countrywide Renewable power Laboratory. most beneficial analysis-telephone efficiencies. countrywide Renewable energy Laboratory (2016).

  • ninety nine.

    Shin, S. S. et al. Colloidally organized La-doped BaSnO3 electrodes for productive, photostable perovskite photo voltaic cells. Science 356, 167–171 (2017).

  • a hundred.

    Li, G., Zhu, R. & Yang, Y. Polymer solar cells. Nat. Photonics 6, 153–161 (2012).

  • a hundred and one.

    Gaudiana, R. & Brabec, C. J. staggering plastic. Nat. Photonics 2, 287 (2008).

  • 102.

    Hoth, C. N., Schilinsky, P., Choulis, S. A., Balasubramanian, S. & Brabec, C. J. in applications of biological and Printed Electronics (ed. Cantatore, E.) 27–fifty six (Springer US, Boston, MA, 2013).

  • 103.

    Al-Ibrahim, M., Roth, H.-k., Zhokhavets, U., Gobsch, G. & Sensfuss, S. flexible enormous enviornment polymer solar cells in accordance with poly(three-hexylthiophene)/fullerene. Sol. power Mater. Sol. Cells 85, 13–20 (2005).

  • 104.

    Kaltenbrunner, M. et al. Ultrathin and light organic solar cells with high flexibility. Nat. Commun. 3, 770 (2012).

  • one hundred and five.

    Schubert, M. B. & Werner, J. H. bendy photo voltaic cells for apparel. Mater. nowadays 9, forty two–50 (2006).

  • 106.

    Salvador, M. et al. Suppressing photooxidation of conjugated polymers and their blends with fullerenes through nickel chelates. power Environ. Sci. 10, 2005–2016 (2017).

  • 107.

    Henemann, A. BIPV: constructed-in photo voltaic energy. Renew. energy focus 9, 14–19 (2008).

  • 108.

    Azzopardi, B. et al. financial assessment of photo voltaic electricity production from biological-based photovoltaic modules in a home ambiance. energy Environ. Sci. 4, 3741–3753 (2011).

  • 109.

    Li, N. & Brabec, C. J. Washing away barriers. Nat. energy 2, 772–773 (2017).

  • 110.

    Perea, J. D. et al. Introducing a new competencies figure of benefit for evaluating microstructure balance in photovoltaic polymer-fullerene blends. J. Phys. Chem. C 121, 18153–18161 (2017).

  • 111.

    Teichler, A. et al. Combinatorial screening of polymer:fullerene blends for organic solar cells by means of inkjet printing. Adv. energy Mater. 1, one hundred and five–114 (2011).

  • 112.

    Chen, S. et al. Exploring the balance of novel vast bandgap perovskites by means of a robotic based high throughput approach. Adv. power Mater. 8, 1701543 (2018).

  • 113.

    Lawrence Livermore country wide Laboratory. energy movement charts. LLNL circulate Charts (2016).

  • 114.

    Zebarjadi, M., Esfarjani, ok., Dresselhaus, M. S., Ren, Z. F. & Chen, G. perspectives on thermoelectrics: from fundamentals to device applications. power Environ. Sci. 5, 5147–5162 (2012).

  • one hundred fifteen.

    Biswas, okay. et al. high-efficiency bulk thermoelectrics with all-scale hierarchical architectures. Nature 489, 414–418 (2012).

  • 116.

    Aydemir, U. et al. YCuTe2: a member of a new category of thermoelectric substances with CuTe4-based layered structure. J. Mater. Chem. A 4, 2461–2472 (2016).

  • 117.

    Chen, W. et al. knowing thermoelectric properties from excessive-throughput calculations: traits, insights, and comparisons with scan. J. Mater. Chem. C four, 4414–4426 (2016).

  • 118.

    Jain, A., Hautier, G., Ong, S. P. & Persson, k. New alternatives for substances informatics: materials and facts mining options for uncovering hidden relationships. J. Mater. Res. 31, 977994 (2016).

  • 119.

    Pohls, J.-H. et al. metallic phosphides as abilities thermoelectric substances. J. Mater. Chem. C 5, 12441–12456 (2017).

  • one hundred twenty.

    Faghaninia, A. et al. A computational evaluation of the electronic, thermoelectric, and defect properties of bournonite (CuPbSbS3) and linked substitutions. Phys. Chem. Chem. Phys. 19, 6743–6756 (2017).

  • 121.

    Kim, H. M., Shao, L., Zhang, ok. & Pipe, ok. P. Engineered doping of organic semiconductors for enhanced thermoelectric effectivity. Nat. Mater. 12, 719–723 (2013).

  • 122.

    Russ, B., Glaudell, A., urban, J. J., Chabinyc, M. L. & Segalman, R. A. biological thermoelectric materials for energy harvesting and temperature manage. Nat. Rev. Mater. 1, 16050 (2016).

  • 123.

    sun, L. et al. A microporous and naturally nanostructured thermoelectric steel–organic framework with ultralow thermal conductivity. Joule 1, 168–177 (2017).

  • 124.

    Ürge-Vorsatz, D., Cabeza, L. F., Serrano, S., Barreneche, C. & Petrichenko, k. Heating and cooling energy tendencies and drivers in constructions. Renew. preserve. power Rev. forty one, eighty five–ninety eight (2015).

  • one hundred twenty five.

    Waqas, A. & Din, Z. U. part trade cloth (PCM) storage at no cost cooling of constructions — a review. Renew. preserve. power Rev. 18, 607–625 (2013).

  • 126.

    Memon, S. A. part change substances built-in in constructing partitions: a state of the paintings review. Renew. sustain. power Rev. 31, 870–906 (2014).

  • 127.

    Baetens, R., Jelle, B. P. & Gustavsen, A. phase change substances for constructing functions: a state-of-the-art review. energy build. 42, 1361–1368 (2010).

  • 128.

    Koebel, M., Rigacci, A. & Achard, P. Aerogel-based thermal superinsulation: an overview. J. Sol-Gel Sci. Technol. 63, 315–339 (2012).

  • 129.

    Bendahou, D., Bendahou, A., Seantier, B., Grohens, Y. & Kaddami, H. Nano-fibrillated cellulose-zeolites based mostly new hybrid composites aerogels with super thermal insulating homes. Ind. vegetation Prod. sixty five, 374–382 (2015).

  • one hundred thirty.

    Seantier, B., Bendahou, D., Bendahou, A., Grohens, Y. & Kaddami, H. Multi-scale cellulose based mostly new bio-aerogel composites with thermal tremendous-insulating and tunable mechanical residences. Carbohydr. Polym. 138, 335–348 (2016).

  • 131.

    Wicklein, B. et al. Thermally insulating and fire-retardant light-weight anisotropic foams according to nanocellulose and graphene oxide. Nat. Nanotechnol. 10, 277–283 (2015).

  • 132.

    Wang, Y., Runnerstrom, E. L. & Milliron, D. J. Switchable substances for wise home windows. Annu. Rev. Chem. Bio. Eng. 7, 283–304 (2016).

  • 133.

    Runnerstrom, E. L., Llordes, A., Lounis, S. D. & Milliron, D. J. Nanostructured electrochromic wise home windows: traditional substances and NIR-selective plasmonic nanocrystals. Chem. Commun. 50, 10555–10572 (2014).

  • 134.

    Kamalisarvestani, M., Saidur, R., Mekhilef, S. & Javadi, F. efficiency, materials and coating technologies of thermochromic skinny films on smart windows. Renew. sustain. power Rev. 26, 353–364 (2013).

  • a hundred thirty five.

    Baetens, R., Jelle, B. P. & Gustavsen, A. homes, necessities and chances of wise home windows for dynamic daylight and photo voltaic energy handle in structures: a state-of-the-paintings overview. Sol. energy Mater. Sol. Cells ninety four, 87–one hundred and five (2010).

  • 136.

    DeForest, N. et al. u.s. power and CO2 discounts skills from deployment of near-infrared electrochromic window glazings. build. Environ. 89, 107–117 (2015).

  • 137.

    Monk, P. M. S. The Viologens: Physicochemical residences, Synthesis and functions of the Salts of four,four´-Bipyridine. (Wiley, Weinheim, 1999).

  • 138.

    Jasinski, R. J. n-Heptylviologen radical cation films on clear oxide electrodes. J. Electrochem. Soc. 125, 1619–1623 (1978).

  • 139.

    Sammells, A. F. & Pujare, N. U. Electrochromic consequences on heptylviologen incorporated within a high-quality polymer electrolyte phone. J. Electrochem. Soc. 133, 1270–1271 (1986).

  • 140.

    Akahoshi, H., Toshima, S. & Itaya, okay. Electrochemical and spectroelectrochemical houses of polyviologen complicated modified electrodes. J. Phys. Chem. eighty five, 818–822 (1981).

  • 141.

    Beaujuge, P. M. & Reynolds, J. R. color control in π-conjugated organic polymers for use in electrochromic instruments. Chem. Rev. 110, 268–320 (2010).

  • 142.

    Ribeiro, A. S. & Mortimer, R. J. Conjugated conducting polymers with electrochromic and fluorescent houses. Electrochemistry 13, 21–49 (2016).

  • 143.

    Kline, W. M., Lorenzini, R. G. & Sotzing, G. A. A assessment of biological electrochromic fabric instruments. colour. Technol. a hundred thirty, seventy three–eighty (2014).

  • a hundred and forty four.

    Monk, P. M. S., Mortimer, R. J. & Rosseinsky, D. R. Electrochromism: Fundamentals and purposes (Wiley, Weinheim, 1995).

  • 145.

    Mortimer, R. J. Electrochromic materials. Ann. Rev. Mater. Res. 41, 241–268 (2011).

  • 146.

    Xie, Y.-X., Zhao, W.-N., Li, G.-C., Liu, P.-F. & Han, L. A naphthalenediimide-based mostly metallic–biological framework and thin film exhibiting photochromic and electrochromic homes. Inorg. Chem. 55, 549–551 (2016).

  • 147.

    Wade, C. R., Li, M. & Dinca, M. Facile deposition of multicolored electrochromic metallic–biological framework thin films. Angew. Chem. Int. Ed. fifty two, 13377–13381 (2013).

  • 148.

    AlKaabi, okay., Wade, C. R. & Dincă, M. clear-to-dark electrochromic habits in naphthalene-diimide-based mostly mesoporous MOF-seventy four analogs. Chem 1, 264–272 (2016).

  • 149.

    Mjejri, I., Doherty, C. M., Rubio-Martinez, M., Drisko, G. L. & Rougier, A. Double-sided electrochromic equipment in line with metal–organic frameworks. ACS Appl. Mater. Interfaces 9, 39930–39934 (2017).

  • one hundred fifty.

    Mehlana, G. & Bourne, S. A. Unravelling chromism in metal–organic frameworks. CrystEngComm 19, 4238–4259 (2017).

  • 151.

    Gomez-Gualdron, D. A. et al. Computational design of steel–biological frameworks in keeping with good zirconium constructing instruments for storage and delivery of methane. Chem. Mater. 26, 5632–5639 (2014).

  • 152.

    Chung, Y. G. et al. In silico discovery of steel–organic frameworks for precombustion CO2 trap the use of a genetic algorithm. Sci. Adv. 2, e1600909 (2016).

  • 153.

    Borboudakis, G. et al. Chemically intuited, big-scale screening of MOFs by laptop learning recommendations. Comput. Mater. 3, forty (2017).

  • 154.

    Wilmer, C. E. et al. gigantic-scale screening of hypothetical steel–biological frameworks. Nat. Chem. 4, 83–89 (2011).

  • one hundred fifty five.

    Pardakhti, M., Moharreri, E., Wanik, D., Suib, S. L. & Srivastava, R. computing device studying the usage of combined structural and chemical descriptors for prediction of methane adsorption efficiency of metallic biological frameworks (MOFs). ACS Comb. Sci. 19, 640–645 (2017).

  • 156.

    Thackeray, M. M., Wolverton, C. & Isaacs, E. D. electricity storage for transportation approaching the bounds of, and going beyond, lithium-ion batteries. energy Environ. Sci. 5, 7854–7863 (2012).

  • 157.

    Winsberg, J., Hagemann, T., Janoschka, T., Hager, M. D. & Schubert, U. S. Redox-flow batteries: from metals to biological redox-active materials. Angew. Chem. Int. Ed. 56, 686–711 (2017).

  • 158.

    González, A., Goikolea, E., Barrena, J. A. & Mysyk, R. assessment on supercapacitors: technologies and materials. Renew. preserve. power Rev. fifty eight, 1189–1206 (2016).

  • 159.

    Goodenough, J. B. & Park, okay. S. The Li-ion rechargeable battery: a point of view. J. Am. Chem. Soc. 135, 1167–1176 (2013).

  • a hundred and sixty.

    Choi, J. W. & Aurbach, D. Promise and reality of publish-lithium-ion batteries with high energy densities. Nat. Rev. Mater. 1, 16013 (2016).

  • 161.

    Hautier, G., Fischer, C., Ehrlacher, V., Jain, A. & Ceder, G. records mined ionic substitutions for the discovery of latest compounds. Inorg. Chem. 50, 656–663 (2011).

  • 162.

    Hautier, G. et al. Phosphates as lithium-ion battery cathodes: an evaluation in response to high-throughput ab initio calculations. Chem. Mater. 23, 3495–3508 (2011).

  • 163.

    Chen, H. et al. Carbonophosphates: a brand new family unit of cathode materials for Li-ion batteries identified computationally. Chem. Mater. 24, 2009–2016 (2012).

  • 164.

    Ermon, S., Xue, Y., Gomes, C. & Selman, B. discovering guidelines for battery utilization optimization in electric motors. computing device study. 92, 177–194 (2013).

  • 165.

    Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M. & Dietmayer, okay. fitness analysis and closing positive existence prognostics of lithium-ion batteries using facts-driven methods. J. power Sources 239, 680–688 (2013).

  • 166.

    Waag, W., Fleischer, C. & Sauer, D. U. vital evaluate of the methods for monitoring of lithium-ion batteries in electric powered and hybrid motors. J. vigor Sources 258, 321–339 (2014).

  • 167.

    Chaouachi, A., Kamel, R. M., Andoulsi, R. & Nagasaka, k. Multiobjective intelligent energy administration for a microgrid. IEEE Trans. Ind. Electron. 60, 1688–1699 (2013).

  • 168.

    Huskinson, B. et al. A steel-free biological–inorganic aqueous stream battery. Nature 505, 195–198 (2014).

  • 169.

    Lin, okay. et al. Alkaline quinone movement battery. Science 349, 1529–1532 (2015).

  • one hundred seventy.

    Lin, ok. et al. A redox-flow battery with an alloxazine-based biological electrolyte. Nat. energy 1, 16102 (2016).

  • 171.

    Liu, T., Wei, X., Nie, Z., Sprenkle, V. & Wang, W. a complete organic aqueous redox move battery using a in your price range and sustainable methyl viologen anolyte and four-HO-TEMPO catholyte. Adv. power Mater. 6, 1501449 (2016).

  • 172.

    Hu, B., DeBruler, C., Rhodes, Z. & Liu, T. L. lengthy-biking aqueous biological redox circulate battery (AORFB) toward sustainable and protected energy storage. J. Am. Chem. Soc. 139, 1207–1214 (2017).

  • 173.

    Beh, E. S. et al. A neutral pH aqueous organic–organometallic redox circulation battery with extremely excessive capability retention. ACS energy Lett. 2, 639–644 (2017).

  • 174.

    Pyzer-Knapp, E. O., Suh, C., Gómez-Bombarelli, R., Aguilera-Iparraguirre, J. & Aspuru-Guzik, A. what's excessive-throughput digital screening? A viewpoint from biological substances discovery. Annu. Rev. Mater. Res. forty five, 195–216 (2015).

  • a hundred seventy five.

    Jain, A., Shin, Y. & Persson, okay. A. Computational predictions of energy substances using density practical thought. Nat. Rev. Mater. 1, 15004 (2016).

  • 176.

    Hachmann, J. et al. The Harvard clean energy task: tremendous-scale computational screening and design of biological photovoltaics on the realm group grid. J. Phys. Chem. Lett. 2, 2241–2251 (2011).

  • 177.

    Hachmann, J. et al. Lead candidates for high-efficiency organic photovoltaics from high-throughput quantum chemistry — the Harvard clean energy task. energy Environ. Sci. 7, 698–704 (2014).

  • 178.

    Er, S., Suh, C., Marshak, M. P. & Aspuru-Guzik, A. Computational design of molecules for an all-quinone redox circulation battery. Chem. Sci. 6, 885–893 (2015).

  • 179.

    Gómez-Bombarelli, R. et al. Design of productive molecular biological light-emitting diodes by means of a high-throughput digital screening and experimental approach. Nat. Mater. 15, 1120–1127 (2016).

  • one hundred eighty.

    Sokolov, A. N. et al. From computational discovery to experimental characterization of a excessive gap mobility biological crystal. Nat. Commun. 2, 437 (2011).

  • 181.

    Raccuglia, P. et al. desktop-studying-assisted materials discovery the usage of failed experiments. Nature 533, seventy three–seventy six (2016).

  • 182.

    Jain, A. et al. The materials project: a materials genome method to accelerating materials innovation. APL Mater. 1, 011002 (2013).

  • 183.

    Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N. & Kozinsky, B. AiiDA: computerized interactive infrastructure and database for computational science. Comput. Mater. Sci. 111, 218–230 (2016).

  • 184.

    Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. materials design and discovery with excessive-throughput density useful theory: the open quantum materials database (OQMD). JOM 65, 1501–1509 (2013).

  • 185.

    Curtarolo, S. et al. The excessive-throughput dual carriageway to computational substances design. Nat. Mater. 12, 191–201 (2013).

  • 186.

    Curtarolo, S. et al. AFLOWLIB. ORG: a disbursed substances properties repository from excessive-throughput ab initio calculations. Comput. Mater. Sci. fifty eight, 227–235 (2012).

  • 187.

    Hautier, G. et al. Novel combined polyanions lithium-ion battery cathode substances expected by using excessive-throughput ab initio computations. J. Mater. Chem. 21, 17147–17153 (2011).

  • 188.

    Kirklin, S., Chan, M. okay. Y., Trahey, L., Thackeray, M. M. & Wolverton, C. excessive-throughput screening of excessive-means electrodes for hybrid Li-ion–Li–O2 cells. Phys. Chem. Chem. Phys. sixteen, 22073–22082 (2014).

  • 189.

    Qu, X. et al. The Electrolyte Genome undertaking: a big records method in battery materials discovery. Comput. Mater. Sci. 103, fifty six–67 (2015).

  • a hundred ninety.

    Aykol, M. et al. high-throughput computational design of cathode coatings for Li-ion batteries. Nat. Commun. 7, 13779 (2016).

  • 191.

    Toher, C. et al. excessive-throughput computational screening of thermal conductivity, Debye temperature, and Gruneisen parameter the use of a quasiharmonic Debye model. Phys. Rev. B ninety, 174107 (2014).

  • 192.

    Wu, Y., Lazic, P., Hautier, G., Persson, k. & Ceder, G. First principles high throughput screening of oxynitrides for water-splitting photocatalysts. power Environ. Sci. 6, 157–168 (2013).

  • 193.

    Khatami, S. N. & Aksamija, Z. Lattice thermal conductivity of the binary and ternary group-IV alloys Si-Sn, Ge-Sn, and Si-Ge-Sn. Phys. Rev. Appl 6, 014015 (2016).

  • 194.

    Compton, W. D. & Schulman, J. H. colour facilities in Solids 2 (Pergamon, Oxford, 1962).

  • 195.

    Ding, H. et al. Computational strategy for epitaxial polymorph stabilization via substrate option. ACS Appl. Mater. Interfaces 8, 13086–13093 (2016).

  • 196.

    Dunstan, M. T. et al. colossal scale computational screening and experimental discovery of novel materials for extreme temperature CO2 catch. energy Environ. Sci. 9, 1346–1360 (2016).

  • 197.

    Zhu, H. et al. Computational and experimental investigation of TmAgTe2 and XYZ 2 compounds, a brand new group of thermoelectric substances identified by first-principles excessive-throughput screening. J. Mater. Chem. C 3, 10554–10565 (2015).

  • 198.

    Pyzer-Knapp, E. O., Li, okay. & Aspuru-Guzik, A. learning from the Harvard clear power mission: the use of neural networks to accelerate substances discovery. Adv. Func. Mater. 25, 6495–6502 (2015).

  • 199.

    Ghiringhelli, L. M., Vybiral, J., Levchenko, S. V., Draxl, C. & Scheffler, M. big information of materials science: critical position of the descriptor. Phys. Rev. Lett. 114, 105503 (2015).

  • 200.

    Segler, M. H. S., Kogej, T., Tyrchan, C. & Waller, M. P. producing concentrated molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci. four, a hundred and twenty–131 (2018).

  • 201.

    Ikebata, H., Hongo, okay., Isomura, T., Maezono, R. & Yoshida, R. Bayesian molecular design with a chemical language mannequin. J. Comput. Aided Mol. Des. 31, 379–391 (2017).

  • 202.

    Kadurin, A. et al. The cornucopia of meaningful leads: making use of deep adversarial autoencoders for brand new molecule building in oncology. Oncotarget eight, 10883–10890 (2017).

  • 203.

    Podlewska, S., Czarnecki, W. M., Kafel, R. & Bojarski, A. J. creating the new from the ancient: combinatorial libraries technology with computer-studying-primarily based compound structure optimization. J. Chem. Inf. model. fifty seven, 133–147 (2017).

  • 204.

    Tibbetts, okay. M., Feng, X.-J. & Rabitz, H. Exploring experimental fitness landscapes for chemical synthesis and property optimization. Phys. Chem. Chem. Phys. 19, 4266–4287 (2017).

  • 205.

    Moore, ok. W. et al. generic features of chemical synthesis and property optimization. Chem. Sci. 2, 417–424 (2011).

  • 206.

    Moore, okay. W. et al. Why is chemical synthesis and property optimization simpler than anticipated? Phys. Chem. Chem. Phys. 13, 10048–10070 (2011).

  • 207.

    Ping Ong, S., Wang, L., Kang, B. & Ceder, G. Li–Fe–P–O2 phase diagram from first principles calculations. Chem. Mater. 20, 1798–1807 (2008).

  • 208.

    Langer, J. S. fashions of sample formation in first-order section transitions. Dir. Condens. Matt. Phys. 1, one hundred sixty five–186 (1986).

  • 209.

    Lee, D. D., Choy, J. H. & Lee, J. okay. computer era of binary and ternary phase diagrams via a convex hull system. J. phase Equilib. 13, 365–372 (1992).

  • 210.

    Pourbaix, M. Atlas of Electrochemical Equilibria in Aqueous solutions 1 (Pergamon, Oxford, 1966).

  • 211.

    Dannatt, C. W. & Ellingham, H. J. T. Roasting and discount approaches. Roasting and reduction strategies-a commonplace survey. talk about. Faraday Soc four, 126–139 (1948).

  • 212.

    Spencer, P. a short heritage of CALPHAD. Calphad 32, 1–8 (2008).

  • 213.

    Phillips, R. Crystals, Defects and Microstructures: Modeling across Scales (Cambridge Univ. Press, Cambridge, 2001).

  • 214.

    Goyal, A., Gorai, P., Peng, H., Lany, S. & Stevanovic, V. A computational framework for automation of point defect calculations. Preprint at arXiv, 1611.00825 (2016).

  • 215.

    Gomberg, J. A., Medford, A. J. & Kalidindi, S. R. Extracting talents from molecular mechanics simulations of grain boundaries the usage of desktop researching. Acta Mater. 133, one hundred–108 (2017).

  • 216.

    El-Awady, J. A. Unravelling the physics of measurement-elegant dislocation-mediated plasticity. Nat. Commun. 6, 5926 (2015).

  • 217.

    Wu, H., Mayeshiba, T. & Morgan, D. Dataset for high-throughput ab-initio dilute solute diffusion database. Globus (2016).

  • 218.

    Toher, C. et al. Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly monitor thermomechanical homes of solids. Phys. Rev. Mater. 1, 015401 (2017).

  • 219.

    de Jong, M. et al. Charting the finished elastic residences of inorganic crystalline compounds. Sci. records 2, 150009 (2015).

  • 220.

    sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. 2, e1600225 (2016).

  • 221.

    Bartók, A. P. et al. desktop researching unifies the modeling of materials and molecules. Sci. Adv. 3, e1701816 (2017).

  • 222.

    Segler, M. H. S., Preuss, M. & Waller, M. P. getting to know to devise chemical syntheses. Preprint at arXiv, 1708.04202 (2017).

  • 223.

    Corey, E. J. & Jorgensen, W. L. computing device-assisted synthetic analysis. synthetic options in keeping with appendages and the use of reconnective transforms. J. Am. Chem. Soc. ninety eight, 189–203 (1976).

  • 224.

    Corey, E. J. & Wipke, W. T. desktop-assisted design of complicated biological syntheses. Science 166, 178–192 (1969).

  • 225.

    Pensak, D. A. & Corey, E. J. in computing device-Assisted biological Synthesis (eds Wipke, W. T. & Howe, W. J.) 1–32 (American Chemical Society, Washington, DC, 1977).

  • 226.

    Wipke, W. T. & Howe, W. J. laptop-Assisted organic Synthesis (American Chemical Society, Washington, DC, 1977).

  • 227.

    Jorgensen, W. L. et al. CAMEO: a program for the logical prediction of the items of organic reactions. Pure Appl. Chem. 62, 1921–1932 (1990).

  • 228.

    Gasteiger, J. & Jochum, C. EROS a pc program for producing sequences of reactions. organic Compunds 74, 93–126 (1978).

  • 229.

    Satoh, H. & Funatsu, ok. SOPHIA, a knowledge base-guided reaction prediction system — utilization of an information base derived from a reaction database. J. Chem. Inf. Comp. Sci. 35, 34–44 (1995).

  • 230.

    Gelernter, H. L. et al. Empirical explorations of SYNCHEM. Science 197, 1041–1049 (1977).

  • 231.

    Pence, H. E. & Williams, A. ChemSpider: an online chemical tips resource. J. Chem. Ed. 87, 1123–1124 (2010).

  • 232.

    Akhondi, S. A. et al. Annotated chemical patent corpus: a gold usual for textual content mining. PLOS One 9, e107477 (2014).

  • 233.

    Bøgevig, A. et al. Route design in the twenty first century: the ICSYNTH software device as an idea generator for synthesis prediction. Org. system Res. Dev. 19, 357–368 (2015).

  • 234.

    Szymkuć, S. et al. laptop-assisted artificial planning: the end of the beginning. Angew. Chem. Int. Ed. fifty five, 5904–5937 (2016).

  • 235.

    Bergeler, M., Simm, G. N., Proppe, J. & Reiher, M. Heuristics-guided exploration of response mechanisms. J. Chem. idea Comput. 11, 5712–5722 (2015).

  • 236.

    Kayala, M. A. & Baldi, P. ReactionPredictor: prediction of advanced chemical reactions at the mechanistic stage using computer getting to know. J. Chem. Inf. mannequin. fifty two, 2526–2540 (2012).

  • 237.

    Wei, J. N., Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of biological chemistry reactions. ACS Cent. Sci. 2, 725–732 (2016).

  • 238.

    Segler, M. H. S. & Waller, M. P. Neural-symbolic desktop learning for retrosynthesis and response prediction. Chem. Eur. J. 23, 5966–5971 (2017).

  • 239.

    Coley, C. W., Barzilay, R., Jaakkola, T. S., eco-friendly, W. H. & Jensen, k. F. Prediction of biological response outcomes the use of desktop discovering. ACS Cent. Sci. three, 434–443 (2017).

  • 240.

    Duvenaud, D. ok. et al. in Advances in Neural suggestions Processing methods (eds Cortes, C., Lawrence,N. D., Lee, D. D., Sugiyama, M. & Garnett, R.) 2224–2232 (Curran friends, 2015).

  • 241.

    Peplow, M. organic synthesis: the robo-chemist. Nature 512, 20–22 (2014).

  • 242.

    Nicolaou, C. A., Watson, I. A., Hu, H. & Wang, J. The Proximal Lilly assortment: mapping, exploring and exploiting possible chemical space. J. Chem. Inf. model. 56, 1253–1266 (2016).

  • 243.

    Godfrey, A. G., Masquelin, T. & Hemmerle, H. A faraway-controlled adaptive Medchem Lab: an imaginitive approach to permit drug discovery in the twenty first century. Drug Discov. today 18, 795–802 (2013).

  • 244.

    Nicolaou, ok. C., Hanko, R. & Hartwig, W. in instruction manual of Combinatorial Chemistry (eds Nicolaou, ok. C., Hanko, R. & Hartwig, W.) 1–9 (Wiley-VCH, Weinheim, 2005).

  • 245.

    Shevlin, M. practical high-throughput experimentation for chemists. ACS Med. Chem. Lett. eight, 601–607 (2017).

  • 246.

    Weber, A., von Roedern, E. & Stilz, H. U. SynCar: an method to automatic synthesis. J. Comb. Chem. 7, 178–184 (2005).

  • 247.

    Prabhu, G. R. D. & city, P. L. The first light of unmanned analytical laboratories. tendencies Anal. Chem. 88, forty one–fifty two (2017).

  • 248.

    Ley, S. V., Fitzpatrick, D. E., Myers, R. M., Battilocchio, C. & Ingham, R. J. desktop-assisted biological synthesis. Angew. Chem. Int. Ed. 54, 10122–10136 (2015).

  • 249.

    Pastre, J. C., Browne, D. L. & Ley, S. V. movement chemistry syntheses of herbal items. Chem. Soc. Rev. 42, 8849–8869 (2013).

  • 250.

    Adamo, A. et al. On-demand continuous-movement creation of prescription drugs in a compact, reconfigurable system. Science 352, sixty one–67 (2016).

  • 251.

    Rasheed, M. & Wirth, T. intelligent microflow: construction of self-optimizing reaction techniques. Angew. Chem. Int. Ed. 50, 357–358 (2011).

  • 252.

    Buitrago Santanilla, A. et al. Nanomole-scale excessive-throughput chemistry for the synthesis of complex molecules. Science 347, 49–53 (2015).

  • 253.

    Nelson, J. D. in functional artificial biological Chemistry (ed. Caron, S.) 1–71 (John Wiley & Sons, Hoboken, 2011).

  • 254.

    Vaidyanathan, R. & Wager, C. B. in practical artificial organic Chemistry (ed. Caron, S.) seventy three–a hundred sixty five (John Wiley & Sons, Hoboken, 2011).

  • 255.

    Caron, S. et al. in functional artificial organic Chemistry (ed. Caron, S.) 279–340 (John Wiley & Sons, Hoboken, 2011).

  • 256.

    Ripin, D. H. B. in functional synthetic organic Chemistry (ed. Caron, S.) 341–381; 493–556 (John Wiley & Sons, Hoboken, 2011).

  • 257.

    Pouliot, J.-R., Grenier, F., Blaskovits, J. T., Beauprè, S. & Leclerc, M. Direct (hetero)arylation polymerization: simplicity for conjugated polymer synthesis. Chem. Rev. 116, 14225–14274 (2016).

  • 258.

    Woerly, E. M., Roy, J. & Burke, M. D. Synthesis of most polyene herbal product motifs using simply 12 building blocks and one coupling response. Nat. Chem. 6, 484–491 (2014).

  • 259.

    carrier, R. F. The synthesis desktop. Science 347, 1190–1193 (2015).

  • 260.

    Li, J. et al. Synthesis of numerous types of organic small molecules using one computerized system. Science 347, 1221–1226 (2015).

  • 261.

    Maiwald, M., Fischer, H. H., Kim, Y.-okay., Albert, ok. & Hasse, H. Quantitative excessive-decision online NMR spectroscopy in reaction and procedure monitoring. J. Magn. Reson. 166, one hundred thirty five–146 (2004).

  • 262.

    Baranczak, A. et al. built-in platform for expedited synthesis–purification–testing of small molecule libraries. ACS Med. Chem. Lett. eight, 461–465 (2017).

  • 263.

    green, M. L. et al. pleasing the promise of the materials genome initiative with excessive- throughput experimental methodologies. Appl. Phys. Rev. four, 011105 (2017).

  • 264.

    Xiang, X. D. et al. A combinatorial strategy to substances discovery. Science 268, 1738–1740 (1995).

  • 265.

    Tsui, F. & Ryan, P. Combinatorial molecular beam epitaxy synthesis and char- acterization of magnetic alloys. Appl. Surf. Sci. 189, 333–338 (2002).

  • 266.

    Wang, Q., Itaka, okay., Minami, H., Kawaji, H. & Koinuma, H. Combinatorial pulsed laser deposition and thermoelectricity of (La1−xCa x )VO3 composition-unfold movies. Sci. Technol. Adv. Mater. 5, 543–547 (2004).

  • 267.

    Chang, ok.-S., Aronova, M. & Takeuchi, I. Combinatorial pulsed laser deposition the use of a compact excessive-right through skinny-movie deposition flange. Appl. Surf. Sci. 223, 224–228 (2004).

  • 268.

    Takeuchi, I. in Pulsed Laser Deposition of thin films (ed. Eason, R.) 161–176 (John Wiley & Sons, Hoboken, 2006).

  • 269.

    Kim, D. H. et al. Combinatorial pulsed laser deposition of Fe, Cr, Mn, and Ni-substituted SrTiO3 movies on Si substrates. ACS Comb. Sci. 14, 179–190 (2012).

  • 270.

    Havelia, S. et al. Combinatorial substrate epitaxy: a new strategy to boom of advanced metastable compounds. CrystEngComm 15, 5434–5441 (2013).

  • 271.

    solar, X. Y. et al. Combinatorial pulsed laser deposition of magnetic and magneto-optical Sr(Ga x Ti y Fe0.34−0.40)O3−δ perovskite films. ACS Comb. Sci. sixteen, 640–646 (2014).

  • 272.

    Kadhim, A. et al. development of combinatorial pulsed laser deposition for expedited device optimization in CdTe/CdS thin-film solar cells. Int. J. decide. 2016, 1696848 (2016).

  • 273.

    Keifer, P. A. excessive-resolution NMR strategies for strong-section synthesis and combinatorial chemistry. Drug Discov. these days 2, 468–478 (1997).

  • 274.

    bog down, B. C. et al. high-throughput 1H NMR and HPLC characterization of a ninety six-member substituted methylene malonamic acid library. J. Comb. Chem. 1, a hundred and forty–150 (1999).

  • 275.

    Carter, C. F. et al. ReactIR circulate mobilephone: a new analytical tool for continuous circulation chemical processing. Org. manner Res. Dev. 14, 393–404 (2010).

  • 276.

    Huang, H., Yu, H., Xu, H. & Ying, Y. near infrared spectroscopy for on/in-line monitoring of exceptional in meals and beverages: a overview. J. meals. Eng. 87, 303–313 (2008).

  • 277.

    Otani, M. et al. A high-throughput thermoelectric energy-element screening tool for swift construction of thermoelectric property diagrams. Appl. Phys. Lett. 91, 132102 (2007).

  • 278.

    Kuo, T.-C., Malvadkar, N. A., Drumright, R., Cesaretti, R. & Bishop, M. T. excessive- throughput industrial coatings analysis at the Dow Chemical business. ACS Comb. Sci 18, 507–526 (2016).

  • 279.

    Hepp, J., Machui, F., Egelhaaf, H.-J., Brabec, C. J. & Vetter, A. Automatized analysis of IR-images of photovoltaic modules and its use for nice control of solar cells. power Sci. Eng. 4, 363–371 (2016).

  • 280.

    Alstrup, J., Jørgensen, M., Medford, A. J. & Krebs, F. C. ultra fast and parsimonious materials screening for polymer solar cells the use of differentially pumped slot-die coating. ACS Appl. Mater. Interfaces 2, 2819–2827 (2010).

  • 281.

    Guldal, N. S. et al. actual-time assessment of skinny film drying kinetics the usage of an superior, multi-probe optical setup. J. Mater. Chem. C 4, 2178–2186 (2016).

  • 282.

    Dragone, V., Sans, V., Henson, A. B., Granda, J. M. & Cronin, L. An self reliant biological reaction search engine for chemical reactivity. Nat. Commun. 8, 15733 (2017).

  • 283.

    Kitson, P. J. et al. Digitization of multistep biological synthesis in reactionware for on-demand prescription drugs. Science 359, 314–319 (2018).

  • 284.

    Gutierrez, J. P. M., Hinkley, T., Taylor, J. W., Yanev, okay. & Cronin, L. Evolution of oil droplets on a chemorobtic platform. Nat. Commun. 5, 5571 (2014).

  • 285.

    Krein, M., Huang, T. W., Morkowchuk, L., Agrafiotis, D. k. & Breneman, C. M. in Statistical Modelling of Molecular Descriptors in QSAR/QSPR (eds Dehmer, M., Varmuza, ok., Bonchev, D. & Emmert-Streib, F.) 33–64 (Wiley-Blackwell, Weinheim, 2012).

  • 286.

    Seffers, G. I. Scientists opt for AI for lab accomplice. AFCEA (2017).

  • 287.

    Kaur, N. & Sood, S. okay. An power-productive structure for the cyber web of things (IoT). IEEE Syst. J. 11, 796–805 (2017).

  • 288.

    Jacoby, M. The way forward for low-cost solar cells. Chem. Eng. information 94, 30–35 (2016).

  • 289.

    Snyder, G. J. & Toberer, E. S. complicated thermoelectric substances. Nat. Mater. 7, a hundred and five–114 (2008).

  • 290.

    Korgel, B. A. substances science: composite for smarter windows. Nature 500, 278–279 (2013).

  • 291.

    Mathews, C. Battery storage: vigour of respectable can circulation in SA. financial Mail (2017).

  • 292.

    Zhang, C. et al. Thienobenzene-fused perylene bisimide as a non-fullerene acceptor for organic solar cells with a high open-circuit voltage and power conversion efficiency. Mater. Chem. entrance. 1, 749–756 (2017).

  • 293.

    Yan, Y. G., Martin, J., Wong-Ng, W., green, M. & Tang, X. F. A temperature stylish screening tool for top throughput thermoelectric characterization of combinatorial movies. Rev. Sci. Instrum. eighty four, 115110 (2013).

  • | itcertbibles it certification practice materials. the most professional and accurate real exam q&as. | itcertbibles real it exam questions and answers: vmware, ibm, hp, oracle, citrix, cisco, microsoft, comptia and so on. | exam, dump, dumps, real, demo | pass4sure 000-591 dumps at pass4surenow | pass it exams easily | | | itexamcup - pass all it certification exams easily with their real exam practice. latest update and experts revised. | first-hand it exam questions and answers help you pass the exam in first time. try itexamcup free demo of cisco, comptia, hp, ibm, microsoft exams and so on. | exam, practice, certification, test, dumps, provider, cert, material | exam dumps | free amateur gallery - exam dumps | gallery, braindumps, teens, babes, dumps, amateurs, amateur, image | gbxemu | #1 source for gameboy emulators | .nds and .3ds files dot nds in the most popular extension for nintendo ds rom files. other alternatives include .bin for binary 000-591 dumps of the rom | emulator, emulators | top pmi exam | pmi questions answers practice test dumps | |

    RSS Killexams 000-591 dumps


    Fox News

    Google News

    Article 1 | Article 2 | Article 3 | Article 4 | Article 5 | Article 6 | Article 7 | Article 8 | Article 9 | Article 10 |
    Back to Exam List