Abstract

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.

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Awesome Pipeline: https://github.com/pditommaso/awesome-pipelineChemPlanner: http://www.chemplanner.com/Clean Energy Materials Innovation Challenge: http://mission-innovation.net/our- work/innovation-challenges/clean-energy-materials-challenge/Climeworks: http://www.climeworks.com/Dial-a-Molecule: http://generic.wordpress.soton.ac.uk/dial-a-molecule/InfoChem: http://www.infochem.de/products/databases/spresi.shtmlInorganic Crystal Structure Database: http://www2.fiz-karlsruhe.de/icsd_home.htmlNIST high-throughput screening tool: https://www.nist.gov/laboratories/tools-instruments/high-throughput-combinatorial-screening-tool-characterization-thinRDKit: Open-source cheminformatics: http://www.rdkit.org Reaxys: https://www.reaxys.com/UniEnergy Technologies: http://www.uetechnologies.com/

References

  1. 1.

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

  2. 2.

    She, X., Huang, A. Q. & Burgos, R. Review of solid-state transformer technologies and their application in power distribution systems. IEEE J. Emerg. Sel. Top. Power Electron. 1, 186–198 (2013).

  3. 3.

    Mahlia, T. M. I., Saktisahdan, T. J., Jannifar, A., Hasan, M. H. & Matseelar, H. S. C. A review of available methods and development on energy storage; technology update. Renew. Sustain. Energy Rev. 33, 532–545 (2014).

  4. 4.

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

  5. 5.

    Ferreira, A. D. B., Nóvoa, P. R. & Marques, A. T. Multifunctional material systems: a state-of-the-art review. Compos. Struct. 151, 3–35 (2016).

  6. 6.

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

  7. 7.

    Maine, E. & Garnsey, E. Commercializing generic technology: the case of advanced materials ventures. Res. Policy 35, 375–393 (2006).

  8. 8.

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

  9. 9.

    Sabatier, M. & Chollet, B. Is there a first mover advantage in science? Pioneering behavior and scientific production in nanotechnology. Res. Policy 46, 522–533 (2017).

  10. 10.

    Jackson, R. B. in New U.S. Leadership, Next Steps on Climate Change (ed. Hayes, D. J.) 129–135 (Stanford Woods Institute for the Environment, Stanford, CA, USA, 2016).

  11. 11.

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

  12. 12.

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

  13. 13.

    [No authors listed.] The first five years of the materials genome initiative: accomplishments and technical highlights. Materials Genome Initiative https://www.mgi.gov/sites/default/files/documents/mgi-accomplishments-at-5-years-august-2016.pdf (2016).

  14. 14.

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

  15. 15.

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

  16. 16.

    Northrop, E., Biru, H., Lima, S., Bouyé, M. & Song, R. Examining the alignment between the intended nationally determined contributions and sustainable development goals. World Resources Institute https://www.wri.org/sites/default/files/WRI_INDCs_v5.pdf (2016).

  17. 17.

    Knight, W. The dark secret at the heart of AI. MIT Technology Review https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/ (2017).

  18. 18.

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

  19. 19.

    Schrage, M. 4 Models for using AI to make decisions. Harvard Business Review https://hbr.org/2017/01/4-models-for-using-ai-to-make-decisions (2017).

  20. 20.

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

  21. 21.

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

  22. 22.

    Borman, S. Combinatorial chemistry. Chem. Eng. News 76, 47–67 (1998).

  23. 23.

    Nikolaev, P. et al. Autonomy in materials research: a case study in carbon nanotube growth. Comput. Mater. 2, 16031 (2016).

  24. 24.

    Wigley, P. B. et al. Fast machine-learning online optimization of ultra-cold-atom experiments. Sci. Rep. 6, 25890 (2016).

  25. 25.

    Xue, D. et al. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 7, 11241 (2016).

  26. 26.

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

  27. 27.

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

  28. 28.

    Shahriari, B., Swersky, K., 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. 29.

    Allen, K. How a Toronto professor’s research revolutionized artificial intelligence. thestar.com https://www.thestar.com/news/world/2015/04/17/how-a-toronto-professors-research-revolutionized-artificial-intelligence.html (2015).

  30. 30.

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

  31. 31.

    Cisco Public. Encrypted traffic analytics. Cisco https://www.cisco.com/c/dam/en/us/solutions/collateral/enterprise-networks/enterprise-network-security/nb-09-encrytd-traf-anlytcs-wp-cte-en.pdf (2018).

  32. 32.

    Basuchoudhary, A., Bang, J. T. & Sen, T. Machine-Learning Techniques in Economics. (Springer, Berlin, 2017).

  33. 33.

    Mullainathan, S. & Spiess, J. Machine learning: an applied econometric approach. J. Econ. Perspect. 31, 87–106 (2017).

  34. 34.

    Rao, A. Digital twins beyond the industrials. PWC http://usblogs.pwc.com/emerging-technology/digital-twins/ (2017).

  35. 35.

    Magoulas, G. D. & Prentza, A. in Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science Vol 2049 (eds Paliouras, G., Karkaletsis, V. & Spyropoulos, C. D.) 300–307 (Springer, Berlin, 2001).

  36. 36.

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

  37. 37.

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

  38. 38.

    Shen, G., Horikawa, T., Majima, K. & Kamitani, Y. Deep image reconstruction from human brain activity. Preprint at bioRxiv, 240317 (2017).

  39. 39.

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

  40. 40.

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

  41. 41.

    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).

  42. 42.

    Matlock, M. K., Dang, N. L. & Swamidass, S. J. Learning a local-variable model of aromatic and conjugated systems. ACS Cent. Sci. 4, 52–62 (2018).

  43. 43.

    Jiménez, J., Škalic, M., Martinez-Rosell, G. & De Fabritiis, G. KDEEP: protein–ligand absolute binding affinity prediction via 3D-convolutional neural networks. J. Chem. Inf. Model. 58, 287–296 (2018).

  44. 44.

    Wang, H. & Yeung, D.-Y. Towards Bayesian deep learning: a framework and some existing methods. IEEE Trans Knowl. Data Eng 28, 3395–3408 (2016).

  45. 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. 46.

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

  47. 47.

    Hansen, K. et al. Assessment and validation of machine learning methods for predicting molecular atomization energies. J. Chem. Theor. Comput. 9, 3404–3419 (2013).

  48. 48.

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

  49. 49.

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

  50. 50.

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

  51. 51.

    Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018).

  52. 52.

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

  53. 53.

    Sánchez-Lengeling, B., Outeiral, C., Guimaraes, G. L. & Aspuru-Guzik, A. Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC). Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv.5309668.v3 (2017).

  54. 54.

    Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A. & Zhavoronkov, A. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharm. 14, 3098–3104 (2017).

  55. 55.

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

  56. 56.

    Duros, V. et al. Human versus robots in the discovery and crystallization of gigantic polyoxometalates. Angew. Chem. Int. Ed. 56, 10815–10820 (2017).

  57. 57.

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

  58. 58.

    King, R. D. et al. The automation of science. Science 324, 85–89 (2009).

  59. 59.

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

  60. 60.

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

  61. 61.

    Muratori, M. et al. Carbon capture and storage across fuels and sectors in energy system transformation pathways. Int. J. Greenhouse Gas Control 57, 34–41 (2017).

  62. 62.

    Tzimas, E. et al. CO2 utilisation today: report 2017. DepositOnce https://doi.org/10.14279/depositonce-5806 (2017).

  63. 63.

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

  64. 64.

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

  65. 65.

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

  66. 66.

    Reymond, H., Vitas, S., Vernuccio, S. & von Rohr, P. R. Reaction process of resin-catalyzed methyl formate hydrolysis in biphasic continuous flow. Ind. Eng. Chem. Res. 56, 1439–1449 (2017).

  67. 67.

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

  68. 68.

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

  69. 69.

    U.S. Energy Information Agency. Manufacturing Energy Consumption Survey (MECS) 2014 (U.S. Energy Information Agency, 2014).

  70. 70.

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

  71. 71.

    Kondratenko, E. V., Mul, G., Baltrusaitis, J., Larrazabal, G. O. & Perez-Ramirez, J. Status and perspectives of CO2 conversion into fuels and chemicals by catalytic, photocatalytic and electrocatalytic processes. Energy Environ. Sci. 6, 3112–3135 (2013).

  72. 72.

    Olah, G. A. Beyond oil and gas: the methanol economy. Angew. Chem. Int. Ed. 44, 2636–2639 (2005).

  73. 73.

    Lal, R. Soil carbon sequestration to mitigate climate change. Geoderma 123, 1–22 (2004).

  74. 74.

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

  75. 75.

    Williamson, P. Emissions reduction: scrutinize CO2 removal methods. Nature 530, 153–155 (2016).

  76. 76.

    Marshall, C. In Switzerland, a giant new machine is sucking carbon directly from the air. Science https://doi.org/10.1126/science.aan6915 (2017).

  77. 77.

    Man, I. C. et al. Universality in oxygen evolution electrocatalysis on oxide surfaces. ChemCatChem 3, 1159–1165 (2011).

  78. 78.

    Montoya, J. H., Tsai, C., Vojvodic, A. & Nørskov, J. K. The challenge of electrochemical ammonia synthesis: a new perspective on the role of nitrogen scaling relations. ChemSusChem 8, 2180–2186 (2015).

  79. 79.

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

  80. 80.

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

  81. 81.

    Montoya, J. H. et al. Materials for solar fuels and chemicals. Nat. Mater. 16, 70–81 (2017).

  82. 82.

    Ma, X., Li, Z., Achenie, L. E. K. & Xin, H. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening. J. Phys. Chem. Lett. 6, 3528–3533 (2015).

  83. 83.

    Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8, 14621 (2017).

  84. 84.

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

  85. 85.

    Lysgaard, S., Landis, D. D., Bligaard, T. & Vegge, T. Genetic algorithm procreation operators for alloy nanoparticle catalysts. Top. Catal. 57, 33–39 (2014).

  86. 86.

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

  87. 87.

    Rosenbrock, C. W., Homer, E. R., Csányi, G. & Hart, G. L. W. Discovering the building blocks of atomic systems using machine learning: application to grain boundaries. Comput. Mater. 3, 29 (2017).

  88. 88.

    Jinnouchi, R. & Asahi, R. Predicting catalytic activity of nanoparticles by a DFT-aided machine-learning algorithm. J. Phys. Chem. Lett. 8, 4279–4283 (2017).

  89. 89.

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

  90. 90.

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

  91. 91.

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

  92. 92.

    Tran, R. et al. Surface energies of elemental crystals. Sci. Data 3, 160080 (2016).

  93. 93.

    Kalidindi, S. R., Medford, A. J. & McDowell, D. L. Vision for data and informatics in the future materials innovation ecosystem. JOM 68, 2126–2137 (2016).

  94. 94.

    Green, M. A. Commercial progress and challenges for photovoltaics. Nat. Energy 1, 15015 (2016).

  95. 95.

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

  96. 96.

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

  97. 97.

    Tan, H. et al. Efficient and stable solution-processed planar perovskite solar cells via contact passivation. Science 355, 722–726 (2017).

  98. 98.

    National Renewable Energy Laboratory. Best research-cell efficiencies. National Renewable Energy Laboratory www.nrel.gov/pv/assets/images/efficiency_chart.jpg (2016).

  99. 99.

    Shin, S. S. et al. Colloidally prepared La-doped BaSnO3 electrodes for efficient, photostable perovskite solar cells. Science 356, 167–171 (2017).

  100. 100.

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

  101. 101.

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

  102. 102.

    Hoth, C. N., Schilinsky, P., Choulis, S. A., Balasubramanian, S. & Brabec, C. J. in Applications of Organic and Printed Electronics (ed. Cantatore, E.) 27–56 (Springer US, Boston, MA, 2013).

  103. 103.

    Al-Ibrahim, M., Roth, H.-K., Zhokhavets, U., Gobsch, G. & Sensfuss, S. Flexible large area polymer solar cells based on poly(3-hexylthiophene)/fullerene. Sol. Energy Mater. Sol. Cells 85, 13–20 (2005).

  104. 104.

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

  105. 105.

    Schubert, M. B. & Werner, J. H. Flexible solar cells for clothing. Mater. Today 9, 42–50 (2006).

  106. 106.

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

  107. 107.

    Henemann, A. BIPV: built-in solar energy. Renew. Energy Focus 9, 14–19 (2008).

  108. 108.

    Azzopardi, B. et al. Economic assessment of solar electricity production from organic-based photovoltaic modules in a domestic environment. Energy Environ. Sci. 4, 3741–3753 (2011).

  109. 109.

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

  110. 110.

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

  111. 111.

    Teichler, A. et al. Combinatorial screening of polymer:fullerene blends for organic solar cells by inkjet printing. Adv. Energy Mater. 1, 105–114 (2011).

  112. 112.

    Chen, S. et al. Exploring the stability of novel wide bandgap perovskites by a robot based high throughput approach. Adv. Energy Mater. 8, 1701543 (2018).

  113. 113.

    Lawrence Livermore National Laboratory. Energy flow charts. LLNL Flow Charts https://flowcharts.llnl.gov/ (2016).

  114. 114.

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

  115. 115.

    Biswas, K. et al. High-performance bulk thermoelectrics with all-scale hierarchical architectures. Nature 489, 414–418 (2012).

  116. 116.

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

  117. 117.

    Chen, W. et al. Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment. J. Mater. Chem. C 4, 4414–4426 (2016).

  118. 118.

    Jain, A., Hautier, G., Ong, S. P. & Persson, K. New opportunities for materials informatics: resources and data mining techniques for uncovering hidden relationships. J. Mater. Res. 31, 977994 (2016).

  119. 119.

    Pohls, J.-H. et al. Metal phosphides as potential thermoelectric materials. J. Mater. Chem. C 5, 12441–12456 (2017).

  120. 120.

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

  121. 121.

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

  122. 122.

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

  123. 123.

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

  124. 124.

    Ürge-Vorsatz, D., Cabeza, L. F., Serrano, S., Barreneche, C. & Petrichenko, K. Heating and cooling energy trends and drivers in buildings. Renew. Sustain. Energy Rev. 41, 85–98 (2015).

  125. 125.

    Waqas, A. & Din, Z. U. Phase change material (PCM) storage for free cooling of buildings — a review. Renew. Sustain. Energy Rev. 18, 607–625 (2013).

  126. 126.

    Memon, S. A. Phase change materials integrated in building walls: a state of the art review. Renew. Sustain. Energy Rev. 31, 870–906 (2014).

  127. 127.

    Baetens, R., Jelle, B. P. & Gustavsen, A. Phase change materials for building applications: a state-of-the-art review. Energy Build. 42, 1361–1368 (2010).

  128. 128.

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

  129. 129.

    Bendahou, D., Bendahou, A., Seantier, B., Grohens, Y. & Kaddami, H. Nano-fibrillated cellulose-zeolites based new hybrid composites aerogels with super thermal insulating properties. Ind. Crops Prod. 65, 374–382 (2015).

  130. 130.

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

  131. 131.

    Wicklein, B. et al. Thermally insulating and fire-retardant lightweight anisotropic foams based on nanocellulose and graphene oxide. Nat. Nanotechnol. 10, 277–283 (2015).

  132. 132.

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

  133. 133.

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

  134. 134.

    Kamalisarvestani, M., Saidur, R., Mekhilef, S. & Javadi, F. Performance, materials and coating technologies of thermochromic thin films on smart windows. Renew. Sustain. Energy Rev. 26, 353–364 (2013).

  135. 135.

    Baetens, R., Jelle, B. P. & Gustavsen, A. Properties, requirements and possibilities of smart windows for dynamic daylight and solar energy control in buildings: a state-of-the-art review. Sol. Energy Mater. Sol. Cells 94, 87–105 (2010).

  136. 136.

    DeForest, N. et al. United States energy and CO2 savings potential from deployment of near-infrared electrochromic window glazings. Build. Environ. 89, 107–117 (2015).

  137. 137.

    Monk, P. M. S. The Viologens: Physicochemical Properties, Synthesis and Applications of the Salts of 4,4´-Bipyridine. (Wiley, Weinheim, 1999).

  138. 138.

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

  139. 139.

    Sammells, A. F. & Pujare, N. U. Electrochromic effects on heptylviologen incorporated within a solid polymer electrolyte cell. J. Electrochem. Soc. 133, 1270–1271 (1986).

  140. 140.

    Akahoshi, H., Toshima, S. & Itaya, K. Electrochemical and spectroelectrochemical properties of polyviologen complex modified electrodes. J. Phys. Chem. 85, 818–822 (1981).

  141. 141.

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

  142. 142.

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

  143. 143.

    Kline, W. M., Lorenzini, R. G. & Sotzing, G. A. A review of organic electrochromic fabric devices. Color. Technol. 130, 73–80 (2014).

  144. 144.

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

  145. 145.

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

  146. 146.

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

  147. 147.

    Wade, C. R., Li, M. & Dinca, M. Facile deposition of multicolored electrochromic metal–organic framework thin films. Angew. Chem. Int. Ed. 52, 13377–13381 (2013).

  148. 148.

    AlKaabi, K., Wade, C. R. & Dincă, M. Transparent-to-dark electrochromic behavior in naphthalene-diimide-based mesoporous MOF-74 analogs. Chem 1, 264–272 (2016).

  149. 149.

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

  150. 150.

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

  151. 151.

    Gomez-Gualdron, D. A. et al. Computational design of metal–organic frameworks based on stable zirconium building units for storage and delivery of methane. Chem. Mater. 26, 5632–5639 (2014).

  152. 152.

    Chung, Y. G. et al. In silico discovery of metal–organic frameworks for precombustion CO2 capture using a genetic algorithm. Sci. Adv. 2, e1600909 (2016).

  153. 153.

    Borboudakis, G. et al. Chemically intuited, large-scale screening of MOFs by machine learning techniques. Comput. Mater. 3, 40 (2017).

  154. 154.

    Wilmer, C. E. et al. Large-scale screening of hypothetical metal–organic frameworks. Nat. Chem. 4, 83–89 (2011).

  155. 155.

    Pardakhti, M., Moharreri, E., Wanik, D., Suib, S. L. & Srivastava, R. Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs). ACS Comb. Sci. 19, 640–645 (2017).

  156. 156.

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

  157. 157.

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

  158. 158.

    González, A., Goikolea, E., Barrena, J. A. & Mysyk, R. Review on supercapacitors: technologies and materials. Renew. Sustain. Energy Rev. 58, 1189–1206 (2016).

  159. 159.

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

  160. 160.

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

  161. 161.

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

  162. 162.

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

  163. 163.

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

  164. 164.

    Ermon, S., Xue, Y., Gomes, C. & Selman, B. Learning policies for battery usage optimization in electric vehicles. Machine Learn. 92, 177–194 (2013).

  165. 165.

    Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M. & Dietmayer, K. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 239, 680–688 (2013).

  166. 166.

    Waag, W., Fleischer, C. & Sauer, D. U. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J. Power Sources 258, 321–339 (2014).

  167. 167.

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

  168. 168.

    Huskinson, B. et al. A metal-free organic–inorganic aqueous flow battery. Nature 505, 195–198 (2014).

  169. 169.

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

  170. 170.

    Lin, K. et al. A redox-flow battery with an alloxazine-based organic electrolyte. Nat. Energy 1, 16102 (2016).

  171. 171.

    Liu, T., Wei, X., Nie, Z., Sprenkle, V. & Wang, W. A total organic aqueous redox flow battery employing a low cost and sustainable methyl viologen anolyte and 4-HO-TEMPO catholyte. Adv. Energy Mater. 6, 1501449 (2016).

  172. 172.

    Hu, B., DeBruler, C., Rhodes, Z. & Liu, T. L. Long-cycling aqueous organic redox flow battery (AORFB) toward sustainable and safe energy storage. J. Am. Chem. Soc. 139, 1207–1214 (2017).

  173. 173.

    Beh, E. S. et al. A neutral pH aqueous organic–organometallic redox flow battery with extremely high capacity retention. ACS Energy Lett. 2, 639–644 (2017).

  174. 174.

    Pyzer-Knapp, E. O., Suh, C., Gómez-Bombarelli, R., Aguilera-Iparraguirre, J. & Aspuru-Guzik, A. What is high-throughput virtual screening? A perspective from organic materials discovery. Annu. Rev. Mater. Res. 45, 195–216 (2015).

  175. 175.

    Jain, A., Shin, Y. & Persson, K. A. Computational predictions of energy materials using density functional theory. Nat. Rev. Mater. 1, 15004 (2016).

  176. 176.

    Hachmann, J. et al. The Harvard Clean Energy Project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2, 2241–2251 (2011).

  177. 177.

    Hachmann, J. et al. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry — the Harvard Clean Energy Project. Energy Environ. Sci. 7, 698–704 (2014).

  178. 178.

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

  179. 179.

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

  180. 180.

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

  181. 181.

    Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).

  182. 182.

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

  183. 183.

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

  184. 184.

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

  185. 185.

    Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).

  186. 186.

    Curtarolo, S. et al. AFLOWLIB. ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227–235 (2012).

  187. 187.

    Hautier, G. et al. Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations. J. Mater. Chem. 21, 17147–17153 (2011).

  188. 188.

    Kirklin, S., Chan, M. K. Y., Trahey, L., Thackeray, M. M. & Wolverton, C. High-throughput screening of high-capacity electrodes for hybrid Li-ion–Li–O2 cells. Phys. Chem. Chem. Phys. 16, 22073–22082 (2014).

  189. 189.

    Qu, X. et al. The Electrolyte Genome Project: a big data approach in battery materials discovery. Comput. Mater. Sci. 103, 56–67 (2015).

  190. 190.

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

  191. 191.

    Toher, C. et al. High-throughput computational screening of thermal conductivity, Debye temperature, and Gruneisen parameter using a quasiharmonic Debye model. Phys. Rev. B 90, 174107 (2014).

  192. 192.

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

  193. 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. 194.

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

  195. 195.

    Ding, H. et al. Computational approach for epitaxial polymorph stabilization through substrate selection. ACS Appl. Mater. Interfaces 8, 13086–13093 (2016).

  196. 196.

    Dunstan, M. T. et al. Large scale computational screening and experimental discovery of novel materials for high temperature CO2 capture. Energy Environ. Sci. 9, 1346–1360 (2016).

  197. 197.

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

  198. 198.

    Pyzer-Knapp, E. O., Li, K. & Aspuru-Guzik, A. Learning from the Harvard Clean Energy Project: the use of neural networks to accelerate materials discovery. Adv. Func. Mater. 25, 6495–6502 (2015).

  199. 199.

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

  200. 200.

    Segler, M. H. S., Kogej, T., Tyrchan, C. & Waller, M. P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci. 4, 120–131 (2018).

  201. 201.

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

  202. 202.

    Kadurin, A. et al. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 8, 10883–10890 (2017).

  203. 203.

    Podlewska, S., Czarnecki, W. M., Kafel, R. & Bojarski, A. J. Creating the new from the old: combinatorial libraries generation with machine-learning-based compound structure optimization. J. Chem. Inf. Model. 57, 133–147 (2017).

  204. 204.

    Tibbetts, K. 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. 205.

    Moore, K. W. et al. Universal characteristics of chemical synthesis and property optimization. Chem. Sci. 2, 417–424 (2011).

  206. 206.

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

  207. 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. 208.

    Langer, J. S. Models of pattern formation in first-order phase transitions. Dir. Condens. Matt. Phys. 1, 165–186 (1986).

  209. 209.

    Lee, D. D., Choy, J. H. & Lee, J. K. Computer generation of binary and ternary phase diagrams via a convex hull method. J. Phase Equilib. 13, 365–372 (1992).

  210. 210.

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

  211. 211.

    Dannatt, C. W. & Ellingham, H. J. T. Roasting and reduction processes. Roasting and reduction processes-a general survey. Discuss. Faraday Soc 4, 126–139 (1948).

  212. 212.

    Spencer, P. A brief history of CALPHAD. Calphad 32, 1–8 (2008).

  213. 213.

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

  214. 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. 215.

    Gomberg, J. A., Medford, A. J. & Kalidindi, S. R. Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning. Acta Mater. 133, 100–108 (2017).

  216. 216.

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

  217. 217.

    Wu, H., Mayeshiba, T. & Morgan, D. Dataset for high-throughput ab-initio dilute solute diffusion database. Globus https://doi.org/10.18126/M2X59R (2016).

  218. 218.

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

  219. 219.

    de Jong, M. et al. Charting the complete elastic properties of inorganic crystalline compounds. Sci. Data 2, 150009 (2015).

  220. 220.

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

  221. 221.

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

  222. 222.

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

  223. 223.

    Corey, E. J. & Jorgensen, W. L. Computer-assisted synthetic analysis. Synthetic strategies based on appendages and the use of reconnective transforms. J. Am. Chem. Soc. 98, 189–203 (1976).

  224. 224.

    Corey, E. J. & Wipke, W. T. Computer-assisted design of complex organic syntheses. Science 166, 178–192 (1969).

  225. 225.

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

  226. 226.

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

  227. 227.

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

  228. 228.

    Gasteiger, J. & Jochum, C. EROS a computer program for generating sequences of reactions. Organic Compunds 74, 93–126 (1978).

  229. 229.

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

  230. 230.

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

  231. 231.

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

  232. 232.

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

  233. 233.

    Bøgevig, A. et al. Route design in the 21st century: the ICSYNTH software tool as an idea generator for synthesis prediction. Org. Process Res. Dev. 19, 357–368 (2015).

  234. 234.

    Szymkuć, S. et al. Computer-assisted synthetic planning: the end of the beginning. Angew. Chem. Int. Ed. 55, 5904–5937 (2016).

  235. 235.

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

  236. 236.

    Kayala, M. A. & Baldi, P. ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning. J. Chem. Inf. Model. 52, 2526–2540 (2012).

  237. 237.

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

  238. 238.

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

  239. 239.

    Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H. & Jensen, K. F. Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci. 3, 434–443 (2017).

  240. 240.

    Duvenaud, D. K. et al. in Advances in Neural Information Processing Systems (eds Cortes, C., Lawrence,N. D., Lee, D. D., Sugiyama, M. & Garnett, R.) 2224–2232 (Curran Associates, 2015).

  241. 241.

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

  242. 242.

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

  243. 243.

    Godfrey, A. G., Masquelin, T. & Hemmerle, H. A remote-controlled adaptive Medchem Lab: an innovative approach to enable drug discovery in the 21st century. Drug Discov. Today 18, 795–802 (2013).

  244. 244.

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

  245. 245.

    Shevlin, M. Practical high-throughput experimentation for chemists. ACS Med. Chem. Lett. 8, 601–607 (2017).

  246. 246.

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

  247. 247.

    Prabhu, G. R. D. & Urban, P. L. The dawn of unmanned analytical laboratories. Trends Anal. Chem. 88, 41–52 (2017).

  248. 248.

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

  249. 249.

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

  250. 250.

    Adamo, A. et al. On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system. Science 352, 61–67 (2016).

  251. 251.

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

  252. 252.

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

  253. 253.

    Nelson, J. D. in Practical Synthetic Organic Chemistry (ed. Caron, S.) 1–71 (John Wiley & Sons, Hoboken, 2011).

  254. 254.

    Vaidyanathan, R. & Wager, C. B. in Practical Synthetic Organic Chemistry (ed. Caron, S.) 73–165 (John Wiley & Sons, Hoboken, 2011).

  255. 255.

    Caron, S. et al. in Practical Synthetic Organic Chemistry (ed. Caron, S.) 279–340 (John Wiley & Sons, Hoboken, 2011).

  256. 256.

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

  257. 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. 258.

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

  259. 259.

    Service, R. F. The synthesis machine. Science 347, 1190–1193 (2015).

  260. 260.

    Li, J. et al. Synthesis of many different types of organic small molecules using one automated process. Science 347, 1221–1226 (2015).

  261. 261.

    Maiwald, M., Fischer, H. H., Kim, Y.-K., Albert, K. & Hasse, H. Quantitative high-resolution on-line NMR spectroscopy in reaction and process monitoring. J. Magn. Reson. 166, 135–146 (2004).

  262. 262.

    Baranczak, A. et al. Integrated platform for expedited synthesis–purification–testing of small molecule libraries. ACS Med. Chem. Lett. 8, 461–465 (2017).

  263. 263.

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

  264. 264.

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

  265. 265.

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

  266. 266.

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

  267. 267.

    Chang, K.-S., Aronova, M. & Takeuchi, I. Combinatorial pulsed laser deposition using a compact high-throughout thin-film deposition flange. Appl. Surf. Sci. 223, 224–228 (2004).

  268. 268.

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

  269. 269.

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

  270. 270.

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

  271. 271.

    Sun, 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. 16, 640–646 (2014).

  272. 272.

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

  273. 273.

    Keifer, P. A. High-resolution NMR techniques for solid-phase synthesis and combinatorial chemistry. Drug Discov. Today 2, 468–478 (1997).

  274. 274.

    Hamper, B. C. et al. High-throughput 1H NMR and HPLC characterization of a 96-member substituted methylene malonamic acid library. J. Comb. Chem. 1, 140–150 (1999).

  275. 275.

    Carter, C. F. et al. ReactIR Flow Cell: a new analytical tool for continuous flow chemical processing. Org. Process Res. Dev. 14, 393–404 (2010).

  276. 276.

    Huang, H., Yu, H., Xu, H. & Ying, Y. Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. J. Food. Eng. 87, 303–313 (2008).

  277. 277.

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

  278. 278.

    Kuo, T.-C., Malvadkar, N. A., Drumright, R., Cesaretti, R. & Bishop, M. T. High- throughput industrial coatings research at The Dow Chemical Company. ACS Comb. Sci 18, 507–526 (2016).

  279. 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 quality control of solar cells. Energy Sci. Eng. 4, 363–371 (2016).

  280. 280.

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

  281. 281.

    Guldal, N. S. et al. Real-time evaluation of thin film drying kinetics using an advanced, multi-probe optical setup. J. Mater. Chem. C 4, 2178–2186 (2016).

  282. 282.

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

  283. 283.

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

  284. 284.

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

  285. 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, K., Bonchev, D. & Emmert-Streib, F.) 33–64 (Wiley-Blackwell, Weinheim, 2012).

  286. 286.

    Seffers, G. I. Scientists pick AI for lab partner. AFCEA https://www.afcea.org/content/scientists-pick-ai-lab-partner (2017).

  287. 287.

    Kaur, N. & Sood, S. K. An energy-efficient architecture for the Internet of Things (IoT). IEEE Syst. J. 11, 796–805 (2017).

  288. 288.

    Jacoby, M. The future of low-cost solar cells. Chem. Eng. News 94, 30–35 (2016).

  289. 289.

    Snyder, G. J. & Toberer, E. S. Complex thermoelectric materials. Nat. Mater. 7, 105–114 (2008).

  290. 290.

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

  291. 291.

    Mathews, C. Battery storage: power of good can flow in SA. Financial Mail https://www.businesslive.co.za/fm/fm-fox/2017-06-29-battery-storage-power-of-good-can-flow-in-sa/ (2017).

  292. 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. Front. 1, 749–756 (2017).

  293. 293.

    Yan, Y. G., Martin, J., Wong-Ng, W., Green, M. & Tang, X. F. A temperature dependent screening tool for high throughput thermoelectric characterization of combinatorial films. Rev. Sci. Instrum. 84, 115110 (2013).

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Acknowledgements

D.P.T. and A.A.-G. were supported by the National Science Foundation (NSF) Science and Technology Center for Integrated Quantum Materials, CIQM (Grant No. NSF-DMR-1231319). L.M.R. and A.A.-G. acknowledge support from Anders Frøseth. S.K.S., C.K. and A.A.-G. were supported by the NSF (Grant No. CHE-1464862). D.S. and A.A.-G. acknowledge the Harvard Climate Solution Fund. J.H.M. and K.P. were supported by the Materials Project Center (Grant No. EDCBEE) through the US Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division (Contract No. DE-AC02 05CH11231). S.D. acknowledges support from the Center for the Next Generation of Materials by Design, an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences (Contract No. DE-AC36-08GO28308). A.A.-G. acknowledges support from the Canadian Institute for Advanced Research (Grant No. BSE-ASPU-162439-CF).

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Affiliations

  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    • Daniel P. Tabor
    • , Loïc M. Roch
    • , Semion K. Saikin
    • , Christoph Kreisbeck
    • , Dennis Sheberla
    •  & Alán Aspuru-Guzik
  2. Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    • Joseph H. Montoya
    • , Shyam Dwaraknath
    •  & Kristin A. Persson
  3. Toyota Research Institute, Los Altos, CA, USA

    • Muratahan Aykol
  4. Secretaría de Energía, México Del Valle, Mexico City, Mexico

    • Carlos Ortiz
  5. Fondo de Sustentabilidad Energética, Mexico City, Mexico

    • Hermann Tribukait
  6. Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico

    • Carlos Amador-Bedolla
  7. Department of Materials Science, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany

    • Christoph J. Brabec
  8. Renewable Energy Division, ZAE Bayern, Erlangen, Germany

    • Christoph J. Brabec
  9. Air Force Research Laboratory, Materials and Manufacturing Directorate, Wright–Patterson Air Force Base, Dayton, OH, USA

    • Benji Maruyama
  10. Department of Materials Science, University of California Berkeley, Berkeley, CA, USA

    • Kristin A. Persson

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Contributions

D.P.T., L.M.R., S.K.S., C.K. and D.S. researched data and wrote the article. All authors contributed to the discussion of content and assisted in editing the manuscript before submission.

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The authors declare no competing interests.

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Correspondence to Alán Aspuru-Guzik.

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DOI

https://doi.org/10.1038/s41578-018-0005-z