The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human–machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities.
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LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: Recent applications and prospects. NPJ Comput. Mater. 3, 54 (2017).
Schmidt, J., Marques, M. R., Botti, S. & Marques, M. A. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput. Mater. 5, 83 (2019).
Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).
Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 361, 360–365 (2018).
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
The Minerals Metals & Materials Society (TMS). Building a Materials Data Infrastructure: Opening New Pathways to Discovery and Innovation in Science and Engineering (TMS, 2017).
Fisher, R. A. The Design of Experiments 9th edn (Macmillan, 1971).
Cao, B. et al. How to optimize materials and devices via design of experiments and machine learning: Demonstration using organic photovoltaics. ACS Nano 12, 7434–7444 (2018).
Morris, M. D. & Mitchell, T. J. Exploratory designs for computational experiments. J. Stat. Plan. Inference 43, 381–402 (1995).
Qian, P. Z. Sliced Latin hypercube designs. J. Am. Stat. Assoc. 107, 393–399 (2012).
Joseph, V. R., Gul, E. & Ba, S. Designing computer experiments with multiple types of factors: The MaxPro approach. J. Qual. Technol. 52, 343–354 (2019).
Zhang, Y., Yoon, H. S., Koh, C. S. & Xie, D. in 2007 International Conference on Electrical Machines and Systems (ICEMS) 1414–1418 (IEEE, 2007).
Joseph, V. R. Space-filling designs for computer experiments: A review. Qual. Eng. 28, 28–35 (2016).
Castillo, A. R. & Kalidindi, S. R. A Bayesian framework for the estimation of the single crystal elastic parameters from spherical indentation stress-strain measurements. Front. Mater. 6, 136 (2019).
Castillo, A. R. & Kalidindi, S. R. Bayesian estimation of single ply anisotropic elastic constants from spherical indentations on multi-laminate polymer-matrix fiber-reinforced composite samples. Meccanica https://doi.org/10.1007/s11012-020-01154-w (2020).
Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning Vol. 2 (MIT Press, 2006).
Forrester, A. I. J., Sóbester, A. & Keane, A. J. Engineering Design via Surrogate Modelling: A Practical Guide (Wiley, 2008).
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 (2015).
Kushner, H. J. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J. Basic Eng. 86, 97–106 (1964).
Russo, D. J. et al. A tutorial on Thompson sampling. Found. Trends Mach. Learn. 11, 1–96 (2018).
Xue, D. et al. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 7, 11241 (2016).
Kim, C., Chandrasekaran, A., Jha, A. & Ramprasad, R. Active-learning and materials design: The example of high glass transition temperature polymers. MRS Commun. 9, 860–866 (2019).
Yuan, R. et al. Accelerated discovery of large electrostrains in BaTiO3-based piezoelectrics using active learning. Adv. Mater. 30, 1702884 (2018).
Wen, C. et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 170, 109–117 (2019).
Xue, D. et al. Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning. Proc. Natl Acad. Sci. USA 113, 13301–13306 (2016).
Lookman, T., Balachandran, P. V., Xue, D., Hogden, J. & Theiler, J. Statistical inference and adaptive design for materials discovery. Curr. Opin. Solid State Mater. Sci. 21, 121–128 (2017).
Lookman, T., Balachandran, P. V., Xue, D. & Yuan, R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. NPJ Comput. Mater. 5, 21 (2019).
Rohr, B. et al. Benchmarking the acceleration of materials discovery by sequential learning. Chem. Sci. 11, 2696–2706 (2020).
Swain, M. C. & Cole, J. M. ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature. J. Chem. Inf. Model. 56, 1894–1904 (2016).
Pennington, J., Socher, R. & Manning, C. D. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1532–1543 (Association for Computational Linguistics, 2014).
Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. Preprint at arXiv https://arxiv.org/abs/1301.3781 (2013).
Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).
Court, C. J. & Cole, J. M. Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction. Sci. Data 5, 180111 (2018).
Jensen, Z. et al. A machine learning approach to zeolite synthesis enabled by automatic literature data extraction. ACS Cent. Sci. 5, 892–899 (2019).
Kim, E. et al. Materials synthesis insights from scientific literature via text extraction and machine learning. Chem. Mater. 29, 9436–9444 (2017).
Kim, E. et al. Inorganic materials synthesis planning with literature-trained neural networks. J. Chem. Inf. Model. 60, 1194–1201 (2020).
He, T. et al. Similarity of precursors in solid-state synthesis as text-mined from scientific literature. Chem. Mater. 32, 7861–7873 (2020).
Writer, B. Lithium-Ion Batteries. A Machine-Generated Summary of Current Research (Springer, 2019).
Wu, P., Carberry, S., Elzer, S. & Chester, D. in International Conference on Theory and Application of Diagrams 220–234 (Springer, 2010).
Savva, M. et al. in Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology 393–402 (ACM, 2011).
Ray Choudhury, S. & Giles, C. L. in Proceedings of the 24th International Conference on World Wide Web 667–672 (ACM, 2015).
Siegel, N., Horvitz, Z., Levin, R., Divvala, S. & Farhadi, A. in European Conference on Computer Vision 664–680 (Springer, 2016).
Seo, M., Hajishirzi, H., Farhadi, A., Etzioni, O. & Malcolm, C. in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing 1466–1476 (Association for Computational Linguistics, 2015).
Sachan, M., Dubey, K. & Xing, E. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing 773–784 (Association for Computational Linguistics, 2017).
Sachan, M. et al. Discourse in multimedia: A case study in extracting geometry knowledge from textbooks. Comput. Linguist. 45, 627–665 (2019).
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. Preprint at arXiv https://arxiv.org/abs/1603.04467 (2015).
Mueller, T., Kusne, A. G. & Ramprasad, R. Machine learning in materials science: Recent progress and emerging applications. Rev. Comput. Chem. 29, 186–273 (2016).
Schleder, G. R., Padilha, A. C., Acosta, C. M., Costa, M. & Fazzio, A. From DFT to machine learning: Recent approaches to materials science–a review. J. Phys. Mater. 2, 032001 (2019).
Mannodi-Kanakkithodi, A. et al. Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond. Mater. Today 21, 785–796 (2018).
Huang, A., Huo, Y., Yang, J. & Li, G. Computational simulation and prediction on electrical conductivity of oxide-based melts by big data mining. Materials 12, 1059 (2019).
Kim, C., Pilania, G. & Ramprasad, R. Machine learning assisted predictions of intrinsic dielectric breakdown strength of ABX3 perovskites. J. Phys. Chem. C 120, 14575–14580 (2016).
Kim, C., Pilania, G. & Ramprasad, R. From organized high-throughput data to phenomenological theory using machine learning: The example of dielectric breakdown. Chem. Mater. 28, 1304–1311 (2016).
Santos, I., Nieves, J., Penya, Y. K. & Bringas, P. G. in 2009 ICCAS-SICE 4536–4541 (IEEE, 2009).
Yaseen, Z. M. et al. Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv. Eng. Softw. 115, 112–125 (2018).
De Jong, M. et al. A statistical learning framework for materials science: Application to elastic moduli of k-nary inorganic polycrystalline compounds. Sci. Rep. 6, 34256 (2016).
Hamdia, K. M., Lahmer, T., Nguyen-Thoi, T. & Rabczuk, T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Comput. Mater. Sci. 102, 304–313 (2015).
Kauwe, S. K., Graser, J., Vazquez, A. & Sparks, T. D. Machine learning prediction of heat capacity for solid inorganics. Integrat. Mater. Manuf. Innov. 7, 43–51 (2018).
Legrain, F., Carrete, J., van Roekeghem, A., Curtarolo, S. & Mingo, N. How chemical composition alone can predict vibrational free energies and entropies of solids. Chem. Mater. 29, 6220–6227 (2017).
Chen, L., Tran, H., Batra, R., Kim, C. & Ramprasad, R. Machine learning models for the lattice thermal conductivity prediction of inorganic materials. Comput. Mater. Sci. 170, 109155 (2019).
Stanev, V. et al. Machine learning modeling of superconducting critical temperature. NPJ Comput. Mater. 4, 29 (2018).
Balachandran, P. V., Kowalski, B., Sehirlioglu, A. & Lookman, T. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat. Commun. 9, 1668 (2018).
Zhang, Y. & Kim, E.-A. Quantum loop topography for machine learning. Phys. Rev. Lett. 118, 216401 (2017).
Gaultois, M. W. et al. Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Mater. 4, 053213 (2016).
Sendek, A. D. et al. Machine learning-assisted discovery of solid Li-ion conducting materials. Chem. Mater. 31, 342–352 (2018).
Mansouri Tehrani, A. et al. Machine learning directed search for ultraincompressible, superhard materials. J. Am. Chem. Soc. 140, 9844–9853 (2018).
Wu, Y.-J., Sasaki, M., Goto, M., Fang, L. & Xu, Y. Electrically conductive thermally insulating Bi–Si nanocomposites by interface design for thermal management. ACS Appl. Nano Mater. 1, 3355–3363 (2018).
Ren, F. et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci. Adv. 4, eaaq1566 (2018).
Kim, C., Chandrasekaran, A., Huan, T. D., Das, D. & Ramprasad, R. Polymer genome: A data-powered polymer informatics platform for property predictions. J. Phys. Chem. C 122, 17575–17585 (2018).
Kim, C., Batra, R., Chen, L., Tran, H. & Ramprasad, R. Polymer design using genetic algorithm and machine learning. Comput. Mat. Sci. 186, 110067 (2020).
Yoshida, M. et al. Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides. Chem 4, 533–543 (2018).
Meredig, B. et al. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Mol. Syst. Des. Eng. 3, 819–825 (2018).
Bajusz, D., Rácz, A. & Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminform. 7, 20 (2015).
Venkatram, S. et al. Predicting crystallization tendency of polymers using multi-fidelity information fusion and machine learning. J. Phys. Chem. B 124, 6046–6054 (2020).
Pilania, G., Gubernatis, J. E. & Lookman, T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput. Mater. Sci. 129, 156–163 (2017).
Zaspel, P., Huang, B., Harbrecht, H. & von Lilienfeld, O. A. Boosting quantum machine learning models with a multilevel combination technique: Pople diagrams revisited. J. Chem. Theory Comput. 15, 1546–1559 (2018).
Patra, A. et al. A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap. Comput. Mater. Sci. 172, 109286 (2020).
Batra, R., Pilania, G., Uberuaga, B. P. & Ramprasad, R. Multifidelity information fusion with machine learning: A case study of dopant formation energies in hafnia. ACS Appl. Mater. Interfaces 11, 24906–24918 (2019).
Kukreja, S. L., Löfberg, J. & Brenner, M. J. A least absolute shrinkage and selection operator (LASSO) for nonlinear system identification. IFAC Proc. Vol. 39, 814–819 (2006).
Ghiringhelli, L. M. et al. Learning physical descriptors for materials science by compressed sensing. New J. Phys. 19, 023017 (2017).
Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M. & Ghiringhelli, L. M. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2, 083802 (2018).
Bartel, C. J. et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci. Adv. 5, eaav0693 (2019).
Goldschmidt, V. M. Die gesetze der krystallochemie. Naturwissenschaften 14, 477–485 (1926).
Bartel, C. J. et al. Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry. Nat. Commun. 9, 4168 (2018).
Andersen, M., Levchenko, S. V., Scheffler, M. & Reuter, K. Beyond scaling relations for the description of catalytic materials. ACS Catal. 9, 2752–2759 (2019).
Sun, S., Ouyang, R., Zhang, B. & Zhang, T.-Y. Data-driven discovery of formulas by symbolic regression. MRS Bull. 44, 559–564 (2019).
Wang, Y., Wagner, N. & Rondinelli, J. M. Symbolic regression in materials science. MRS Commun. 9, 793–805 (2019).
Hernandez, A., Balasubramanian, A., Yuan, F., Mason, S. A. & Mueller, T. Fast, accurate, and transferable many-body interatomic potentials by symbolic regression. NPJ Comput. Mater. 5, 112 (2019).
Sastry, K., Johnson, D. D., Goldberg, D. E. & Bellon, P. Genetic programming for multitimescale modeling. Phys. Rev. B 72, 085438 (2005).
Gandomi, A. H., Sajedi, S., Kiani, B. & Huang, Q. Genetic programming for experimental big data mining: A case study on concrete creep formulation. Autom. Constr. 70, 89–97 (2016).
Batra, R. & Sankaranarayanan, S. Machine learning for multi-fidelity scale bridging and dynamical simulations of materials. J. Phys. Mater. 3, 031002 (2020).
Jackson, N. E., Webb, M. A. & de Pablo, J. J. Recent advances in machine learning towards multiscale soft materials design. Curr. Opin. Chem. Eng. 23, 106–114 (2019).
Ye, W., Chen, C., Wang, Z., Chu, I.-H. & Ong, S. P. Deep neural networks for accurate predictions of crystal stability. Nat. Commun. 9, 3800 (2018).
Jha, D. et al. ElemNet: Deep learning the chemistry of materials from only elemental composition. Sci. Rep. 8, 17593 (2018).
DeCost, B. L., Lei, B., Francis, T. & Holm, E. A. High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel. Microsc. Microanal. 25, 21–29 (2019).
Nash, W., Drummond, T. & Birbilis, N. A review of deep learning in the study of materials degradation. NPJ Mater. Degrad. 2, 37 (2018).
Cecen, A., Dai, H., Yabansu, Y. C., Kalidindi, S. R. & Song, L. Material structure-property linkages using three-dimensional convolutional neural networks. Acta Mater. 146, 76–84 (2018).
Sanyal, S. et al. MT-CGCNN: Integrating crystal graph convolutional neural network with multitask learning for material property prediction. Preprint at https://arxiv.org/abs/1811.05660 (2018).
Agrawal, A. & Choudhary, A. Deep materials informatics: Applications of deep learning in materials science. MRS Commun. 9, 779–792 (2019).
Zheng, X., Zheng, P. & Zhang, R.-Z. Machine learning material properties from the periodic table using convolutional neural networks. Chem. Sci. 9, 8426–8432 (2018).
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. Schnet–A deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).
Dai, H., Li, C., Coley, C., Dai, B. & Song, L. in Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 8870–8880 (Curran Associates, 2019).
Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10, 370–377 (2019).
You, J., Liu, B., Ying, Z., Pande, V. & Leskovec, J. in Advances in Neural Information Processing Systems 31 (eds Bengio, S. et al) 6410–6421 (Curran Associates, 2018).
Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).
Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31, 3564–3572 (2019).
Aykol, M. et al. Network analysis of synthesizable materials discovery. Nat. Commun. 10, 2018 (2019).
Kearnes, S., McCloskey, K., Berndl, M., Pande, V. & Riley, P. Molecular graph convolutions: Moving beyond fingerprints. J. Comput. Mol. Des. 30, 595–608 (2016).
Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at arXiv https://arxiv.org/abs/1312.6114 (2014).
Goodfellow, I. et al. in Advances in Neural Information Processing Systems 27 (eds Ghahramani, Z. et al.) 2672–2680 (Curran Associates, 2014).
Li, W., Jacobs, R. & Morgan, D. Predicting the thermodynamic stability of perovskite oxides using machine learning models. Comput. Mater. Sci. 150, 454–463 (2018).
Ziletti, A., Kumar, D., Scheffler, M. & Ghiringhelli, L. M. Insightful classification of crystal structures using deep learning. Nat. Commun. 9, 2775 (2018).
Lennard-Jones, J. E. On the determination of molecular fields. II. From the equation of state of gas. Proc. R. Soc. Lond. A 106, 463–477 (1924).
Chenoweth, K., Van Duin, A. C. & Goddard, W. A. ReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidation. J. Phys. Chem. A 112, 1040–1053 (2008).
Liu, H., Fu, Z., Li, Y., Sabri, N. F. A. & Bauchy, M. Parameterization of empirical forcefields for glassy silica using machine learning. MRS Commun. 9, 593–599 (2019).
Chan, H. et al. Machine learning coarse grained models for water. Nat. Commun. 10, 379 (2019).
Chan, H. et al. Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures. Nanoscale 11, 10381–10392 (2019).
Chan, H. et al. Machine learning classical interatomic potentials for molecular dynamics from first-principles training data. J. Phys. Chem. C 123, 6941–6957 (2019).
Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87, 184115 (2013).
Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019).
Behler, J. Perspective: Machine learning potentials for atomistic simulations. J. Chem. Phys. 145, 170901 (2016).
Handley, C. M. & Popelier, P. L. Potential energy surfaces fitted by artificial neural networks. J. Phys. Chem. A 114, 3371–3383 (2010).
Botu, V., Batra, R., Chapman, J. & Ramprasad, R. Machine learning force fields: Construction, validation, and outlook. J. Phys. Chem. C 121, 511–522 (2017).
Huan, T. D. et al. A universal strategy for the creation of machine learning-based atomistic force fields. NPJ Comput. Mater. 3, 37 (2017).
Rowe, P., Csányi, G., Alfè, D. & Michaelides, A. Development of a machine learning potential for graphene. Phys. Rev. B 97, 054303 (2018).
Podryabinkin, E. V., Tikhonov, E. V., Shapeev, A. V. & Oganov, A. R. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Phys. Rev. B 99, 064114 (2019).
Podryabinkin, E. V. & Shapeev, A. V. Active learning of linearly parametrized interatomic potentials. Comput. Mater. Sci. 140, 171–180 (2017).
Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).
Deringer, V. L. et al. Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics. J. Phys. Chem. Lett. 9, 2879–2885 (2018).
Dragoni, D., Daff, T. D., Csányi, G. & Marzari, N. Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron. Phys. Rev. Mater. 2, 013808 (2018).
Zong, H., Pilania, G., Ding, X., Ackland, G. J. & Lookman, T. Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning. NPJ Comput. Mater. 4, 48 (2018).
Artrith, N. & Kolpak, A. M. Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials. Comput. Mater. Sci. 110, 20–28 (2015).
Chiriki, S. & Bulusu, S. S. Modeling of DFT quality neural network potential for sodium clusters: Application to melting of sodium clusters (Na20 to Na40). Chem. Phys. Lett. 652, 130–135 (2016).
Chiriki, S., Jindal, S. & Bulusu, S. S. Neural network potentials for dynamics and thermodynamics of gold nanoparticles. J. Chem. Phys. 146, 084314 (2017).
Sosso, G. C., Miceli, G., Caravati, S., Behler, J. & Bernasconi, M. Neural network interatomic potential for the phase change material GeTe. Phys. Rev. B 85, 174103 (2012).
Artrith, N., Morawietz, T. & Behler, J. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide. Phys. Rev. B 83, 153101 (2011).
Artrith, N. & Urban, A. An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2. Comput. Mater. Sci. 114, 135–150 (2016).
Morawietz, T., Singraber, A., Dellago, C. & Behler, J. How van der Waals interactions determine the unique properties of water. Proc. Natl Acad. Sci. USA 113, 8368–8373 (2016).
Cheng, B., Behler, J. & Ceriotti, M. Nuclear quantum effects in water at the triple point: Using theory as a link between experiments. J. Phys. Chem. Lett. 7, 2210–2215 (2016).
Jose, K. J., Artrith, N. & Behler, J. Construction of high-dimensional neural network potentials using environment-dependent atom pairs. J. Chem. Phys. 136, 194111 (2012).
Gastegger, M., Kauffmann, C., Behler, J. & Marquetand, P. Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes. J. Chem. Phys. 144, 194110 (2016).
Boes, J. R. & Kitchin, J. R. Neural network predictions of oxygen interactions on a dynamic Pd surface. Mol. Simul. 43, 346–354 (2017).
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).
Khorshidi, A. & Peterson, A. A. Amp: A modular approach to machine learning in atomistic simulations. Comput. Phys. Commun. 207, 310–324 (2016).
Wang, H., Zhang, L., Han, J. & Weinan, E. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228, 178–184 (2018).
Shapeev, A. V. Moment tensor potentials: A class of systematically improvable interatomic potentials. Multiscale Model. Simul. 14, 1153–1173 (2016).
Schutt, K. et al. SchNetPack: A deep learning toolbox for atomistic systems. J. Chem. Theory Comput. 15, 448–455 (2018).
Desai, S., Reeve, S. T. & Belak, J. F. Implementing a neural network interatomic model with performance portability for emerging exascale architectures. Preprint at arXiv https://arxiv.org/abs/2002.00054 (2020).
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. J. Comput. Phys. 285, 316–330 (2015).
Zuo, Y. et al. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 124, 731–745 (2020).
Artrith, N., Urban, A. & Ceder, G. Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species. Phys. Rev. B 96, 014112 (2017).
Musil, F., Willatt, M. J., Langovoy, M. A. & Ceriotti, M. Fast and accurate uncertainty estimation in chemical machine learning. J. Chem. Theory Comput. 15, 906–915 (2019).
Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O. & Roitberg, A. E. Less is more: Sampling chemical space with active learning. J. Chem. Phys. 148, 241733 (2018).
Huan, T. D. et al. Iterative-learning strategy for the development of application-specific atomistic force fields. J. Phys. Chem. C 123, 20715–20722 (2019).
Smith, J. S. et al. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat. Commun. 10, 2903 (2019).
Brockherde, F. et al. Bypassing the Kohn-Sham equations with machine learning. Nat. Commun. 8, 872 (2017).
Grisafi, A. et al. Transferable machine-learning model of the electron density. ACS Cent. Sci. 5, 57–64 (2018).
Chandrasekaran, A. et al. Solving the electronic structure problem with machine learning. NPJ Comput. Mater. 5, 22 (2019).
Kamal, D., Chandrasekaran, A., Batra, R. & Ramprasad, R. A charge density prediction model for hydrocarbons using deep neural networks. Mach. Learn. 1, 025003 (2020).
Schmidt, J., Benavides-Riveros, C. L. & Marques, M. A. Machine learning the physical nonlocal exchange–correlation functional of density-functional theory. J. Phys. Chem. Lett. 10, 6425–6431 (2019).
Lei, X. & Medford, A. J. Design and analysis of machine learning exchange-correlation functionals via rotationally invariant convolutional descriptors. Phys. Rev. Mater. 3, 063801 (2019).
Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R. & Burke, K. Finding density functionals with machine learning. Phys. Rev. Lett. 108, 253002 (2012).
Comer, M., Bouman, C. A., De Graef, M. & Simmons, J. P. Bayesian methods for image segmentation. JOM 63, 55–57 (2011).
Simmons, J. et al. Application and further development of advanced image processing algorithms for automated analysis of serial section image data. Model. Simul. Mater. Sci. Eng. 17, 025002 (2008).
Gibert, X., Patel, V. M. & Chellappa, R. Deep multitask learning for railway track inspection. IEEE Trans. Intell. Transp Syst. 18, 153–164 (2016).
Niezgoda, S. R., Yabansu, Y. C. & Kalidindi, S. R. Understanding and visualizing microstructure and microstructure variance as a stochastic process. Acta Mater. 59, 6387–6400 (2011).
Steinmetz, P. et al. Analytics for microstructure datasets produced by phase-field simulations. Acta Mater. 103, 192–203 (2016).
Yang, Z. et al. Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches. Acta Mater. 166, 335–345 (2019).
Yang, Z. et al. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Comput. Mater. Sci. 151, 278–287 (2018).
Yang, Z. et al. Microstructural materials design via deep adversarial learning methodology. J. Mech. Des. 140, 111416 (2018).
Jha, D. et al. Extracting grain orientations from EBSD patterns of polycrystalline materials using convolutional neural networks. Microsc. Microanal. 24, 497–502 (2018).
Ziatdinov, M. et al. Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling. NPJ Comput. Mater. 6, 21 (2020).
Maksov, A. et al. Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2. NPJ Comput. Mater. 5, 12 (2019).
Vasudevan, R. K. et al. Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images. NPJ Comput. Mater. 4, 30 (2018).
Laanait, N., He, Q. & Borisevich, A. Y. Reconstruction of 3-D atomic distortions from electron microscopy with deep learning. Preprint at arXiv https://arxiv.org/abs/1902.06876 (2019).
Laanait, N., Yin, J. & Borisevich, A. in Conference on Neural Information Processing Systems (NeurIPS) 2019 Workshop Deep Inverse (OpenReview, 2019).
Cherukara, M. J., Nashed, Y. S. & Harder, R. J. Real-time coherent diffraction inversion using deep generative networks. Sci. Rep. 8, 16520 (2018).
Godaliyadda, G. D. et al. A supervised learning approach for dynamic sampling. Electron. Imaging 2016, 1–8 (2016).
Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397–402 (2020).
Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4, 383–391 (2019).
Kusne, A. G. et al. On-the-fly machine-learning for high-throughput experiments: Search for rare-earth-free permanent magnets. Sci. Rep. 4, 6367 (2014).
Long, C. et al. Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis. Rev. Sci. Instrum. 78, 072217 (2007).
Long, C., Bunker, D., Li, X., Karen, V. & Takeuchi, I. Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization. Rev. Sci. Instrum. 80, 103902 (2009).
Suram, S. K. et al. Automated phase mapping with AgileFD and its application to light absorber discovery in the V–Mn–Nb oxide system. ACS Comb. Sci. 19, 37–46 (2017).
Gomes, C. P. et al. CRYSTAL: a multi-agent AI system for automated mapping of materials’ crystal structures. MRS Commun. 9, 600–608 (2019).
Bai, J. et al. Phase mapper: Accelerating materials discovery with AI. AI Mag. 39, 15–26 (2018).
Tabor, D. P. et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5–20 (2018).
King, R. D. et al. The automation of science. Science 324, 85–89 (2009).
Williams, K. et al. Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. J. R. Soc. Interface 12, 20141289 (2015).
Nikolaev, P. et al. Autonomy in materials research: A case study in carbon nanotube growth. NPJ Comput. Mater. 2, 16031 (2016).
Wigley, P. B. et al. Fast machine-learning online optimization of ultra-cold-atom experiments. Sci. Rep. 6, 25890 (2016).
Duros, V. et al. Human versus robots in the discovery and crystallization of gigantic polyoxometalates. Angew. Chem. 129, 10955–10960 (2017).
Noack, M. M. et al. A kriging-based approach to autonomous experimentation with applications to x-ray scattering. Sci. Rep. 9, 11809 (2019).
Masubuchi, S. et al. Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices. Nat. Commun. 9, 1413 (2018).
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).
Jensen, K. F. Automated synthesis on a hub-and-spoke system. Nature 579, 346–348 (2020).
Roch, L. M. et al. Chemos: An orchestration software to democratize autonomous discovery. PLoS ONE 15, e0229862 (2020).
Montoya, J. H. et al. Autonomous intelligent agents for accelerated materials discovery. Chem. Sci. 11, 8517–8532 (2020).
Mannodi-Kanakkithodi, A. et al. Rational co-design of polymer dielectrics for energy storage. Adv. Mater. 28, 6277–6291 (2016).
Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T. & Ramprasad, R. Machine learning strategy for accelerated design of polymer dielectrics. Sci. Rep. 6, 20952 (2016).
Pilania, G., Iverson, C. N., Lookman, T. & Marrone, B. L. Machine-learning-based predictive modeling of glass transition temperatures: A case of polyhydroxyalkanoate homopolymers and copolymers. J. Chem. Inf. Model. 59, 5013–5025 (2019).
Batra, R. et al. Polymers for extreme conditions designed using syntax-directed variational autoencoders. Preprint at http://arxiv.org/abs/2011.02551v1 (2020).
Dai, H., Tian, Y., Dai, B., Skiena, S. & Song, L. Syntax-directed variational autoencoder for structured data. Preprint at arXiv https://arxiv.org/abs/1802.08786 (2018).
Kim, B., Lee, S. & Kim, J. Inverse design of porous materials using artificial neural networks. Sci. Adv. 6, eaax9324 (2020).
Corey, E. J. The Logic of Chemical Synthesis (Wiley, 1991).
Coley, C. W., Green, W. H. & Jensen, K. F. Machine learning in computer-aided synthesis planning. Acc. Chem. Res. 51, 1281–1289 (2018).
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).
Segler, M. H., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–601 (2018).
Jin, W., Coley, C., Barzilay, R. & Jaakkola, T. Predicting organic reaction outcomes with Weisfeiler-Lehman network. in Advances in Neural Information Processing Systems 2607–2616 (Cornell University, 2017).
Bradshaw, J., Kusner, M. J., Paige, B., Segler, M. H. & Hernández-Lobato, J. M. A generative model for electron paths. Preprint at arXiv https://arxiv.org/abs/1805.10970 (2018).
Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. Computer-assisted retrosynthesis based on molecular similarity. ACS Cent. Sci. 3, 1237–1245 (2017).
Segler, M. H. & Waller, M. P. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry 23, 5966–5971 (2017).
Liu, B. et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent. Sci. 3, 1103–1113 (2017).
Karpov, P., Godin, G. & Tetko, I. V. in International Conference on Artificial Neural Networks 817–830 (Springer, 2019).
Schwaller, P., Gaudin, T., Lanyi, D., Bekas, C. & Laino, T. “Found in Translation”: Predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chem. Sci. 9, 6091–6098 (2018).
Schwaller, P. et al. Molecular transformer for chemical reaction prediction and uncertainty estimation. ACS Cent. Sci. 5, 1572–1583 (2018).
Kim, E., Huang, K., Jegelka, S. & Olivetti, E. Virtual screening of inorganic materials synthesis parameters with deep learning. NPJ Comput. Mater. 3, 53 (2017).
Rossi, F., Van Beek, P. & Walsh, T. Handbook of Constraint Programming (Elsevier, 2006).
Pun, G. P., Batra, R., Ramprasad, R. & Mishin, Y. Physically informed artificial neural networks for atomistic modeling of materials. Nat. Commun. 10, 2339 (2019).
Zaheer, M. et al. in Advances in Neural Information Processing Systems 3391–3401 (Cornell University, 2017).
Schütt, K. et al. in Advances in Neural Information Processing Systems 991–1001 (Cornell University, 2017).
Zhang, L. et al. in Advances in Neural Information Processing Systems 4436–4446 (Association for Computing Machinery, 2018).
Noé, F., Tkatchenko, A., Müller, K.-R. & Clementi, C. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71, 361–390 (2020).
Chmiela, S. et al. Machine learning of accurate energy-conserving molecular force fields. Sci. Adv. 3, e1603015 (2017).
Chandrasekaran, A., Kim, C., Venkatram, S. & Ramprasad, R. A deep learning solvent-selection paradigm powered by a massive solvent/nonsolvent database for polymers. Macromolecules 53, 4764–4769 (2020).
Zhu, G. et al. Polymer genome–based prediction of gas permeabilities in polymers. J. Polym. Eng. 40, 451–457 (2020).
Zubatyuk, R., Smith, J. S., Leszczynski, J. & Isayev, O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci. Adv. 5, eaav6490 (2019).
Finn, C., Abbeel, P. & Levine, S. in Proceedings of the 34th International Conference on Machine Learning - Volume 70 1126–1135 (JMLR.org, 2017).
Levin, I. NIST Inorganic Crystal Structure Database (ICSD) (National Institute of Standards and Technology, 2018).
Pauling File. paulingfile.com (2020).
Otsuka, S., Kuwajima, I., Hosoya, J., Xu, Y. & Yamazaki, M. in 2011 International Conference on Emerging Intelligent Data and Web Technologies 22–29 (IEEE, 2011).
Groom, C. R., Bruno, I. J., Lightfoot, M. P. & Ward, S. C. The Cambridge structural database. Acta Crystallogr. B Struct. Sci. Cryst. Eng. Mater. 72, 171–179 (2016).
MatWeb. www.matweb.com (2020).
Total Materia. www.totalmateria.com (2020).
INTERGLAD. www.newglass.jp/interglad_n/gaiyo/info_e.html (2020).
Mindat. www.mindat.org (2020).
ASM International. www.asminternational.org (2020).
Downs, R. T. & Hall-Wallace, M. The American Mineralogist crystal structure database. Am. Mineral 88, 247–250 (2003).
O’Mara, J., Meredig, B. & Michel, K. Materials data infrastructure: A case study of the citrination platform to examine data import, storage, and access. JOM 68, 2031–2034 (2016).
Zagorac, D., Müller, H., Ruehl, S., Zagorac, J. & Rehme, S. Recent developments in the Inorganic Crystal Structure Database: Theoretical crystal structure data and related features. J. Appl. Crystallogr. 52, 918–925 (2019).
Pence, H. E. & Williams, A. ChemSpider: An online chemical information resource. J. Chem. Educ. 87, 1123–1124 (2010).
Ogata, T. & Yamazaki, M. in Harnessing The Materials Genome: Accelerated Materials Development via Computational and Experimental Tools, ECI Symposium Series (ECI Digital Archives, 2012).
NIST Materials Data Repository. materialsdata.nist.gov (2020).
Zhao, H. et al. Perspective: NanoMine: A material genome approach for polymer nanocomposites analysis and design. APL Mater. 4, 053204 (2016).
SpringerMaterials Databases. materials.springer.com (2020).
Quirós, M., Gražulis, S., Girdzijauskaite˙, S., Merkys, A. & Vaitkus, A. Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database. J. Cheminform. 10, 23 (2018).
Jain, A. et al. The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).
Kirklin, S. et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. NPJ Comput. Mater. 1, 15010 (2015).
Calderon, C. E. et al. The AFLOW standard for high-throughput materials science calculations. Comput. Mater. Sci. 108, 233–238 (2015).
Choudhary, K. et al. Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms. Sci. Data 5, 180082 (2018).
Hafiz, H. et al. A high-throughput data analysis and materials discovery tool for strongly correlated materials. NPJ Comput. Mater. 4, 63 (2018).
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).
NOMAD Centre of Excellence. nomad-coe.eu (2020).
Nieves, P. et al. Database of novel magnetic materials for high-performance permanent magnet development. Comput. Mater. Sci. 168, 188–202 (2019).
Spencer, P. A brief history of CALPHAD. Calphad 32, 1–8 (2008).
Landis, D. D. et al. The computational materials repository. Comput. Sci. Eng. 14, 51–57 (2012).
Ashton, M., Paul, J., Sinnott, S. B. & Hennig, R. G. Topology-scaling identification of layered solids and stable exfoliated 2D materials. Phys. Rev. Lett. 118, 106101 (2017).
R.R. is grateful to the Office of Naval Research, the Toyota Research Institute, the Department of Energy and the National Science Foundation for financial support on machine-learning-related research through several grants. R.B was supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under contract no. DE-AC02-06CH11357. Use of the Center for Nanoscale Materials, an Office of Science user facility, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract no. DE-AC02-06CH11357. Discussions with K. Lipkowitz on various aspects of informatics and machine learning are greatly acknowledged. The authors are thankful to B. Storey for critical comments on the manuscript.
The authors declare no competing interests.
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Batra, R., Song, L. & Ramprasad, R. Emerging materials intelligence ecosystems propelled by machine learning. Nat Rev Mater 6, 655–678 (2021). https://doi.org/10.1038/s41578-020-00255-y
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