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CRYSTAL: a multi-agent AI system for automated mapping of materials' crystal structures

  • Carla P. Gomes (a1), Junwen Bai (a1), Yexiang Xue (a1), Johan Björck (a1), Brendan Rappazzo (a1), Sebastian Ament (a1), Richard Bernstein (a1), Shufeng Kong (a1), Santosh K. Suram (a2), R. Bruce van Dover (a3) and John M. Gregoire (a2)...


We introduce CRYSTAL, a multi-agent AI system for crystal-structure phase mapping. CRYSTAL is the first system that can automatically generate a portfolio of physically meaningful phase diagrams for expert-user exploration and selection. CRYSTAL outperforms previous methods to solve the example Pd-Rh-Ta phase diagram, enabling the discovery of a mixed-intermetallic methanol oxidation electrocatalyst. The integration of multiple data-knowledge sources and learning and reasoning algorithms, combined with the exploitation of problem decompositions, relaxations, and parallelism, empowers AI to supersede human scientific data interpretation capabilities and enable otherwise inaccessible scientific discovery in materials science and beyond.


Corresponding author

Address all correspondence to Carla P. Gomes at and John M. Gregoire at


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Current address: Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.


Current address: Toyota Research Institute, Los Altos, CA 94022, USA.



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1.Artificial intelligence. Science 349, 248 (2015).
2.Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., and Hassabis, D.: Mastering the game of Go without human knowledge. Nature 550, 354 (2017).
3.Tabor, D.P., Roch, L.M., Saikin, S.K., Kreisbeck, C., Sheberla, D., Montoya, J.H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., Amador-Bedolla, C., Brabec, C.J., Maruyama, B., Persson, K.A., and Aspuru-Guzik, A.: Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5 (2018).
4.De Luna, P., Wei, J., Bengio, Y., Aspuru-Guzik, A., and Sargent, E.: Use machine learning to find energy materials. Nature 552, 23 (2017).
5.Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., and Kim, C.: Machine learning in materials informatics: recent applications and prospects. Nat. Comput. Mater. 3, 54 (2017).
6.Nikolaev, P., Hooper, D., Webber, F., Rao, R., Decker, K., Krein, M., Poleski, J., Barto, R., and Maruyama, B.: Autonomy in materials research: a case study in carbon nanotube growth. Nat. Comput. Mater. 2, 16031 (2016).
7.Smalley, E.: AI-powered drug discovery captures pharma interest. Nat. Biotechnol. 35, 604 (2017).
8.King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.G.K., Bryant, C.H., Muggleton, S.H., Kell, D.B., and Oliver, S.G.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247 (2004).
9.Green, M.L., Choi, C.L., Hattrick-Simpers, J.R., Joshi, A.M., Takeuchi, I., Barron, S.C., Campo, E., Chiang, T., Empedocles, S., Gregoire, J.M., Kusne, A.G., Martin, J., Mehta, A., Persson, K., Trautt, Z., Duren, J.V., and Zakutayev, A.: Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Appl. Phys. Rev. 4, 011105 (2017).
10.Kusne, A.G., Gao, T., Mehta, A., Ke, L., Nguyen, M.C., Ho, K.-M., Antropov, V., Wang, C.-Z., Kramer, M.J., Long, C., and Takeuchi, I.: On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets. Sci. Rep. 4, 6367 (2014).
11.Reddington, E., Sapienza, A., Gurau, B., Viswanathan, R., Sarangapani, S., Smotkin, E.S., and Mallouk, T.E.: Combinatorial electrochemistry: a highly parallel, optical screening method for discovery of better electrocatalysts. Science 280, 1735 (1998).
12.Hattrick-Simpers, J.R., Gregoire, J.M., and Kusne, A.G.: Perspective: composition–structure–property mapping in high-throughput experiments: turning data into knowledge. APL Mater. 4, 053211 (2016).
13.Baumes, L.A., Moliner, M., Nicoloyannis, N., and Corma, A.: A reliable methodology for high throughput identification of a mixture of crystallographic phases from powder x-ray diffraction data. Cryst. Eng. Comm. 10, 1321 (2008).
14.Lee, D.D. and Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788 (1999).
15.Long, C.J., Bunker, D., Li, X., Karen, V.L., and 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).
16.Kusne, A.G., Keller, D., Anderson, A., Zaban, A., and Takeuchi, I.: High-throughput determination of structural phase diagram and constituent phases using GRENDEL. Nanotechnology 26, 444002 (2015).
17.LeBras, R., Damoulas, T., Gregoire, J.M., Sabharwal, A., Gomes, C.P., and van Dover, R.B.: Constraint Reasoning and Kernel Clustering for Pattern Decomposition with Scaling, in Principles and Practice of Constraint Programming – CP 2011: 17th International Conference, CP 2011, Perugia, Italy, September 12–16, 2011. Proceedings, edited by J. Lee (Springer Berlin Heidelberg, Berlin, Heidelberg, 2011), p. 508.
18.Cichocki, A., Zdunek, R., Phan, A.H., and Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation (John Wiley & Sons, Chichester, West Sussex, UK, 2009).
19.Smaragdis, P.: Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sources from Monophonic Inputs, in Independent Component Analysis and Blind Signal Separation: Fifth International Conference, ICA 2004, Granada, Spain, September 22–24, 2004. Proceedings, edited by C. G. Puntonet and A. Prieto (Springer Berlin Heidelberg, Berlin, Heidelberg, 2004), p. 494.
20.Suram, S.K., Xue, Y., Bai, J., Le Bras, R., Rappazzo, B., Bernstein, R., Bjorck, J., Zhou, L., van Dover, R.B., Gomes, C.P., and Gregoire, J.M.: Automated phase mapping with AgileFD and its application to light absorber discovery in the V–Mn–Nb oxide system. ACS Comb. Sci. 19, 37 (2017).
21.Bai, J., Bjorck, J., Xue, Y., Suram, S.K., Gregoire, J., and Gomes, C.: Relaxation methods for constrained matrix factorization problems: solving the phase mapping problem in materials discovery, in International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (Springer 2017), p. 104.
22.Bianchini, C. and Shen, P.K.: Palladium-based electrocatalysts for alcohol oxidation in half cells and in direct alcohol fuel cells. Chem. Rev. 109, 4183 (2009).
23.Gregoire, J.M., Tague, M.E., Cahen, S., Khan, S., Abruña, H.C.D., DiSalvo, F.J., and van Dover, R.B.: Improved fuel cell oxidation catalysis in Pt1−xTax. Chem. Mater. 22, 1080 (2009).
24.Gregoire, J.M., Dale, D., Kazimirov, A., DiSalvo, F.J., and van Dover, R.B.: High energy x-ray diffraction/x-ray fluorescence spectroscopy for high-throughput analysis of composition spread thin films. Rev. Sci. Instrum. 80, 123905 (2009).
25.Jin, J., Prochaska, M., Rochefort, D., Kim, D., Zhuang, L., Disalvo, F., Vandover, R., and Abruna, H.: A high-throughput search for direct methanol fuel cell anode electrocatalysts of type PtxBiyPbz. Appl. Surf. Sci. 254, 653 (2007).
26.Stanev, V., Vesselinov, V.V., Kusne, A.G., Antoszewski, G., Takeuchi, I., and Alexandrov, B.S.: Unsupervised phase mapping of x-ray diffraction data by nonnegative matrix factorization integrated with custom clustering. npj Comput. Mater. 4, 43 (2018).
27.Liu, H., Song, C., Zhang, L., Zhang, J., Wang, H., and Wilkinson, D.P.: A review of anode catalysis in the direct methanol fuel cell. J. Power Sources 155, 95 (2006).
28.Andersen, M., Medford, A.J., Nørskov, J.K., and Reuter, K.: Scaling-relation-based analysis of bifunctional catalysis: the case for homogeneous bimetallic alloys. ACS Catal. 7, 3960 (2017).
29.Casado-Rivera, E., Gál, Z., Angelo, A.C.D., Lind, C., DiSalvo, F.J., and Abruña, H.D.: Electrocatalytic oxidation of formic acid at an ordered intermetallic PtBi surface. ChemPhysChem 4, 193 (2003).
30.Tague, M.E., Gregoire, J.M., Legard, A., Smith, E., Dale, D., Hennig, R., DiSalvo, F.J., van Dover, R.B., and Abruña, H.D.: High throughput thin film Pt-M alloys for fuel electrooxidation: low concentrations of M (M = Sn, Ta, W, Mo, Ru, Fe, In, Pd, Hf, Zn, Zr, Nb, Sc, Ni, Ti, V, Cr, Rh). J. Electrochem. Soc. 159, F880 (2012).
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MRS Communications
  • ISSN: 2159-6859
  • EISSN: 2159-6867
  • URL: /core/journals/mrs-communications
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