Hierarchical visualization of materials space with graph convolutional neural networks

Xie, Tian; Grossman, Jeffrey C.

doi:10.1063/1.5047803

Title: Hierarchical visualization of materials space with graph convolutional neural networks

Journal Article · Tue Nov 06 00:00:00 EST 2018 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.5047803· OSTI ID:1543881

Xie, Tian ^[1]; Grossman, Jeffrey C. ^[1]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science and Engineering

The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property spaces. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1543881

Journal Information:: Journal of Chemical Physics, Vol. 149, Issue 17; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 41 works

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References (56)

Learning atoms for materials discovery Zhou, Quan; Tang, Peizhe; Liu, Shenxiu Proceedings of the National Academy of Sciences, Vol. 115, Issue 28 https://doi.org/10.1073/pnas.1801181115	journal	June 2018
Graph Theory Meets Ab Initio Molecular Dynamics: Atomic Structures and Transformations at the Nanoscale Pietrucci, Fabio; Andreoni, Wanda Physical Review Letters, Vol. 107, Issue 8 https://doi.org/10.1103/physrevlett.107.085504	journal	August 2011
Computational high-throughput screening of electrocatalytic materials for hydrogen evolution Greeley, Jeff; Jaramillo, Thomas F.; Bonde, Jacob Nature Materials, Vol. 5, Issue 11, p. 909-913 https://doi.org/10.1038/nmat1752	journal	October 2006
Progress, Challenges, and Opportunities in Two-Dimensional Materials Beyond Graphene Butler, Sheneve Z.; Hollen, Shawna M.; Cao, Linyou ACS Nano, Vol. 7, Issue 4, p. 2898-2926 https://doi.org/10.1021/nn400280c	journal	March 2013
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties Schütt, K. T.; Glawe, H.; Brockherde, F. Physical Review B, Vol. 89, Issue 20 https://doi.org/10.1103/physrevb.89.205118	journal	May 2014
Machine learning for the structure–energy–property landscapes of molecular crystals Musil, Félix; De, Sandip; Yang, Jack Chemical Science, Vol. 9, Issue 5 https://doi.org/10.1039/c7sc04665k	journal	January 2018
Neural network models of potential energy surfaces Blank, Thomas B.; Brown, Steven D.; Calhoun, August W. The Journal of Chemical Physics, Vol. 103, Issue 10 https://doi.org/10.1063/1.469597	journal	September 1995
Systematic comparison of crystalline and amorphous phases: Charting the landscape of water structures and transformations Pietrucci, Fabio; Martoňák, Roman The Journal of Chemical Physics, Vol. 142, Issue 10 https://doi.org/10.1063/1.4914138	journal	March 2015
Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization Seko, Atsuto; Togo, Atsushi; Hayashi, Hiroyuki Physical Review Letters, Vol. 115, Issue 20 https://doi.org/10.1103/physrevlett.115.205901	journal	November 2015
Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap Spiwok, Vojtěch; Králová, Blanka The Journal of Chemical Physics, Vol. 135, Issue 22 https://doi.org/10.1063/1.3660208	journal	December 2011
Observation of an all-boron fullerene Zhai, Hua-Jin; Zhao, Ya-Fan; Li, Wei-Li Nature Chemistry, Vol. 6, Issue 8 https://doi.org/10.1038/nchem.1999	journal	July 2014
The high-throughput highway to computational materials design Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno Nature Materials, Vol. 12, Issue 3 https://doi.org/10.1038/nmat3568	journal	February 2013
Comparing molecules and solids across structural and alchemical space De, Sandip; Bartók, Albert P.; Csányi, Gábor Physical Chemistry Chemical Physics, Vol. 18, Issue 20 https://doi.org/10.1039/c6cp00415f	journal	January 2016
MoleculeNet: a benchmark for molecular machine learning Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N. Chemical Science, Vol. 9, Issue 2 https://doi.org/10.1039/c7sc02664a	journal	January 2018
Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions Rohrdanz, Mary A.; Zheng, Wenwei; Clementi, Cecilia Annual Review of Physical Chemistry, Vol. 64, Issue 1 https://doi.org/10.1146/annurev-physchem-040412-110006	journal	April 2013
Big Data of Materials Science: Critical Role of the Descriptor Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V. Physical Review Letters, Vol. 114, Issue 10 https://doi.org/10.1103/physrevlett.114.105503	journal	March 2015
Graphene-Like Two-Dimensional Materials Xu, Mingsheng; Liang, Tao; Shi, Minmin Chemical Reviews, Vol. 113, Issue 5, p. 3766-3798 https://doi.org/10.1021/cr300263a	journal	January 2013
SchNet – A deep learning architecture for molecules and materials Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019779	journal	June 2018
Machine Learning Energies of 2 Million Elpasolite $(A B C_{2} D_{6})$ Crystals Faber, Felix A.; Lindmaa, Alexander; von Lilienfeld, O. Anatole Physical Review Letters, Vol. 117, Issue 13 https://doi.org/10.1103/physrevlett.117.135502	journal	September 2016
The Inorganic Crystal Structure Database (ICSD)—Present and Future Hellenbrandt, Mariette Crystallography Reviews, Vol. 10, Issue 1 https://doi.org/10.1080/08893110410001664882	journal	January 2004
β-Rhombohedral Boron: At the Crossroads of the Chemistry of Boron and the Physics of Frustration Ogitsu, Tadashi; Schwegler, Eric; Galli, Giulia Chemical Reviews, Vol. 113, Issue 5 https://doi.org/10.1021/cr300356t	journal	March 2013
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning Schmidt, Jonathan; Shi, Jingming; Borlido, Pedro Chemistry of Materials, Vol. 29, Issue 12 https://doi.org/10.1021/acs.chemmater.7b00156	journal	May 2017
Assessing Local Structure Motifs Using Order Parameters for Motif Recognition, Interstitial Identification, and Diffusion Path Characterization Zimmermann, Nils E. R.; Horton, Matthew K.; Jain, Anubhav Frontiers in Materials, Vol. 4 https://doi.org/10.3389/fmats.2017.00034	journal	November 2017
Review of recent progress in chemical stability of perovskite solar cells Niu, Guangda; Guo, Xudong; Wang, Liduo Journal of Materials Chemistry A, Vol. 3, Issue 17, p. 8970-8980 https://doi.org/10.1039/c4ta04994b	journal	December 2014
Atom-centered symmetry functions for constructing high-dimensional neural network potentials Behler, Jörg The Journal of Chemical Physics, Vol. 134, Issue 7 https://doi.org/10.1063/1.3553717	journal	February 2011
New cubic perovskites for one- and two-photon water splitting using the computational materials repository Castelli, Ivano E.; Landis, David D.; Thygesen, Kristian S. Energy & Environmental Science, Vol. 5, Issue 10 https://doi.org/10.1039/c2ee22341d	journal	January 2012
High-throughput screening of solid-state catalyst libraries Senkan, Selim M. Nature, Vol. 394, Issue 6691 https://doi.org/10.1038/28575	journal	July 1998
Molecular graph convolutions: moving beyond fingerprints Kearnes, Steven; McCloskey, Kevin; Berndl, Marc Journal of Computer-Aided Molecular Design, Vol. 30, Issue 8 https://doi.org/10.1007/s10822-016-9938-8	journal	August 2016
On representing chemical environments Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/physrevb.87.184115	journal	May 2013
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert Physical Review Letters, Vol. 108, Issue 5 https://doi.org/10.1103/physrevlett.108.058301	journal	January 2012
Accelerating materials property predictions using machine learning Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep02810	journal	September 2013
Alchemical and structural distribution based representation for universal quantum machine learning Faber, Felix A.; Christensen, Anders S.; Huang, Bing The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5020710	journal	June 2018
Computational screening of perovskite metal oxides for optimal solar light capture Castelli, Ivano E.; Olsen, Thomas; Datta, Soumendu Energy Environ. Sci., Vol. 5, Issue 2 https://doi.org/10.1039/c1ee02717d	journal	January 2012
Data-Driven Learning of Total and Local Energies in Elemental Boron Deringer, Volker L.; Pickard, Chris J.; Csányi, Gábor Physical Review Letters, Vol. 120, Issue 15 https://doi.org/10.1103/physrevlett.120.156001	journal	April 2018
Simplifying the representation of complex free-energy landscapes using sketch-map Ceriotti, Michele; Tribello, Gareth A.; Parrinello, Michele Proceedings of the National Academy of Sciences, Vol. 108, Issue 32 https://doi.org/10.1073/pnas.1108486108	journal	July 2011
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Behler, Jörg; Parrinello, Michele Physical Review Letters, Vol. 98, Issue 14 https://doi.org/10.1103/physrevlett.98.146401	journal	April 2007
Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction Das, P.; Moll, M.; Stamati, H. Proceedings of the National Academy of Sciences, Vol. 103, Issue 26 https://doi.org/10.1073/pnas.0603553103	journal	June 2006
Crystal structure representations for machine learning models of formation energies Faber, Felix; Lindmaa, Alexander; von Lilienfeld, O. Anatole International Journal of Quantum Chemistry, Vol. 115, Issue 16 https://doi.org/10.1002/qua.24917	journal	April 2015
Mapping uncharted territory in ice from zeolite networks to ice structures Engel, Edgar A.; Anelli, Andrea; Ceriotti, Michele Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-04618-6	journal	June 2018
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Study of the hydrogen solid solution in thulium Bonnet, J. E.; Daou, J. N. Journal of Physics and Chemistry of Solids, Vol. 40, Issue 6 https://doi.org/10.1016/0022-3697(79)90056-8	journal	January 1979
Combinatorial and High-Throughput Screening of Materials Libraries: Review of State of the Art Potyrailo, Radislav; Rajan, Krishna; Stoewe, Klaus ACS Combinatorial Science, Vol. 13, Issue 6 https://doi.org/10.1021/co200007w	journal	August 2011
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) Saal, James E.; Kirklin, Scott; Aykol, Muratahan JOM, Vol. 65, Issue 11 https://doi.org/10.1007/s11837-013-0755-4	journal	September 2013
Metrics for measuring distances in configuration spaces Sadeghi, Ali; Ghasemi, S. Alireza; Schaefer, Bastian The Journal of Chemical Physics, Vol. 139, Issue 18 https://doi.org/10.1063/1.4828704	journal	November 2013
Quantum-chemical insights from deep tensor neural networks Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms13890	journal	January 2017
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D. Nature Materials, Vol. 15, Issue 10 https://doi.org/10.1038/nmat4717	journal	August 2016
Machine Learning Force Fields: Construction, Validation, and Outlook Botu, V.; Batra, R.; Chapman, J. The Journal of Physical Chemistry C, Vol. 121, Issue 1 https://doi.org/10.1021/acs.jpcc.6b10908	journal	December 2016
A general-purpose machine learning framework for predicting properties of inorganic materials Ward, Logan; Agrawal, Ankit; Choudhary, Alok npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.28	journal	August 2016
Universal fragment descriptors for predicting properties of inorganic crystals Isayev, Olexandr; Oses, Corey; Toher, Cormac Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms15679	journal	June 2017
Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav Chemistry of Materials, Vol. 22, Issue 12 https://doi.org/10.1021/cm100795d	journal	June 2010
Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints Isayev, Olexandr; Fourches, Denis; Muratov, Eugene N. Chemistry of Materials, Vol. 27, Issue 3 https://doi.org/10.1021/cm503507h	journal	January 2015
Efficient nonparametric $n$ -body force fields from machine learning Glielmo, Aldo; Zeni, Claudio; De Vita, Alessandro Physical Review B, Vol. 97, Issue 18 https://doi.org/10.1103/physrevb.97.184307	journal	May 2018
Perovskites: The Emergence of a New Era for Low-Cost, High-Efficiency Solar Cells Snaith, Henry J. The Journal of Physical Chemistry Letters, Vol. 4, Issue 21, p. 3623-3630 https://doi.org/10.1021/jz4020162	journal	October 2013
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav Computational Materials Science, Vol. 68 https://doi.org/10.1016/j.commatsci.2012.10.028	journal	February 2013
Study of the hydrogen solid solution in thulium Bonnet, J. E.; Daou, J. N. Solid State Communications, Vol. 28, Issue 12 https://doi.org/10.1016/0038-1098(78)90671-3	journal	December 1978
Data-mined similarity function between material compositions Yang, Lusann; Ceder, Gerbrand Physical Review B, Vol. 88, Issue 22 https://doi.org/10.1103/physrevb.88.224107	journal	December 2013

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