Analyzing machine learning models to accelerate generation of fundamental materials insights
Open accessAbstract
Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Novel challenges for ML in materials science include coping with moderately sized datasets and inferring physical principles from trained models.
“novel challenges for ML in materials science include coping with moderately sized datasets and inferring physical principles from trained models [800].”
From Tackling climate change with machine learning (2023) · cites this paper as [800]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447 · see citing works
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572 · see citing works
Solving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215 · see citing works
A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831 · see citing works
Towards fully autonomous driving: Systems and algorithms
Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Kolter, J. Z., Langer, D., Pink, O., Pratt, V., et al. (2011) · 2011 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2011.5940562 · Cited by 1,374 · see citing works
Reddington, E., Sapienza, A., Gurauꝉ, B., et al. (1998) · Science · doi:10.1126/science.280.5370.1735 · Cited by 948 · see citing works
Accelerated search for materials with targeted properties by adaptive design
Xue, D., Balachandran, P. V., Hogden, J., Theiler, J. P., Xue, D., & Lookman, T. (2016) · Nature Communications · doi:10.1038/ncomms11241 · Cited by 801 · see citing works
Systematic behaviour in trivalent lanthanide charge transfer energies
Dorenbos, P. (2003) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/15/49/018 · Cited by 661 · see citing works
Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint
Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000249929 · Cited by 634 · see citing works
Machine learning modeling of superconducting critical temperature
Stanev, V., Oses, C., Kusne, A. G., Rodriguez, E., Paglione, J., Stefano Curtarolo, A., & Takeuchi, I. (2018) · npj Computational Materials · doi:10.1038/s41524-018-0085-8 · Cited by 500 · see citing works
Ziatdinov, M. A., Dyck, O., Maksov, A., Li, X., Sang, X., Xiao, K., Unocic, R. R., Vasudevan, R. K., Jesse, S., & Kalinin, S. V. (2017) · ACS Nano · doi:10.1021/acsnano.7b07504 · Cited by 431 · see citing works
Transparent, explainable, and accountable AI for robotics
Wachter, S., Mittelstadt, B., & Floridi, L. (2017) · Science Robotics · doi:10.1126/scirobotics.aan6080 · Cited by 413 · see citing works
Data Mined Ionic Substitutions for the Discovery of New Compounds
Hautier, G., Fischer, C., Ehrlacher, V., Jain, A., & Ceder, G. (2011) · Inorganic Chemistry · doi:10.1021/ic102031h · Cited by 394 · see citing works
Autonomy in materials research: a case study in carbon nanotube growth
Nikolaev, P. N., Hooper, D. J., Webber, F. C., Rao, R. S., Decker, K., Krein, M. P., Poleski, J., Barto, R., & Maruyama, B. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.31 · Cited by 383 · see citing works
Discovery of a useful thin-film dielectric using a composition-spread approach
Dover, R. B. V., Schneemeyer, L. F., & Fleming, R. M. (1998) · Nature · doi:10.1038/32381 · Cited by 356 · see citing works
The 2019 materials by design roadmap
Alberi, K., Nardelli, M. B., Zakutayev, A., Mitáš, L., Stefano Curtarolo, A., Jain, A., Fornari, M., Marzari, N., Takeuchi, I., Green, M. L., et al. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aad926 · Cited by 320 · see citing works
Self-citationIdentification of a Blue Photoluminescent Composite Material from a Combinatorial Library
Wang, J., Yoo, Y. K., Gao, C., et al. (1998) · Science · doi:10.1126/science.279.5357.1712 · Cited by 290 · see citing works
Gradient-Based Attribution Methods
Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_9 · Cited by 284 · see citing works
Zhou, S., & Gan, J. Q. (2008) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2008.05.016 · Cited by 270 · see citing works
Synthesis and characterisation of bismuth(III) vanadate
Gotić, M., Musić, S., Ivanda, M., et al. (2005) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2004.10.075 · Cited by 269 · see citing works
Green, M. L., Takeuchi, I., & Hattrick-Simpers, J. R. (2013) · Journal of Applied Physics · doi:10.1063/1.4803530 · Cited by 259 · see citing works
Effects of molybdenum substitution on the photocatalytic behavior of BiVO4
Yao, W., Iwaï, H., & Ye, J. (2008) · Dalton Transactions · doi:10.1039/b713338c · Cited by 216 · see citing works
Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
Welborn, M., Cheng, L., & Miller, T. F. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00636 · Cited by 215 · see citing works
Kondo, R., Yamakawa, S., Masuoka, Y., Tajima, S., & Asahi, R. (2017) · Acta Materialia · doi:10.1016/j.actamat.2017.09.004 · Cited by 211 · see citing works
Learning atoms for materials discovery
Zhou, Q., Tang, P., Liu, S., Pan, J., Yan, Q., & Zhang, S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801181115 · Cited by 205 · see citing works
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
Mascharka, D., Tran, P., Soklaski, R., & Majumdar, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00519 · Cited by 203 · see citing works
Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment
Yan, Q., Yu, J., Suram, S. K., Zhou, L., Shinde, A., Newhouse, P. F., Chen, W., Li, G., Persson, K. A., Gregoire, J. M., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619940114 · Cited by 191 · see citing works
Self-citationInformation Science for Materials Discovery and Design
Lookman, T., Alexander, F. J., & Rajan, K. (2015) · Springer series in materials science · doi:10.1007/978-3-319-23871-5 · Cited by 182 · see citing works
Combinatorial Materials Sciences: Experimental Strategies for Accelerated Knowledge Discovery
Rajan, K. (2008) · Annual Review of Materials Research · doi:10.1146/annurev.matsci.38.060407.130217 · Cited by 133 · see citing works
Hardcastle, F. D., Wachs, I. E., Eckert, H., et al. (1991) · Journal of Solid State Chemistry · doi:10.1016/0022-4596(91)90135-5 · Cited by 132 · see citing works
Hattrick-Simpers, J. R., Gregoire, J. M., & Kusne, A. G. (2016) · APL Materials · doi:10.1063/1.4950995 · Cited by 111 · see citing works
Self-citationLearning surface molecular structures via machine vision
Ziatdinov, M. A., Maksov, A., & Kalinin, S. V. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0038-7 · Cited by 108 · see citing works
Zhou, D., Pang, L., Wang, H., et al. (2011) · Journal of Materials Chemistry · doi:10.1039/c1jm14004c · Cited by 106 · see citing works
Building data-driven models with microstructural images: Generalization and interpretability
Ling, J., Hutchinson, M., Antono, E., DeCost, B., Holm, E. A., & Meredig, B. (2017) · Materials Discovery · doi:10.1016/j.md.2018.03.002 · Cited by 98 · see citing works
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., et al. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.6b00153 · Cited by 80 · see citing works
Self-citationHigh-throughput determination of structural phase diagram and constituent phases using GRENDEL
Kusne, A. G., Keller, D. A., Anderson, A. Y., Zaban, A., & Takeuchi, I. (2015) · Nanotechnology · doi:10.1088/0957-4484/26/44/444002 · Cited by 77 · see citing works
Hattrick-Simpers, J. R., Choudhary, K. K., & Corgnale, C. (2018) · Molecular Systems Design & Engineering · doi:10.1039/c8me00005k · Cited by 72 · see citing works
Kajita, S., Ohba, N., Jinnouchi, R., & Asahi, R. (2017) · Scientific Reports · doi:10.1038/s41598-017-17299-w · Cited by 71 · see citing works
Structural, electronic and optical features of molybdenum-doped bismuth vanadium oxide
Merupo, V. I., Velumani, S., Oza, G., et al. (2015) · Materials Science in Semiconductor Processing · doi:10.1016/j.mssp.2014.12.057 · Cited by 61 · see citing works
Gutkowski, R., Khare, C., Conzuelo, F., et al. (2017) · Energy & Environmental Science · doi:10.1039/c7ee00287d · Cited by 60 · see citing works
Combinatorial alloying improves bismuth vanadate photoanodes via reduced monoclinic distortion
Newhouse, P. F., Guevarra, D., Umehara, M., et al. (2018) · Energy & Environmental Science · doi:10.1039/c8ee00179k · Cited by 23 · see citing works
Self-citationCu(001)上の鉄ポルフィリン分子:磁気特性に対する吸着層と配位子の影響
Herper, H. C., Bernien, M., Bhandary, S., Hermanns, C. F., Krüger, A., Miguel, J., Weis, C., Schmitz‐Antoniak, C., Krumme, B., Bovenschen, D., et al. (2013) · Physical Review B · Cited by 19 · see citing works
Newhouse, P. F., Guevarra, D., Umehara, M., Boyd, D. A., Zhou, L., Cooper, J. K., Haber, J. A., & Gregoire, J. M. (2018) · Chemical Communications · doi:10.1039/c8cc07156j · Cited by 15 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Jurafsky, D. & Martin, J. H. Speech and Language Processing: An Introduction to Natural Language Processing. In Computational Linguistics and Speech Recogni- tion (Pearson Education, London, 2000).
- Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. http://arxiv.org/abs/ 1312.6034 (2013). Accessed 19 Apr 2014.
Cited by
Sorted by citations
Machine learning for renewable energy materials
Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
She, C., Huang, Q., Chen, C., Jiang, Y., Fan, Z., & Gao, J. (2021) · Journal of Materials Chemistry A · doi:10.1039/d1ta08194b · Cited by 59 · see citing works
- Authors
- Umehara, M., Stein, H. S., Guevarra, D., Newhouse, P. F., Boyd, D. A., & Gregoire, J. M.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Gregoire, John M.
- Venue
- npj Computational Materials
- Published
- 2019-03-08
- Volume
- 5
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2921711398
- Cited by
- 91
- References
- 51
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 15 · 2022: 18 · 2021: 14 · 2020: 18 · 2019: 9
- FWCI
- 5.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Computational Materials (publishedVersion, cc-by)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- DOE | SC | Basic Energy Sciences (DE-SC0004993)
- An Accelerated Materials Design and Discovery grant from the Toyota Research Institute.
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- materials discovery, photoanodes, Raman spectroscopy, convolutional neural networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
