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  • Review Article
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Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics

Nature Reviews Materials volume 8, pages 241–260 (2023)Cite this article

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Abstract

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural to wonder what lessons can be learned from other fields undergoing similar developments. In this Review, we comparatively assess the evolution of applied ML in materials research, gameplaying and robotics. We observe ML being integrated into each field in three phases: first into discrete hardware and software tools (toolset integration); second across different steps in a workflow (workflow integration); and third through the incorporation, generation and representation of generalizable knowledge beyond any one study (knowledge integration). We identify transferrable lessons from gameplaying and robotics to materials research, including adaptive and accessible automation, the gamification of grand challenges to focus community efforts on specific workflow integrations and motivate benchmarks and canonical datasets, and the adoption of hybrid (data-based and model-based) algorithms that combine domain expertise and current learning to economically address high-complexity tasks. We identify opportunities for researchers from different fields to collaborate, including novel ways to represent and integrate a rich but heterogeneous corpus of knowledge (such as heuristics, physical laws, literature or data) with ML algorithms to create new knowledge, and safe and equitable deployment of technologies with societally beneficial outcomes.

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Fig. 1: Three integration challenges to enable the application of machine learning to materials research.
Fig. 2: Two kinds of workflow integration for materials research.
Fig. 3: Comparison of data availability in materials research and gameplaying and robotics.
Fig. 4: The evolution of solution methodologies in computer gameplaying, and the factors that influence the transitions.
Fig. 5: The evolution of robotics methodologies and the factors that influence the transitions.
Fig. 6: Learning algorithms are evolving towards hybrid approaches.
Fig. 7: Modelling approaches in materials research.

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Data availability

The code to extract the data for Fig. 3b can be found at https://github.com/jserecatala/Scripts-for-data-driven-projects/tree/main/NRevMat_3b_Materials_Data_Sparsity.

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Acknowledgements

The authors benefited from insights deriving from discussions with other researchers too numerous to count. Particularly formative discussions included the authors’ co-workers L. Zhe, D. Ren, P. Teyssonneyre, S. I. Parker Tian (SMART), N. Azizan, G. Barbastathis, S. Bonner, P. Doyle, E. A. Fitzgerald, T. Ganapathy, R. Gomez-Bombarelli, F. Oviedo, Q. Liang, E. E. Looney, R. Naik, E. M. Sachs (originally proposed autonomous invention), A. E. Siemenn, J. Thapa, A. Tiihonen, M. Short, S. Sun (MIT), V. Chellappan, J. Cheng, S. Jayavelu, J. Kumar, Y.-F. Lim, S. Ramasamy, C. Troadec and the AMDM team (A*STAR), D. Bash, G. Bazan, A. Cheetham, S. Khan and A. Thean (NUS), and in alphabetical order by affiliation, B. Maruyama (AFRL), K. Brown (Boston University), J. Schrier (Fordham University), C. Brabec and M. Peters (Forschungszentrum Jülich), A. Norquist (Haverford College), A. Walsh (Imperial), Y. Jung (KAIST), A. Cooper (Liverpool), J.-H. Simpers (NIST), Y. Liu (Shanghai University), R. Simpson and K. Wood (SUTD; introduced T.B. to the concept of autonomous discovery), K. Kumar (Temasek), E. Sargent and A. Aspuru-Guzik (University of Toronto), S. Jaffer (TOTAL), C. Burlinguette and J. Hein (UBC), D. P. Fenning and S. Ping Ong (UCSD), D. Beck and H. Hillhouse (Univ. of Washington) and countless more.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Kedar Hippalgaonkar

  2. Institute of Materials Research and Engineering, Agency for Science Technology and Research, Singapore, Singapore

    Kedar Hippalgaonkar

  3. Department of Mathematics, National University of Singapore, Singapore, Singapore

    Qianxiao Li

  4. Department of Chemical Engineering, Tsinghua University, Beijing, China

    Xiaonan Wang

  5. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    John W. Fisher III

  6. DeepMind, London, UK

    James Kirkpatrick

  7. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Tonio Buonassisi

  8. Singapore MIT Alliance for Research and Technology (SMART), Singapore, Singapore

    Tonio Buonassisi

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The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to Kedar Hippalgaonkar or Tonio Buonassisi.

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Competing interests

K.H. and T.B. hold equity in a start-up company, Xinterra, focused on commercializing ML technologies for accelerated materials development. J.K. is a research scientist at Google DeepMind. The other authors declare no competing interests.

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Related links

Arduino: https://www.arduino.cc/

AtomWork-Advanced database: https://atomwork-adv.nims.go.jp/

Average computational power: https://en.wikipedia.org/wiki/Computer_performance_by_orders_of_magnitude#Deciscale_computing_(10%E2%88%921)

Critical Assessment of protein Structure Prediction: https://predictioncenter.org/

DARPA Grand Challenges for autonomous driving: https://www.darpa.mil/about-us/timeline/-grand-challenge-for-autonomous-vehicles

Data-centric AI: https://datacentric.ai/

Guiding principles: https://futureoflife.org/ai-principles/

ICSD: https://icsd.products.fiz-karlsruhe.de/

Initial test for any AI ethics filter: https://futureoflife.org/ai-principles/

JARVIS: https://www.nist.gov/programs-projects/jarvis-dft

Larger discussion involving ethics in AI: https://www.microsoft.com/en-us/ai/our-approach-to-ai

MaRDA: https://www.marda-alliance.org/

Materials Project: https://MaterialsProject.org/

NOMAD: https://nomad-lab.eu/

NREL: https://htem.nrel.gov/

Ongoing efforts by NIST: https://materialsdata.nist.gov

Openrobotics: http://www.openrobotics.org/

OpenTrons: https://opentrons.com/

OQMD: http://oqmd.org/

ROS: https://www.ros.org/

ROS Industrial: https://rosindustrial.org

Specialized robots to aid scientific research: https://www.youtube.com/watch?v=03p2CADwGF8&t=1447s

The Crystal Structure Blind Test: https://www.ccdc.cam.ac.uk/Community/initiatives/cspblindtests/

The Open Catalyst Project: https://opencatalystproject.org/challenge.html

YARP: https://www.yarp.it/git-master/

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Hippalgaonkar, K., Li, Q., Wang, X. et al. Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics. Nat Rev Mater 8, 241–260 (2023). https://doi.org/10.1038/s41578-022-00513-1

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