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Emerging materials intelligence ecosystems propelled by machine learning


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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Fig. 1: Materials intelligence ecosystems.
Fig. 2: Strategies for materials data generation and acquisition.
Fig. 3: Materials fingerprinting.
Fig. 4: The general learning problem in materials science and its solution using common machine learning techniques.
Fig. 5: Impact of machine learning on the materials research infrastructure.
Fig. 6: Opportunities for materials design using advanced machine learning algorithms.


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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.

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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).

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