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Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

Abstract

Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.

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Fig. 1: Human-centred and data-driven molecular and materials discovery paradigms.
Fig. 2: Human-crafted and data-driven descriptors.
Fig. 3: Sequential screening tiers in the modern high-throughput screening approach.
Fig. 4: Active learning of materials for performance optimization.
Fig. 5: Inverse design of materials with generative models.

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Acknowledgements

This work was supported by the Advanced Research Projects Agency–Energy (ARPA-E), US Department of Energy under award number DE-AR0001220, and by the Toyota Research Institute through the Accelerated Materials Design and Discovery programme.

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Y.S.-H. and J.P. planned and outlined the manuscript. J.P. contributed to the writing of all sections. K.A., L.G. and Y.Y. contributed to the writing of the Edisonian section. L.G.,C.J.E. and R.R.R. contributed to the writing of the model-system section. K.A. and Y.Y. contributed to the writing of the descriptor section. T.X., C.J.E., J.R.L. and D.J.Z. contributed to the writing of the high-throughput and machine learning sections. J.R.L. contributed to the writing of the active learning section. D.S.-K. and R.G.-B. contributed to the writing of the inverse design section. All authors contributed to the editing and proofreading of the manuscript.

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Correspondence to Rafael Gómez-Bombarelli or Yang Shao-Horn.

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Peng, J., Schwalbe-Koda, D., Akkiraju, K. et al. Human- and machine-centred designs of molecules and materials for sustainability and decarbonization. Nat Rev Mater (2022). https://doi.org/10.1038/s41578-022-00466-5

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