Accelerated search for materials with targeted properties by adaptive design

Abstract

Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

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“…Ideally, the result of training such models would be the experimental realization of new materials with promising properties. The MI community has produced several such success stories, including thermoelectric compounds, 10,11 shapememory alloys, 12 superalloys, 13 and 3d-printable high-strength aluminum alloys. 14 However, in many cases, a model is itself the output of a study, and the question becomes: to what extent could the model be used to drive materials discovery?…”

Accelerated search for BaTiO 3 -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

“…However, these approaches have largely been data-driven with materials knowledge used to construct features. Although some of the recent adaptive strategies can potentially overcome certain shortcomings (6,29), a principled approach requires integrating prior information with available data within a Bayesian framework. Few attempts, if any, in the materials literature incorporate theory within a Bayesian formalism to constrain the model outcomes.…”

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

“…Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design. Machine learning (ML) methods are becoming increasingly popular in accelerating the design of new materials by predicting material properties with accuracy close to ab initio calculations, but with computational speeds orders of magnitude faster [1][2][3]. The arbitrary size of crystal systems poses a challenge as they need to be represented as a fixed length vector in order to be compatible with most ML algorithms.…”

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