Combinatorial screening for new materials in unconstrained composition space with machine learning

Abstract

Typically, computational screens for new materials sharply constrain the compositional search space, structural search space, or both, for the sake of tractability. To lift these constraints, we construct a machine learning model from a database of thousands of density functional theory (DFT) calculations. The resulting model can predict the thermodynamic stability of arbitrary compositions without any other input and with six orders of magnitude less computer time than DFT. We use this model to scan roughly 1.6 million candidate compositions for novel ternary compounds (A x B y C z), and predict 4500 new stable materials. Our method can be readily applied to other descriptors of interest to accelerate domain-specific materials discovery.

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

“…The benefits of machine learning for accelerated materials data analysis have already been realized, with numerous studies showing the great potential for research and discovery. [199][200][201] These studies include a wide range of materials analysis challenges including crystal structure [202][203][204] and phase diagram 130,[205][206][207] determination, materials property predictions, 208,209 micrograph analysis, 210,211 development of interatomic potentials [212][213][214] and energy functionals 215 to improve materials simulations, and on-the-fly data analysis of high-throughput experiments. 216 …”

Research Update: Towards designed functionalities in oxide-based electronic materials

“…48 The latter focuses on high-throughput computations of many properties for many materials being cataloged in databases 22,[49][50][51] or combinatorial synthesis for MBD. 52,53 The high-throughput and combinatorial synthesis approaches have in part been energized by national materials discovery initiatives around the globe and have encouraged data organization and curation, which is invaluable to achieving data-mined materials discoveries.…”

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

“…Other predictive modeling work in materials science domain, whether it is to predict the melting temperatures of binary inorganic compounds [38], the formation energy of ternary compounds [6], the mechanical properties of metal alloys [39], or which crystal structure is likely to form at a certain composition [40,41], also sees the limitation of learning with a single agent.…”