Virtual screening of inorganic materials synthesis parameters with deep learning

Abstract

Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, highdimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO 3 synthesis parameter sets, propose driving factors for brookite TiO 2 formation, and identify correlations between alkali-ion intercalation and MnO 2 polymorph selection.

Empirical modeling of dopability in diamond-like semiconductors

“…Machine learning has proven successful in understanding and predicting energy and entropy, 29 potentials and forces, [30][31][32] structure, physical, and elastic properties, [33][34][35][36][37][38] bandgap, 34,39,40 and defects, 41 as well as enabling high-throughput screening and discovery, [42][43][44][45][46] and guiding experimental synthesis. 47,48 In order to properly model and interpret dopability, the construction of an empirical dataset for cross-validation is of vital importance. While other physical properties have been tabulated in databases, there are few resources where carrier concentration in semiconductors has been collected.…”

Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data

“…Albeit this assumption can be seen as unreasonable it allowed us to significantly enhance the results of modeling. The descriptors describing the synthesis process are efficiently used in virtual screening applications in materials science . Heat‐treatment information including the temperature and time required for the calcination and sintering processes has been normalized to a scale of zero to one accepting the maximal and minimal known temperatures and time limits.…”

Machine Learning Approaches for Thermoelectric Materials Research

“…Moreover, the potential driving factors for synthesis outcomes were also provided. In other words, the framework can be used to represent plausible advice regarding the synthesis conditions for novel syntheses …”