Crystal structure prediction with machine learning-based element substitution

Abstract

The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems, such as those with many atoms per unit cell. Here, we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning. It is shown that a binary classifier, trained on a large number of already identified crystal structures, can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96.4%. For a given query composition with an unknown crystal structure, the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied. Apart from the local relaxation calculation of the identified templates, the proposed method does not use ab initio calculations. The potential of this substation-based CSP is demonstrated for a wide variety of crystal systems.

Material transformers: deep learning language models for generative materials design

“…While several machine learning models have been developed for synthesizability prediction [53], formation energy prediction [54], and e-above-hull calculation, these models and algorithms usually require the availability of the crystal structures which are not available for composition generators that we propose here. To do that, we can use the recently developed TCSP algorithms [5,55] or the deep learning-based [56], and global optimization-based crystal structure prediction tools [57,58] to predict the crystal structures for the generated hypothetical compositions by our materials transformer models. Together, we are able to explore and discover new materials in a much larger area of the almost infinite chemical design space.…”

Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials

“…While prediction models have been proposed for synthesizability prediction [53], formation energy prediction [54], and e-above-hull calculation, these models and algorithms usually require the availability of the crystal structures. Fortunately, recent progress in template based [55,52], deep learning based [56], and global optimization based crystal structure prediction tools [57,58] have made it possible to guess the crystal structures for increasing families of materials, which can be combined with our composition generators to explore and discover new materials.…”