Abstract Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While
CSP can be addressed by employing currently-prevailing generative models (eg diffusion …

Generating the periodic structure of stable materials is a long-standing challenge for the
material design community. This task is difficult because stable materials only exist in a low …

In the context of the 21st century and the fourth industrial revolution, the substantial
proliferation of data has established it as a valuable resource, fostering enhanced …

A linear regression-based machine learned interatomic potential (MLIP) was developed for
the silicon–carbon system. The MLIP was predominantly trained on structures discovered …

Crystals are the foundation of numerous scientific and industrial applications. While various
learning-based approaches have been proposed for crystal generation, existing methods …

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. Existing learning-
based generative approaches seldom capture the full symmetries of the crystal structure …

Differential Evolution (DE) is a method of optimization used in symmetrical optimization
problems and also in problems that are not even continuous, and are noisy and change over …

Abstract Machine learning (ML) is a fast-evolving field of artificial intelligence that has been
applied in many domains due to the increasing availability of computerized databases …

In this study, we propose a general way of finding a new crystal structure starting from a
database. We also introduce a concise representation of the data based on principal …

Crystalline materials are a fundamental component in next-generation technologies, yet
modeling their distribution presents unique computational challenges. Of the plausible …