This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …

Rechargeable batteries have become indispensable implements in our daily life and are
considered a promising technology to construct sustainable energy systems in the future …

Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy
sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges …

Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …

Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

Computer simulations can play a central role in the understanding of phase-change
materials and the development of advanced memory technologies. However, direct quantum …