Large language models (LLMs) such as generative pretrained transformers (GPTs) have
shown potential for various commercial applications, but their applicability for materials …

Abstract Machine learning potentials have revolutionised the field of atomistic simulations in
recent years and are becoming a mainstay in the toolbox of computational scientists. This …

We consider achieving equivariance in machine learning systems via frame averaging.
Current frame averaging methods involve a costly sum over large frames or rely on sampling …

We consider the prediction of general tensor properties of crystalline materials, including
dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the …

Crystalline materials, with their symmetrical and periodic structures, possess a diverse array
of properties and have been widely used in various fields, ranging from electronic devices to …

Graph neural networks have been proven to have unique advantages in predicting material
properties, and significant progress has been made in many studies in this field. Bond angle …

Predicting properties of crystals from their structures is a fundamental yet challenging task in
materials science. Unlike molecules, crystal structures exhibit infinite periodic arrangements …

To date, the International Zeolite Association Structure Commission (IZA-SC) has cataloged
merely 255 distinct zeolite structures, with millions of theoretically possible structures yet to …

Predicting the tensor properties of crystalline materials is a fundamental task in materials
science. Unlike single-value property prediction, which is inherently invariant, tensor …

Recent advances in deep learning have enabled the generation of realistic data by training
generative models on large datasets of text, images, and audio. While these models have …