Chemical reaction networks form the heart of microkinetic models, which are one of the key
tools available for gaining detailed mechanistic insight into heterogeneous catalytic …

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 …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

Graph neural networks that model 3D data, such as point clouds or atoms, are typically
desired to be $ SO (3) $ equivariant, ie, equivariant to 3D rotations. Unfortunately …

Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying
Transformers to the domain of 3D atomistic systems. However, they are still limited to small …

Extensive efforts to gather materials data have largely overlooked potential data
redundancy. In this study, we present evidence of a significant degree of redundancy across …

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

The recent rise of computational, data-driven research has significant potential to accelerate
materials discovery. Automated workflows and materials databases are being rapidly …

Under operating conditions, the dynamics of water and ions confined within protonic
aluminosilicate zeolite micropores are responsible for many of their properties, including …

Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …