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 …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

Renowned for their high porosity and structural diversity, metal–organic frameworks (MOFs)
are a promising class of materials for a wide range of applications. In recent decades, with …

The development of machine learning models for electrocatalysts requires a broad set of
training data to enable their use across a wide variety of materials. One class of materials …

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 …

Applications of machine learning techniques for materials modeling typically involve
functions that are known to be equivariant or invariant to specific symmetries. While graph …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Modeling the energy and forces of atomic systems is a fundamental problem in
computational chemistry with the potential to help address many of the world's most pressing …

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 …