Construction of high accuracy machine learning interatomic potential for surface/interface of nanomaterials—A review
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …
interfaces bestow them with various exceptional properties. These properties, however, also …
Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach
Heavy metals pose a significant threat to ecosystems and human health because of their
toxic properties and their ability to bioaccumulate in living organisms. Traditional removal …
toxic properties and their ability to bioaccumulate in living organisms. Traditional removal …
A Euclidean transformer for fast and stable machine learned force fields
Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …
[HTML][HTML] Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
Artificial intelligence for science in quantum, atomistic, and continuum systems
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 …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules
We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM)
dataset that contains the structural and electronic information of 59,783 low-and high-energy …
dataset that contains the structural and electronic information of 59,783 low-and high-energy …
Pre-training with fractional denoising to enhance molecular property prediction
Deep learning methods have been considered promising for accelerating molecular
screening in drug discovery and material design. Due to the limited availability of labelled …
screening in drug discovery and material design. Due to the limited availability of labelled …
Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing
Geometric deep learning has been revolutionizing the molecular modeling field. Despite the
state-of-the-art neural network models are approaching ab initio accuracy for molecular …
state-of-the-art neural network models are approaching ab initio accuracy for molecular …
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural network toolbox that addresses both the requirements of
method development and the application of atomistic machine learning. Version 2.0 comes …
method development and the application of atomistic machine learning. Version 2.0 comes …
Ewald-based long-range message passing for molecular graphs
Neural architectures that learn potential energy surfaces from molecular data have
undergone fast improvement in recent years. A key driver of this success is the Message …
undergone fast improvement in recent years. A key driver of this success is the Message …