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 …
Spatio-spectral graph neural networks
Abstract Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for
learning on graph-structured data. However, key limitations of ℓ-step MPGNNs are that their" …
learning on graph-structured data. However, key limitations of ℓ-step MPGNNs are that their" …
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …
providing an expressive view of the chemical space and multiscale processes. Their …
The Potential of Neural Network Potentials
In the next half-century, physical chemistry will likely undergo a profound transformation,
driven predominantly by the combination of recent advances in quantum chemistry and …
driven predominantly by the combination of recent advances in quantum chemistry and …
Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo
Metal-organic frameworks (MOF) are an attractive class of porous materials due to their
immense design space, allowing for application-tailored properties. Properties of interest …
immense design space, allowing for application-tailored properties. Properties of interest …
Long-short-range message-passing: A physics-informed framework to capture non-local interaction for scalable molecular dynamics simulation
Computational simulation of chemical and biological systems using ab initio molecular
dynamics has been a challenge over decades. Researchers have attempted to address the …
dynamics has been a challenge over decades. Researchers have attempted to address the …
Efficient machine learning force field for large-scale molecular simulations of organic systems
To address the computational challenges of ab initio molecular dynamics and the accuracy
limitations of empirical force fields, the introduction of machine learning force fields (MLFFs) …
limitations of empirical force fields, the introduction of machine learning force fields (MLFFs) …
Latent Ewald summation for machine learning of long-range interactions
Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such
as electrostatic and dispersion forces. In this work, we introduce a straightforward and …
as electrostatic and dispersion forces. In this work, we introduce a straightforward and …
Modeling Zinc Complexes Using Neural Networks
Understanding the energetic landscapes of large molecules is necessary for the study of
chemical and biological systems. Recently, deep learning has greatly accelerated the …
chemical and biological systems. Recently, deep learning has greatly accelerated the …
Implicit transfer operator learning: multiple time-resolution surrogates for molecular dynamics
Computing properties of molecular systems rely on estimating expectations of the
(unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted …
(unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted …