Data generation for machine learning interatomic potentials and beyond
The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …
machine learning models for predicting molecular properties and behavior. Recent strides in …
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
B Mortazavi - Advanced Energy Materials, 2024 - Wiley Online Library
This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …
materials. Initially, ML algorithms were successfully applied to screen materials databases …
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 …
Ab initio characterization of protein molecular dynamics with AI2BMD
Biomolecular dynamics simulation is a fundamental technology for life sciences research,
and its usefulness depends on its accuracy and efficiency,–. Classical molecular dynamics …
and its usefulness depends on its accuracy and efficiency,–. Classical molecular dynamics …
Prospective de novo drug design with deep interactome learning
De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …
chemical and pharmacological properties. We present a computational approach utilizing …
ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
The emergence of artificial intelligence is profoundly impacting computational chemistry,
particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional …
particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional …
Grappa–a machine learned molecular mechanics force field
Simulating large molecular systems over long timescales requires force fields that are both
accurate and efficient. In recent years, E (3) equivariant neural networks have lifted the …
accurate and efficient. In recent years, E (3) equivariant neural networks have lifted the …
Molecular simulations with a pretrained neural network and universal pairwise force fields
Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations
that can simultaneously achieve efficiency, accuracy, transferability, and scalability for …
that can simultaneously achieve efficiency, accuracy, transferability, and scalability for …
Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials
Machine learned potentials are becoming a popular tool to define an effective energy model
for complex systems, either incorporating electronic structure effects at the atomistic …
for complex systems, either incorporating electronic structure effects at the atomistic …
Towards symbolic XAI–explanation through human understandable logical relationships between features
Abstract Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency
and trust in AI systems. Traditional XAI methods typically provide a single level of abstraction …
and trust in AI systems. Traditional XAI methods typically provide a single level of abstraction …