E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …
equivariant neural network approach for learning interatomic potentials from ab-initio …
Incorporating long-range physics in atomic-scale machine learning
The most successful and popular machine learning models of atomic-scale properties derive
their transferability from a locality ansatz. The properties of a large molecule or a bulk …
their transferability from a locality ansatz. The properties of a large molecule or a bulk …
Raman spectrum and polarizability of liquid water from deep neural networks
We introduce a scheme based on machine learning and deep neural networks to model the
environmental dependence of the electronic polarizability in insulating materials. Application …
environmental dependence of the electronic polarizability in insulating materials. Application …
Machine learning Hubbard parameters with equivariant neural networks
Density-functional theory with extended Hubbard functionals (DFT+ U+ V) provides a robust
framework to accurately describe complex materials containing transition-metal or rare-earth …
framework to accurately describe complex materials containing transition-metal or rare-earth …
Regression clustering for improved accuracy and training costs with molecular-orbital-based machine learning
Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has
been shown to be an accurate and transferable approach to the prediction of post-Hartree …
been shown to be an accurate and transferable approach to the prediction of post-Hartree …
An overview of recent advances and challenges in predicting compound-protein interaction (CPI)
Compound-protein interactions (CPIs) are critical in drug discovery for identifying
therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) …
therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) …
General atomic neighborhood fingerprint for machine learning-based methods
To facilitate chemical space exploration for material screening or to accelerate
computationally expensive first-principles simulations, inexpensive surrogate models that …
computationally expensive first-principles simulations, inexpensive surrogate models that …
Tensor improve equivariant graph neural network for molecular dynamics prediction
C Jiang, Y Zhang, Y Liu, J Peng - Computational Biology and Chemistry, 2024 - Elsevier
Molecular dynamics (MD) simulations are essential for molecular structure optimization,
drug-drug interactions, and other fields of drug discovery by simulating the motion of …
drug-drug interactions, and other fields of drug discovery by simulating the motion of …
Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution
We present design and implementation of a novel neural network potential (NNP) and its
combination with an electrostatic embedding scheme, commonly used within the context of …
combination with an electrostatic embedding scheme, commonly used within the context of …
Predicting Energetic material properties and investigating the effect of pore morphology on shock sensitivity via machine learning
AD Casey - 2020 - search.proquest.com
An improved understanding of energy localization (“hot spots”) is needed to improve the
safety and performance of explosives. In this work I establish a variety of experimental and …
safety and performance of explosives. In this work I establish a variety of experimental and …