Machine learning interatomic potentials and long-range physics
Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …
networks, have resulted in short-range models that can infer interaction energies with near …
Applications and advances in machine learning force fields
S Wu, X Yang, X Zhao, Z Li, M Lu, X **e… - Journal of Chemical …, 2023 - ACS Publications
Force fields (FFs) form the basis of molecular simulations and have significant implications
in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is …
in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is …
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 …
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
We develop a neuroevolution-potential (NEP) framework for generating neural network-
based machine-learning potentials. They are trained using an evolutionary strategy for …
based machine-learning potentials. They are trained using an evolutionary strategy for …
How to train a neural network potential
AM Tokita, J Behler - The Journal of Chemical Physics, 2023 - pubs.aip.org
The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm
change in the development of potential energy surfaces for atomistic simulations. By …
change in the development of potential energy surfaces for atomistic simulations. By …
Artificial intelligence-enhanced quantum chemical method with broad applicability
High-level quantum mechanical (QM) calculations are indispensable for accurate
explanation of natural phenomena on the atomistic level. Their staggering computational …
explanation of natural phenomena on the atomistic level. Their staggering computational …
Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene
Recently a novel two-dimensional (2D) C 60 based crystal called quasi-hexagonal-phase
fullerene (QHPF) has been fabricated and demonstrated to be a promising candidate for 2D …
fullerene (QHPF) has been fabricated and demonstrated to be a promising candidate for 2D …
Structure and pore size distribution in nanoporous carbon
We study the structural and mechanical properties of nanoporous (NP) carbon materials by
extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To …
extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To …
Science‐Driven Atomistic Machine Learning
JT Margraf - Angewandte Chemie International Edition, 2023 - Wiley Online Library
Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …
[HTML][HTML] Gaussian approximation potentials: Theory, software implementation and application examples
Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic
Potentials routinely used to model materials and molecular systems on the atomic scale. The …
Potentials routinely used to model materials and molecular systems on the atomic scale. The …