Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …
methods in computational materials science and chemistry. The focus of the present review …
Deep dive into machine learning density functional theory for materials science and chemistry
With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
Machine learning directed optimization of classical molecular modeling force fields
BJ Befort, RS DeFever, GM Tow… - Journal of Chemical …, 2021 - ACS Publications
Accurate force fields are necessary for predictive molecular simulations. However,
developing force fields that accurately reproduce experimental properties is challenging …
developing force fields that accurately reproduce experimental properties is challenging …
Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces
The atomic descriptors used in machine learning to predict forces are often high
dimensional. In general, by retrieving a significant amount of structural information from …
dimensional. In general, by retrieving a significant amount of structural information from …
Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter
In this paper Δ learning is used to map orbital-free density functional theory (DFT) ionic
forces to the corresponding Kohn-Sham (KS) DFT ionic forces. The development of the …
forces to the corresponding Kohn-Sham (KS) DFT ionic forces. The development of the …
Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations
Owing to the advances in computational techniques and the increase in computational
power, atomistic simulations of materials can simulate large systems with higher accuracy …
power, atomistic simulations of materials can simulate large systems with higher accuracy …
Extraction of local structure differences in silica based on unsupervised learning
Silica exhibits a rich phase diagram with numerous stable structures existing at different
temperature and pressure conditions, including its glassy form. In large-scale atomistic …
temperature and pressure conditions, including its glassy form. In large-scale atomistic …
A reactive molecular dynamics model for uranium/hydrogen containing systems
A Soshnikov, R Lindsey, A Kulkarni… - The Journal of Chemical …, 2024 - pubs.aip.org
Uranium-based materials are valuable assets in the energy, medical, and military industries.
However, understanding their sensitivity to hydrogen embrittlement is particularly …
However, understanding their sensitivity to hydrogen embrittlement is particularly …
Data-driven determination of the spin Hamiltonian parameters and their uncertainties: The case of the zigzag-chain compound
R Tamura, K Hukushima, A Matsuo, K Kindo, M Hase - Physical Review B, 2020 - APS
We propose a data-driven technique to estimate the spin Hamiltonian, including uncertainty,
from multiple physical quantities. Using our technique, an effective model of KCu 4 P 3 O 12 …
from multiple physical quantities. Using our technique, an effective model of KCu 4 P 3 O 12 …
Emergence of Accurate Atomic Energies from Machine Learned Noble Gas Potentials
The quantum theory of atoms in molecules (QTAIM) gives access to well-defined local
atomic energies. Due to their locality, these energies are potentially interesting in fitting …
atomic energies. Due to their locality, these energies are potentially interesting in fitting …