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 …
[HTML][HTML] Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials
Molecular dynamics (MD) simulations play an important role in understanding and
engineering heat transport properties of complex materials. An essential requirement for …
engineering heat transport properties of complex materials. An essential requirement for …
Heat flux for semilocal machine-learning potentials
The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in
materials. However, it requires an accurate description of the potential-energy surface and …
materials. However, it requires an accurate description of the potential-energy surface and …
Thermal transport of LiPS solid electrolytes with ab initio accuracy
The vast amount of computational studies on electrical conduction in solid state electrolytes
is not mirrored by comparable efforts addressing thermal conduction, which has been …
is not mirrored by comparable efforts addressing thermal conduction, which has been …
Guest editorial: Special Topic on software for atomistic machine learning
Welcome to the Journal of Chemical Physics' Special Topic on Software for Atomistic
Machine Learning. For some years now, search engines have been dominating our online …
Machine Learning. For some years now, search engines have been dominating our online …
Neural network enabled molecular dynamics study of phase transitions
The advances of machine-learned force fields have opened up molecular dynamics (MD)
simulations for compounds for which ab initio MD is too resource intensive and phenomena …
simulations for compounds for which ab initio MD is too resource intensive and phenomena …
From Peptides to Nanostructures: 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 …
Thermal conductivity of solid electrolytes with ab initio accuracy
The vast amount of computational studies on electrical conduction in solid-state electrolytes
is not mirrored by comparable efforts addressing thermal conduction, which has been …
is not mirrored by comparable efforts addressing thermal conduction, which has been …
Machine learning-accelerated molecular dynamics calculations for investigating the thermal modulation by ferroelectric domain wall in KTN single crystals
S Li, L Fang, T Liu, X Wang, B Liu, Y Zhang, X Lv… - Computational Materials …, 2025 - Elsevier
Ferroelectric perovskite materials, containing ferroelectric domain configurations, are
promising thermal switching candidates in thermal management due to their fast response …
promising thermal switching candidates in thermal management due to their fast response …