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 …
Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters
Obtaining microscopic structure-property relationships for grain boundaries is challenging
due to their complex atomic structures. Recent efforts use machine learning to derive these …
due to their complex atomic structures. Recent efforts use machine learning to derive these …
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost
Long-range correlations are essential across numerous machine learning tasks, especially
for data embedded in Euclidean space, where the relative positions and orientations of …
for data embedded in Euclidean space, where the relative positions and orientations of …
ANI-1ccx-gelu Universal Interatomic Potential and Its Fine-Tuning: Toward Accurate and Efficient Anharmonic Vibrational Frequencies
SF Alavi, Y Chen, YF Hou, F Ge, P Zheng… - The Journal of …, 2025 - ACS Publications
Calculating anharmonic vibrational modes of molecules for interpreting experimental
spectra is one of the most interesting challenges of contemporary computational chemistry …
spectra is one of the most interesting challenges of contemporary computational chemistry …
The importance of sampling the dynamical modes: Reevaluating benchmarks for invariant and equivariant features of machine learning potentials for simulation of …
Machine learning interatomic potentials (MLIPs) are rapidly gaining interest for molecular
modeling, as they provide a balance between quantum-mechanical level descriptions of …
modeling, as they provide a balance between quantum-mechanical level descriptions of …
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins,
and materials on wide time and length scales. Their reliability and predictiveness, however …
and materials on wide time and length scales. Their reliability and predictiveness, however …