Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm
Machine-learning algorithms have been proposed to capture electrostatic interactions by
using effective partial charges. These algorithms often rely on a pretrained model for partial …
using effective partial charges. These algorithms often rely on a pretrained model for partial …
Multiscale modeling for enhanced battery health analysis: Pathways to longevity
K Yang, L Zhang, W Wang, C Long, S Yang… - Carbon …, 2024 - Wiley Online Library
The issues of health assessment and lifespan prediction have always been prominent
challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews …
challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews …
Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization
A Singh, J Wang, G Henkelman, L Li - Journal of Chemical Theory …, 2024 - ACS Publications
Geometry optimization is an important tool used for computational simulations in the fields of
chemistry, physics, and material science. Developing more efficient and reliable algorithms …
chemistry, physics, and material science. Developing more efficient and reliable algorithms …
Automatic Feature Selection for Atom-Centered Neural Network Potentials Using a Gradient Boosting Decision Algorithm
Atom-centered neural network (ANN) potentials have shown high accuracy and
computational efficiency in modeling atomic systems. A crucial step in developing reliable …
computational efficiency in modeling atomic systems. A crucial step in developing reliable …
Local-environment-guided selection of atomic structures for the development of machine-learning potentials
Machine learning potentials (MLPs) have attracted significant attention in computational
chemistry and materials science due to their high accuracy and computational efficiency …
chemistry and materials science due to their high accuracy and computational efficiency …