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 …
Molecular simulations with a pretrained neural network and universal pairwise force fields
Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations
that can simultaneously achieve efficiency, accuracy, transferability, and scalability for …
that can simultaneously achieve efficiency, accuracy, transferability, and scalability for …
Towards symbolic XAI–explanation through human understandable logical relationships between features
Abstract Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency
and trust in AI systems. Traditional XAI methods typically provide a single level of abstraction …
and trust in AI systems. Traditional XAI methods typically provide a single level of abstraction …
Crash testing machine learning force fields for molecules, materials, and interfaces: Model analysis in the tea challenge 2023
Atomistic simulations are routinely employed in academia and industry to study the behavior
of molecules, materials, and their interfaces. Central to these simulations are force fields …
of molecules, materials, and their interfaces. Central to these simulations are force fields …
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
While machine learning (ML) models have been able to achieve unprecedented accuracies
across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a …
across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a …
Mechanical Properties of Nanoporous Graphenes: Transferability of Graph Machine‐Learned Force Fields Compared to Local and Reactive Potentials
Nanoporous and chemically‐bridged graphene nanosheets span a wide chemical space
with a broad set of applications in sensing and electronics. Modeling the structure and …
with a broad set of applications in sensing and electronics. Modeling the structure and …
Complete and Efficient Covariants for Three-Dimensional Point Configurations with Application to Learning Molecular Quantum Properties
When physical properties of molecules are being modeled with machine learning, it is
desirable to incorporate SO (3)-covariance. While such models based on low body order …
desirable to incorporate SO (3)-covariance. While such models based on low body order …
Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks
The application of machine learning methodologies for predicting properties within materials
science has garnered significant attention. Among recent advancements, Kolmogorov …
science has garnered significant attention. Among recent advancements, Kolmogorov …
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
Equivariant Graph Neural Networks (GNNs) that incorporate E (3) symmetry have achieved
significant success in various scientific applications. As one of the most successful models …
significant success in various scientific applications. As one of the most successful models …
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 …