Quantitative topic analysis of materials science literature using natural language processing
Materials science research has garnered extensive attention from industry, society, policy,
and academia. However, understanding the research landscape and extracting strategic …
and academia. However, understanding the research landscape and extracting strategic …
Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids
This work presents an E (3) equivariant graph neural network called HamGNN, which can fit
the electronic Hamiltonian matrix of molecules and solids by a complete data-driven method …
the electronic Hamiltonian matrix of molecules and solids by a complete data-driven method …
Graph neural networks classify molecular geometry and design novel order parameters of crystal and liquid
Molecular dynamics simulation produces three-dimensional data on molecular structures.
The classification of molecular structure is an important task. Conventionally, various order …
The classification of molecular structure is an important task. Conventionally, various order …
Quantifying the uncertainties in modeling soft composites via a multiscale approach
The goal of this paper is to evaluate the uncertainties in soft composites composed of a soft
solid as the matrix filled by incompressible liquid inclusions. The surface stresses at the …
solid as the matrix filled by incompressible liquid inclusions. The surface stresses at the …
Equivariant neural network force fields for magnetic materials
Neural network force fields have significantly advanced ab initio atomistic simulations across
diverse fields. However, their application in the realm of magnetic materials is still in its early …
diverse fields. However, their application in the realm of magnetic materials is still in its early …
Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property predictions
H Zhan, X Zhu, Z Qiao, J Hu - Analytica Chimica Acta, 2023 - Elsevier
Determining various properties of molecules is a critical step in drug discovery. Recently,
with the improvement of large heterogeneous datasets and the development of deep …
with the improvement of large heterogeneous datasets and the development of deep …
Novel approach for designing order parameters of clathrate hydrate structures by graph neural network
Clathrate hydrates continue to be the focus of active research efforts due to their use in
energy resources, transportation, and storage-related applications. Therefore, it is crucial to …
energy resources, transportation, and storage-related applications. Therefore, it is crucial to …
GNNs for mechanical properties prediction of strut-based lattice structures
B Jiang, Y Wang, H Niu, X Cheng, P Zhao… - International Journal of …, 2024 - Elsevier
The mechanical properties of strut-based lattice structures are greatly influenced by cell
topology, which can be modified by changing connections between nodes within a single …
topology, which can be modified by changing connections between nodes within a single …
[HTML][HTML] Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations
Machine learning interatomic potential (MLIP) overcomes the challenges of high
computational costs in density-functional theory and the relatively low accuracy in classical …
computational costs in density-functional theory and the relatively low accuracy in classical …
[HTML][HTML] Machine learning potential for Ab Initio phase transitions of zirconia
Zirconia has been extensively used in aerospace, military, biomedical and industrial fields
due to its unusual combination of high mechanical, electrical and thermal properties …
due to its unusual combination of high mechanical, electrical and thermal properties …