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Equivariant representations for molecular Hamiltonians

B2FIND2026-04-11 收录

分子哈密顿量预测

等变神经网络

数据链接：

https://b2find.eudat.eu/dataset/bd46cc8f-e17c-5723-8d14-b44240c49824 数据链接链接失效反馈

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The application of machine learning to the modeling of materials and molecules has proven to be extremely successful in accelerating the understanding, design, and...

应用场景：

化学机器学习

分子哈密顿量预测

The application of machine learning to the modeling of materials and molecules has proven to be extremely successful in accelerating the understanding, design, and characterization of materials. A maj

DataCite Commons2026-03-12 更新80

Generalization capabilities of translationally equivariant neural networks

深度学习理论

等变神经网络

This repository contains the datasets used in our paper "Generalization capabilities of translationally equivariant neural networks". For details see our paper and the included pdf.

NIAID Data Ecosystem40

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

量子化学计算

分子哈密顿量预测

This is the official QH9 datasets from paper 'QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules'. QH9 is a new Quantum Hamiltonian dataset providing precise Hamiltonian matrices for

Zenodo2024-03-28 更新30

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

分子动力学模拟

等变神经网络

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamic

DataCite Commons2026-03-12 更新80

Learning local equivariant representations for large-scale atomistic dynamics

原子间势能

等变神经网络

A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this g

DataCite Commons2026-03-12 更新30

Equivariant representations for molecular Hamiltonians

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