Neural-network Density Functional Theory Based on Variational Energy Minimization
Deep-learning density functional theory (DFT) shows great promise to significantly
accelerate material discovery and potentially revolutionize materials research. However …
accelerate material discovery and potentially revolutionize materials research. However …
Interatomic Interaction Models for Magnetic Materials: Recent Advances
TS Kostiuchenko, AV Shapeev… - Chinese Physics …, 2024 - iopscience.iop.org
Atomistic modeling is a widely employed theoretical method of computational materials
science. It has found particular utility in the study of magnetic materials. Initially, magnetic …
science. It has found particular utility in the study of magnetic materials. Initially, magnetic …
Improving density matrix electronic structure method by deep learning
The combination of deep learning and ab initio materials calculations is emerging as a
trending frontier of materials science research, with deep-learning density functional theory …
trending frontier of materials science research, with deep-learning density functional theory …
[HTML][HTML] Universal materials model of deep-learning density functional theory Hamiltonian
Realizing large materials models has emerged as a critical endeavor for materials research
in the new era of artificial intelligence, but how to achieve this fantastic and challenging …
in the new era of artificial intelligence, but how to achieve this fantastic and challenging …
Core structure of dislocations in ordered ferromagnetic FeCo
We elucidated the core structure of screw dislocations in ordered B2 FeCo using a recent
magnetic bond-order potential (BOP)[Egorov, Phys. Rev. Mater. 7, 044403 (2023) 2475 …
magnetic bond-order potential (BOP)[Egorov, Phys. Rev. Mater. 7, 044403 (2023) 2475 …
Deep learning density functional theory Hamiltonian in real space
Deep learning electronic structures from ab initio calculations holds great potential to
revolutionize computational materials studies. While existing methods proved success in …
revolutionize computational materials studies. While existing methods proved success in …
Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials
AS Kotykhov, K Gubaev, V Sotskov… - arXiv preprint arXiv …, 2024 - arxiv.org
We propose a novel method for fitting machine-learning interatomic potentials with magnetic
degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature …
degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature …
SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning
K Ueno, S Ohuchi, K Ichikawa, K Amii… - arXiv preprint arXiv …, 2024 - arxiv.org
Neural Network Potentials (NNPs) have attracted significant attention as a method for
accelerating density functional theory (DFT) calculations. However, conventional NNP …
accelerating density functional theory (DFT) calculations. However, conventional NNP …
深度学习与第一性原理计算
李贺， 段文晖， 徐勇 - 物理, 2024 - wuli.iphy.ac.cn
第一性原理计算基于量子力学基本原理, 通过求解复杂的多电子相互作用问题实现高精度材料
计算预测, 已成为现代物理学, 化学, 材料科学等诸多领域中不可或缺的研究手段. 然而 …
计算预测, 已成为现代物理学, 化学, 材料科学等诸多领域中不可或缺的研究手段. 然而 …
Designing Two-Dimensional Materials With Novel Spin Degrees of Freedom
P Minch - 2024 - search.proquest.com
The central aim of this thesis is to explore the interplay between reduced dimensions in two-
dimensional (2D) materials and magnetism. How reduced dimensionality of 2D materials …
dimensional (2D) materials and magnetism. How reduced dimensionality of 2D materials …