Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium [0.03%]
基于热力学的自动数据库生成及在镁合金中的应用
Vincent G Fletcher,Albert P Bartók,Livia B Pártay
Vincent G Fletcher
We propose a novel approach for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely appealin...
'Interaction annealing' to determine effective quantized valence and orbital structure: an illustration with ferro-orbital order in WTe2 [0.03%]
相互作用退火确定有效量子化价电子和轨道结构：WTe2中铁电轨道有序的示例演示
Ruoshi Jiang,Fangyuan Gu,Wei Ku
Ruoshi Jiang
Correlated materials are known to display qualitatively distinct emergent behaviors at low energy. Conveniently, upon absorbing rapid quantum fluctuations, these rich low-energy behaviors can always be effectively described by dressed parti...
Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective [0.03%]
基于对称性规范的缺陷掺杂一维系统从头算热输运性质研究
Yu-Jie Cen,Sandro Wieser,Georg K H Madsen et al.
Yu-Jie Cen et al.
Nanotubes, with their high aspect ratio and tunable thermal conductivities, are promising nanoscale heat-management components. However, their performance is often constrained by thermal resistance arising from structural defects or interfa...
Free-energy perturbation in the exchange-correlation space accelerated by machine learning: application to silica polymorphs [0.03%]
机器学习加速的交换相关空间中的自由能扰动计算及其在石英多型体研究中的应用
Axel Forslund,Jong Hyun Jung,Yuji Ikeda et al.
Axel Forslund et al.
We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob's ladder. We apply the approach to the dynamically stabilized phas...
Miguel Angel Moreno-Mateos,Paul Steinmann
Miguel Angel Moreno-Mateos
Cutting soft materials is a complex process governed by the interplay of bulk large deformation, interfacial soft fracture, and contact forces with the cutting tool. Existing experimental characterizations and numerical models often fail to...
MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling [0.03%]
MOFSimBench：评估金属有机框架分子建模中的通用机器学习原子间势函数
Hendrik Kraß,Ju Huang,Seyed Mohamad Moosavi
Hendrik Kraß
Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their reliabili...
Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters [0.03%]
平面波DFT的算法微分：材料设计、误差控制和学习模型参数
Niklas Frederik Schmitz,Bruno Ploumhans,Michael F Herbst
Niklas Frederik Schmitz
We present a differentiation framework for plane-wave density-functional theory (DFT) that combines the strengths of forward-mode algorithmic differentiation (AD) and density-functional perturbation theory (DFPT). In the resulting AD-DFPT f...
A configuration interaction approach to solve the Anderson impurity model; applications to elemental Ce [0.03%]
解决安德森杂质模型的配置相互作用方法及在元素铈上的应用
B Herzog,P Thunström,O Eriksson
B Herzog
Accurate calculations of strongly correlated materials remain a formidable challenge in condensed matter physics, particularly due to the computational demand of conventional methods. This paper presents an efficient solver for dynamical me...
From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons [0.03%]
从生物质废物到CO2捕获：用于活性炭高通量筛选的多保真机器学习工作流程
Faezeh Hajiali,Naoko Ellis,Bhushan Gopaluni
Faezeh Hajiali
Rising atmospheric CO2 levels threaten climate stability, demanding transformative solutions in carbon capture, utilization, and storage. Porous activated carbons (ACs) derived from sustainable waste sources offer a promising route for cost...
Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement [0.03%]
基于投影性的磁化和自旋轨道耦合的健壮Wannier化方法
Yuhao Jiang,Junfeng Qiao,Nataliya Paulish et al.
Yuhao Jiang et al.
Maximally-localized Wannier functions (MLWFs) are widely employed as an essential tool for calculating the physical properties of materials due to their localized nature and computational efficiency. Projectability-disentangled Wannier func...