Elucidating oxide-ion and proton transport in ionic conductors using machine learning potentials [0.03%]
利用机器学习电位阐明离子导体中的氧化物阴离子和质子输运
Ying Zhou,Sacha Fop,Abbie C Mclaughlin et al.
Ying Zhou et al.
The design and understanding of oxide-ion and proton transport in solid electrolytes are pivotal to the development of fuel cells that can operate at reduced temperatures of
Leonid V Pourovskii,Alena Vishina,Olle Eriksson et al.
Leonid V Pourovskii et al.
Elemental Pr metal is unique among rare-earth elements in featuring a localized partially filled 4f shell without ordered magnetism. Experimental evidence attributes this absence of magnetism to a singlet crystal-field (CF) ground state of ...
Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction [0.03%]
利用增强型机器学习原子间势有效预测红外光谱
Nitik Bhatia,Patrick Rinke,Ondřej Krejčí
Nitik Bhatia
Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However, interpreting IR spectra often requires high-...
Ineffectiveness of formamidine in suppressing ultralow thermal conductivity in cubic hybrid perovskite FAPbI3 [0.03%]
formamidinium在立方混合钙钛矿FAPbI3中抑制超低热导率的无效性研究
Jiongzhi Zheng,Zheng Chang,Changpeng Lin et al.
Jiongzhi Zheng et al.
Understanding lattice dynamics and thermal transport mechanisms in cubic hybrid organic-inorganic perovskites remain challenging due to strong anharmonicity and phase transitions. Here, we investigate the thermal transport behavior in bench...
Infrared markers of topological phase transitions in quantum spin Hall insulators [0.03%]
量子自旋霍尔绝缘体拓扑相变的红外信号
Paolo Fachin,Francesco Macheda,Paolo Barone et al.
Paolo Fachin et al.
Using first principles techniques, we show that infrared optical response allows us to discriminate between the topological and the trivial phases of 2D quantum spin Hall insulators (QSHI). We showcase germanene and jacutingaite, of recent ...
Electric-field driven nuclear dynamics of liquids and solids from a multi-valued machine-learned dipolar model [0.03%]
基于多值机器学习偶极模型的电场驱动液体和固体核动力学现象研究
Elia Stocco,Christian Carbogno,Mariana Rossi
Elia Stocco
The driving of vibrational motion by external electric fields is a topic of continued interest, due to the possibility of assessing new or metastable material phases with desirable properties. Here, we combine ab initio molecular dynamics w...
Fast and Fourier features for transfer learning of interatomic potentials [0.03%]
傅立叶特征的快速计算在转移学习中应用
Pietro Novelli,Giacomo Meanti,Pedro J Buigues et al.
Pietro Novelli et al.
Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To this effect, we introduce franken, a scalable and lightweight t...
Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation [0.03%]
利用自监督学习方法对未标注的SEM数据进行增强粒子分割
Luca Rettenberger,Nathan J Szymanski,Andrea Giunto et al.
Luca Rettenberger et al.
Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In pa...
Learning non-local molecular interactions via equivariant local representations and charge equilibration [0.03%]
基于等变局部表示和电荷平衡的非局部分子相互作用学习方法
Paul Fuchs,Michał Sanocki,Julija Zavadlav
Paul Fuchs
Graph Neural Network (GNN) potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs. Message-passing GNNs model interactions beyond their immediate neighborhood by propagati...
Duo Zhang,Xinzijian Liu,Xiangyu Zhang et al.
Duo Zhang et al.
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duratio...