npj Computational Materials最新文献_第9页

Benchmarking machine learning models for predicting lithium ion migration 预测锂离子迁移的基准机器学习模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-13 DOI: 10.1038/s41524-025-01571-z

Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy

{"title":"Benchmarking machine learning models for predicting lithium ion migration","authors":"Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy","doi":"10.1038/s41524-025-01571-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01571-z","url":null,"abstract":"The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput (HT) density functional theory (DFT) calculations of transport properties. Machine learning (ML) can accelerate HT workflows but requires high-quality data to ensure accurate predictions from trained models. In this study, we introduce the LiTraj dataset, which comprises 13,000 percolation and 122,000 migration barriers, and 1700 migration trajectories, calculated for Li-ion in diverse crystal structures using empirical force fields and DFT, respectively. With LiTraj, we demonstrate that classical ML models and graph neural networks (GNNs) for structure-to-property prediction of percolation and migration barriers can distinguish between “fast” and “poor” ionic conductors. Furthermore, we evaluate the capability of GNN-based universal ML interatomic potentials (uMLIPs) to identify optimal Li-ion migration trajectories. Fine-tuned uMLIPs achieve near-DFT accuracy in predicting migration barriers, significantly accelerating HT screenings of new ionic conductors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning 通过高效和稳健的主动学习，绘制跨分子的多态学习和间隙驱动动力学的电子态流形

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-13 DOI: 10.1038/s41524-025-01636-z

Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska, Mario Barbatti, Pavlo O. Dral

{"title":"Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning","authors":"Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska, Mario Barbatti, Pavlo O. Dral","doi":"10.1038/s41524-025-01636-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01636-z","url":null,"abstract":"We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in cis-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating magnetic transition states via string method in first principle calculation 用第一性原理计算中的弦法模拟磁跃迁态

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-12 DOI: 10.1038/s41524-025-01603-8

Wenlong Tang, Yanbo Li, Ben Xu

引用次数: 0

Multidimensional coherent spectroscopy of correlated lattice systems 相关晶格系统的多维相干光谱学

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-09 DOI: 10.1038/s41524-025-01619-0

Jiyu Chen, Philipp Werner

引用次数: 0

JAX-BTE: a GPU-accelerated differentiable solver for phonon Boltzmann transport equations JAX-BTE：声子玻尔兹曼输运方程的gpu加速可微求解器

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-09 DOI: 10.1038/s41524-025-01635-0

Wenjie Shang, Jiahang Zhou, J. P. Panda, Zhihao Xu, Yi Liu, Pan Du, Jian-Xun Wang, Tengfei Luo

引用次数: 0

Cartesian atomic moment machine learning interatomic potentials 笛卡尔原子矩机器学习原子间势

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-09 DOI: 10.1038/s41524-025-01623-4

Mingjian Wen, Wei-Fan Huang, Jin Dai, Santosh Adhikari

{"title":"Cartesian atomic moment machine learning interatomic potentials","authors":"Mingjian Wen, Wei-Fan Huang, Jin Dai, Santosh Adhikari","doi":"10.1038/s41524-025-01623-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01623-4","url":null,"abstract":"Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency. While leading MLIPs rely on representing atomic environments using spherical tensors, Cartesian representations offer potential advantages in simplicity and efficiency. Here, we introduce the Cartesian Atomic Moment Potential (CAMP), an approach to building MLIPs entirely in Cartesian space. CAMP constructs atomic moment tensors from neighboring atoms and employs tensor products to incorporate higher body-order interactions, providing a complete description of local atomic environments. Integrated into a graph neural network (GNN) framework, CAMP enables physically motivated, systematically improvable potentials. The model demonstrates excellent performance across diverse systems, including periodic structures, small organic molecules, and two-dimensional materials, achieving accuracy, efficiency, and stability in molecular dynamics simulations that rival or surpass current leading models. CAMP provides a powerful tool for atomistic simulations to accelerate materials understanding and discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"78 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143930986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learned potential for high-throughput phonon calculations of metal—organic frameworks 机器学习在金属有机框架高通量声子计算中的潜力

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-08 DOI: 10.1038/s41524-025-01611-8

Alin Marin Elena, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Andrew S. Rosen, Federica Zanca, Kristin A. Persson

{"title":"Machine learned potential for high-throughput phonon calculations of metal—organic frameworks","authors":"Alin Marin Elena, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Andrew S. Rosen, Federica Zanca, Kristin A. Persson","doi":"10.1038/s41524-025-01611-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01611-8","url":null,"abstract":"Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"16 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polarization switching of HfO2 ferroelectric in bulk and electrode/ferroelectric/electrode heterostructure HfO2铁电体和电极/铁电/电极异质结构的极化开关

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-08 DOI: 10.1038/s41524-025-01633-2

Xinpeng Mu, Yao Wu, Binjian Zeng, Jie Jiang, Yichun Zhou, Lu Yin, Min Liao, Qiong Yang

{"title":"Polarization switching of HfO2 ferroelectric in bulk and electrode/ferroelectric/electrode heterostructure","authors":"Xinpeng Mu, Yao Wu, Binjian Zeng, Jie Jiang, Yichun Zhou, Lu Yin, Min Liao, Qiong Yang","doi":"10.1038/s41524-025-01633-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01633-2","url":null,"abstract":"HfO2-based ferroelectric films are of great potential for the application of non-volatile information storage. In this paper, to understand the polarization switching properties of ferroelectric HfO2, the 180° polarization switching of HfO2 film in the uniform polarization reversal and domain evolution are studied in both bulk form and Ni/HfO2/Ni heterostructure based on the climbing image nudged elastic band (CI-NEB) simulation. It is found that the polarization reversal pathway with O atoms not shifting through the Hf-atomic planes has higher domain nucleation energy barrier due to the induced high energy domain wall (DW) but lower DW migration energy barrier, which is contrary to the pathway with O atoms shifting through the Hf-atomic planes. However, the interface effect of heterostructure considerably lower the energy barrier for the latter pathway in both uniform polarization reversal and DW migration. This indicates that both types of pathways may be possible and synergistically determine the polarization switching mechanism of HfO2 ferroelectric.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dzyaloshinskii–Moriya interaction manipulation in multiferroic Janus monolayers 多铁Janus单层中的Dzyaloshinskii-Moriya相互作用操纵

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-07 DOI: 10.1038/s41524-025-01585-7

Xuanyi Li, Zhiwen Wang, Zefeng Chen, Zhichao Yu, Changsong Xu

{"title":"Dzyaloshinskii–Moriya interaction manipulation in multiferroic Janus monolayers","authors":"Xuanyi Li, Zhiwen Wang, Zefeng Chen, Zhichao Yu, Changsong Xu","doi":"10.1038/s41524-025-01585-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01585-7","url":null,"abstract":"The discovery of two-dimensional (2D) multiferroic materials leads to significant breakthroughs in condensed matter physics. However, the role of Dzyaloshinskii–Moriya interaction (DMI) in determining the magnetic and ferroelectric orderings of 2D multiferroics remains underexplored. In this study, we employ first-principles calculation methods to reveal the multiferroic nature of Janus NiXY monolayers (X, Y = I, Br, Cl). Our analyses demonstrate that, (i) Janus NiXY magnets exhibit a cycloid helical spin ground state propagating along the 〈110〉 direction, dominantly driven by intrinsic DMI; and (ii) macroscopic ferroelectric polarizations are intertwined with spin spiral orders, indicating their type-II multiferroicity. Notably, the inclination of the spin rotation plane directly correlates with the DMI strength, which suggests adjustable electric polarization when spin-spin interactions are modulated by external electrostatic fields. Therefore, our work not only indicates the DMI manipulation for tailoring magnetic and ferroelectric ground states but also highlights the intrinsic strong magnetoelectric coupling effects in multiferroic Janus materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exhaustive screening of high-fold degenerate topological semimetal with chiral structure 具有手性结构的高折叠简并拓扑半金属的详尽筛选

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-05 DOI: 10.1038/s41524-025-01624-3

Yan Wang, Wenwen Yang, Wujun Shi, Wenjian Liu, Qiunan Xu

{"title":"Exhaustive screening of high-fold degenerate topological semimetal with chiral structure","authors":"Yan Wang, Wenwen Yang, Wujun Shi, Wenjian Liu, Qiunan Xu","doi":"10.1038/s41524-025-01624-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01624-3","url":null,"abstract":"High-fold degenerate topological semimetals (TSMs) that can host fermions with high-fold degeneracy have attracted considerable interest recently, but not many topological materials have been verified. Among them, ones with chiral structure possessing larger topological charge and extremely long Fermi arcs bring about some special properties like topological catalysis. Here, based on high-throughput calculations and density functional theory, we have built a program to search high-fold degenerate fermions automatically. A database including 146 chiral high-fold degenerate TSMs with exotic fermions near the Fermi level is established and described in detail. It contains not only the well-known CoSi family of materials, but also a sight of new TSMs originating from 14 space groups. The high-fold degenerate points in chiral structures are three-, four-, or sixfold and exist at the high symmetry k-points. Moreover, these TSMs containing high-fold degenerate fermions can also host Weyl points in chiral structures, which can also be valuable for the achievement of quantum Hall effect, quantized circular photogalvanic effect, and so on.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143910704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0