npj Computational Materials最新文献_第10页

Real-time time-dependent density functional theory study of optical Kerr nonlinearities in semiconductors and metals 半导体和金属光学克尔非线性的实时时变密度泛函理论研究

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-08 DOI: 10.1038/s41524-026-02237-0

Xiao Chen, Nicolas Tancogne-Dejean, Jingyin Huang, Silvana Botti

{"title":"Real-time time-dependent density functional theory study of optical Kerr nonlinearities in semiconductors and metals","authors":"Xiao Chen, Nicolas Tancogne-Dejean, Jingyin Huang, Silvana Botti","doi":"10.1038/s41524-026-02237-0","DOIUrl":"https://doi.org/10.1038/s41524-026-02237-0","url":null,"abstract":"We employ real-time time-dependent density functional theory (rt-TDDFT) to calculate third-order nonlinear optical properties in solids, focusing on the optical Kerr effect: the Kerr coefficient <italic>n</italic>2, the two-photon absorption coefficient <italic>β</italic>, and the Kerr-type third-order susceptibility <italic>χ</italic>(3)(− <italic>ω</italic>∣<italic>ω</italic>, <italic>ω</italic>, − <italic>ω</italic>). We consider centrosymmetric bulk materials, including semiconductors (diamond, silicon) and metals (gold, iridium), and benchmark our results against available experimental data. For semiconductors, we find good agreement with experiment and show that the nonlinear response is robust with respect to pulse duration for photon energies below the bandgap. For metals, we demonstrate that ∣<italic>χ</italic>(3)∣ grows monotonically with pulse duration, consistent with experimental observations, and attribute this behavior to delayed contributions from hot-electron dynamics. We further show that thermal effects in high-repetition-rate experiments can lead to significant overestimation of the measured nonlinearities. Our work establishes rt-TDDFT as a reliable first-principles framework for modeling nonlinear optical phenomena across diverse material classes, including metals, going beyond perturbative approaches by naturally capturing intraband dynamics and pulse-duration effects.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"40 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148687870","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 learning force field molecular dynamics simulation of SEI formation on lithium metal 金属锂上SEI形成的机器学习力场分子动力学模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-07 DOI: 10.1038/s41524-026-02271-y

Norio Takenaka, Taiga Iwata, Théophane Bernhard, Atsuo Yamada

{"title":"Machine learning force field molecular dynamics simulation of SEI formation on lithium metal","authors":"Norio Takenaka, Taiga Iwata, Théophane Bernhard, Atsuo Yamada","doi":"10.1038/s41524-026-02271-y","DOIUrl":"https://doi.org/10.1038/s41524-026-02271-y","url":null,"abstract":"Lithium metal batteries represent a cornerstone technology for next-generation energy storage systems owing to their exceptional energy density. However, the extreme reactivity of lithium metal drives continuous parasitic reactions with electrolytes, causing interfacial instability and dendritic growth that severely hinder practical deployment. Formation of a protective solid electrolyte interphase (SEI) through electrolyte decomposition is essential for stabilizing the lithium metal surface, yet the underlying mechanisms remain poorly understood due to the scarcity of experimental techniques capable of accurately probing highly reducing interfacial environments. Here we investigate the early-stage formation processes of SEI on lithium metal at the atomistic scale using molecular dynamics simulations based on a machine learning force field trained on first-principles calculations. Furthermore, an automatic reaction identification program developed in this work enables systematic extraction of key kinetic information such as the identification, sequence, and probability of elementary reactions, as well as the formation rates of decomposition products. Typical experimental observations on the characteristic SEI structure, consisting of an inner oxide-rich layer and an outer fluorinated layer, were successfully reproduced. This simulation methodology provides an effective framework for analyzing the initial stage of SEI formation and offers useful insights for the design of stable lithium metal batteries.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148693471","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

Plasmon assisted superconductivity in LiTi2O4 等离子体激元辅助li2o4的超导性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-07 DOI: 10.1038/s41524-026-02254-z

Francesco Petocchi, Viktor Christiansson, Ryotaro Arita, Philipp Werner

引用次数: 0

Optimized control protocols for stable skyrmion creation using deep reinforcement learning 优化控制协议稳定skyrmion创建使用深度强化学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-07 DOI: 10.1038/s41524-026-02270-z

Ji Seok Song, Se Kwon Kim, Kyoung-Min Kim

引用次数: 0

Machine learning force field molecular dynamics simulation of interfacial reactions on platinum in concentrated aqueous electrolytes 浓缩水溶液中铂界面反应的机器学习力场分子动力学模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-06 DOI: 10.1038/s41524-026-02260-1

Hao Yu, Norio Takenaka, Atsuo Yamada

{"title":"Machine learning force field molecular dynamics simulation of interfacial reactions on platinum in concentrated aqueous electrolytes","authors":"Hao Yu, Norio Takenaka, Atsuo Yamada","doi":"10.1038/s41524-026-02260-1","DOIUrl":"https://doi.org/10.1038/s41524-026-02260-1","url":null,"abstract":"Aqueous lithium-ion batteries provide a promising route toward safe and sustainable energy storage, yet their energy density is constrained by the narrow electrochemical stability window of aqueous electrolytes. Although high salt concentrations can kinetically extend the cathodic limit of the electrolyte via anion-derived interphases, this effect depends strongly on the electrode material and is absent on platinum. To elucidate the interfacial reaction mechanisms behind the absence of cathodic-limit extension on platinum, we performed molecular dynamics simulations based on a machine learning force field, combining near first-principles accuracy with large-scale configurational sampling. The simulations reveal a dominant pathway for anion decomposition on a hydrogen-covered platinum surface under cathodic conditions, mediated by adsorbed hydrogen (H*). However, this pathway directly competes with hydrogen evolution via the Tafel step (2H* → H2) at comparable activation barriers, suppressing the formation of stable anion-derived interphases and the extension of the cathodic limit. These findings reveal interfacial H* coverage as a key factor governing electrode-dependent cathodic stabilization in concentrated aqueous electrolytes.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"56 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148693591","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

Disorder-induced magnetoelastic behavior of MnTexSbyBi1-x-y alloys MnTexSbyBi1-x-y合金的无序诱导磁弹性行为

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-06 DOI: 10.1038/s41524-026-02206-7

P. R. Raghuvanshi, X. Li, T. Berlijn, A. Ghosh, D. S. Parker, V. R. Cooper, L. Lindsay

引用次数: 0

IGlasGAN: an inverse design framework for inorganic glass materials based on generative artificial intelligence IGlasGAN：基于生成人工智能的无机玻璃材料逆向设计框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-05 DOI: 10.1038/s41524-026-02273-w

Jing Tian, Jijie Zheng, Bentao Zhang, Zhiqiang Cao, Xin Cao, Chong Zhang, Yong Liu, Gaorong Han, Shou Peng

{"title":"IGlasGAN: an inverse design framework for inorganic glass materials based on generative artificial intelligence","authors":"Jing Tian, Jijie Zheng, Bentao Zhang, Zhiqiang Cao, Xin Cao, Chong Zhang, Yong Liu, Gaorong Han, Shou Peng","doi":"10.1038/s41524-026-02273-w","DOIUrl":"https://doi.org/10.1038/s41524-026-02273-w","url":null,"abstract":"Efficient multi-objective collaborative design is a core scientific challenge and a primary obstacle to the development of novel glass materials. Here, we propose IGlasGAN (Inorganic Glass Generative Adversarial Network), a generative artificial intelligence framework for the automatic inverse design of inorganic glass materials subject to box constraints on multiple coupled properties, alongside a full generation-evaluation-validation pipeline. Built upon an improved WGAN-GP-CP architecture, the framework incorporates a property navigation mechanism and an extended normalization strategy to enable end-to-end, on-demand design, from property requirements to glass compositions, across a broad design space. By training on a dataset comprising 2445 experimental samples, in which 156 samples fully satisfy the four property requirements, including coefficient of thermal expansion (CTE), Young’s modulus (E), strain point temperature (Tst), and density (ρ), a multi-objective inverse design for the OLED substrate glass utilizing IGlasGAN was carried out, and novel compositions with relatively high Y₂O₃ content was identified. Two additional design-from-scratch scenarios with progressively more distant target regions from the training data distribution were constructed to further validate the framework’s generation capability. Across the three scenarios, 12 generated samples were experimentally characterized. The measured properties agreed well with the generated values, with deviations within ±3.50%, except for CTE, which showed a maximum deviation of 13.03%. These results confirm the applicability of the IGlasGAN framework across diverse target settings, providing an efficient intelligent pathway for multi-objective inverse design of inorganic oxide glasses.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148693491","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

A ReaxFF study of the covalency-driven cracking-to-shearing transition in metal-metalloid glasses 金属-类金属玻璃中共价驱动的裂纹-剪切转变的ReaxFF研究

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-05 DOI: 10.1038/s41524-026-02252-1

Shan-Si Liao, Xi-Lei Bian, Bo Huang, Gang Wang, Kai-Kai Song, Jürgen Eckert, Daniel Şopu

{"title":"A ReaxFF study of the covalency-driven cracking-to-shearing transition in metal-metalloid glasses","authors":"Shan-Si Liao, Xi-Lei Bian, Bo Huang, Gang Wang, Kai-Kai Song, Jürgen Eckert, Daniel Şopu","doi":"10.1038/s41524-026-02252-1","DOIUrl":"https://doi.org/10.1038/s41524-026-02252-1","url":null,"abstract":"Revealing the atomistic origins of failure in amorphous solids remains challenging. In metal-metalloid glasses, the mechanical response is strongly governed by the breaking and reformation of covalent bonds, which cannot be adequately captured by non-reactive interatomic potentials. In this work, large-scale reactive molecular dynamics simulations using a ReaxFF potential that includes angular constraints for covalent bonding reveal a composition-dependent transition from cleavage cracking to shear banding in model Cu–Si glasses. During uniaxial tension, a highly connected, rigid Si-rich network suppresses strain dispersion, generating highly localized unstable regions characterized by shear-induced reduction in atomic number density and severe distortion of Si–Si–Si bond angles. These unstable regions serve as energetically favorable pathways along which the crack advances. As the Cu content increases, reduced angular rigidity and the enhanced connectivity of flexible Cu-centered polyhedra promote widespread shear transformation and shear band-mediated plasticity. In contrast, a modified embedded-atom method potential, which intrinsically overestimates the angular flexibility of the Si-rich structures, fails to reproduce this transition. Our results suggest that angular rigidity and its spatial heterogeneity are key descriptors of plastic deformation in metal-metalloid glasses.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"694 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148693490","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

Robust crystal property prediction from unrelaxed structures via expert-guided coordinate refinement 通过专家指导的坐标精化，对非松弛结构进行稳健的晶体性质预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-05 DOI: 10.1038/s41524-026-02269-6

Congsheng Xu, Gang Guo

{"title":"Robust crystal property prediction from unrelaxed structures via expert-guided coordinate refinement","authors":"Congsheng Xu, Gang Guo","doi":"10.1038/s41524-026-02269-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02269-6","url":null,"abstract":"Graph neural networks are efficient surrogates for materials property prediction, but their reliance on computationally expensive relaxed geometries creates a major bottleneck in high-throughput screening. Here, we present RUGE-Net, an equivariant graph neural network that predicts properties directly from unrelaxed crystal structures. RUGE-Net integrates a Mixture-of-Experts (MoE) architecture, an iterative coordinate refinement module that progressively adjusts atomic positions to mitigate structural noise, and a perturbation-augmented training strategy. On the JARVIS database, RUGE-Net achieves competitive accuracy across multiple thermodynamic properties including formation and total energies, and maintains stable prediction performance under significant coordinate perturbations. We demonstrate that coordinate refinement and perturbation training are mutually essential to prevent catastrophic error amplification. Furthermore, the MoE gating network spontaneously develops interpretable element-specific specialization, routing covalent and ionic elements through distinct experts without explicit chemical supervision. By preserving near-perfect material ranking fidelity (Spearman ρ > 0.99) on noisy inputs, our approach enables rapid and reliable computational materials discovery directly from unrelaxed candidate structures.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"55 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148693493","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

CliffordIP: Clifford algebra equivariant interatomic potentials for heterogeneous catalysis 非均相催化的Clifford代数等变原子间势

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-04 DOI: 10.1038/s41524-026-02259-8

Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban

{"title":"CliffordIP: Clifford algebra equivariant interatomic potentials for heterogeneous catalysis","authors":"Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban","doi":"10.1038/s41524-026-02259-8","DOIUrl":"https://doi.org/10.1038/s41524-026-02259-8","url":null,"abstract":"Decarbonizing the global economy requires efficient catalysts for key electrochemical transformations, including CO2 reduction, nitrogen reduction, and C–C coupling. Yet systematic catalyst discovery is limited by the cost of density functional theory calculations needed to evaluate adsorbate–surface energetics across chemically diverse surfaces. Machine-learning interatomic potentials (MLIPs) can accelerate this workflow, but many leading equivariant architectures rely on spherical harmonics and Clebsch–Gordan tensor products, increasing computational complexity and implementation overhead. Here we introduce CliffordIP, a message-passing interatomic potential built on the Clifford algebra Cl(3, 0), representing atomic environments as 8-dimensional multivectors (scalars, vectors, bivectors, and pseudoscalars) coupled through the geometric product. This naturally captures bond directions, reaction planes, and local chirality relevant to catalytic intermediates, while enforcing full O(3) equivariance, including reflections, via the Pin(3) group. On catalysis-focused adsorbate subsets, CliffordIP reduces energy MAE by 20–24% relative to the next-best baseline on CO2RR, N2RR, and C2 formation, and achieves the strongest in-distribution energy performance among the compared baselines on OC20 and OC22 under a unified, compute-constrained protocol, while remaining competitive in force magnitude. These results establish Clifford algebra as a promising foundation for energy-focused MLIPs in computational catalysis.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"44 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148693528","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