npj Computational Materials最新文献

{"title":"Ab initio mechanisms and design principles for photodesorption from TiO2","authors":"Aaron R. Altman, Felipe H. da Jornada","doi":"10.1038/s41524-025-01612-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01612-7","url":null,"abstract":"<p>Photocatalytic reactions often exhibit fast kinetics and high product selectivity, qualities difficult to achieve simultaneously in thermal processes. However, photo-driven mechanisms remain poorly understood due to challenges in realistically modeling catalysts in optically excited states. Here, we apply many-body perturbation theory (MBPT) calculations to gain insight into these mechanisms by studying a prototypical photocatalytic reaction, proton desorption from a rutile TiO₂ (110) surface. Our results reveal dramatic changes upon photoexcitation, including an over 50% reduction in the desorption energy and the emergence of an energy barrier. We rationalize these findings using a generalizable model based on Fano theory, and explain the surprising increase of excitonic effects as the proton detaches from the surface. Our model also connects the alignment of various ionization potentials to the shape of the excited-state potential energy surface. These results, not qualitatively captured by constrained density-functional theory, highlight how MBPT calculations can inform photocatalytic reaction design.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"46 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909934","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}

{"title":"Unraveling charge effects on interface reactions and dendrite growth in lithium metal anode","authors":"Genming Lai, Yunxing Zuo, Chi Fang, Zongji Huang, Taowen Chen, Qinghua Liu, Suihan Cui, Jiaxin Zheng","doi":"10.1038/s41524-025-01615-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01615-4","url":null,"abstract":"<p>Li metal is acknowledged as an ultimate anode material for high-specific-energy batteries, although its safety and practical cyclability heavily depend on the mysterious interface between Li metal and liquid electrolyte (LLI). However, there are substantial gaps in understanding the multiple intertwined chemical and electrochemical processes occurring on the LLI. Here, we unprecedentedly present the disentangled analyses of these processes and correlate them with Li dendrite growth by multi-scale simulation techniques combining machine-learning-driven molecular dynamics and phase-field modeling. Our simulations demonstrate a close relationship between Li dendrite growth and the interface reactions, which can be attributed to the charge transfer process. We further reveal that the behaviors of bond cleavages can be regulated by varying charge distribution at the interface. We propose that the charge transfer kinetics, revealed by the newly developed formulism of machine learning potential incorporating charge information, can act as a descriptor to explain the driving forces behind these behaviors on the LLI. This work enables new opportunities to fundamentally understand the intertwined processes occurring on the LLI and provide crucial new insights into the electrode-electrolyte interface design for next-generation high-specific-energy batteries.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"113 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143910620","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}

{"title":"Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction","authors":"Fei Shuang, Kai Liu, Yucheng Ji, Wei Gao, Luca Laurenti, Poulumi Dey","doi":"10.1038/s41524-025-01599-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01599-1","url":null,"abstract":"<p>Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their large characteristic scales exceed the computational limits of first-principles calculations. To address this challenge, we present a computational framework combining a defect genome constructed via empirical interatomic potential-guided sampling, with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations. The effectiveness of this approach was validated through simulations of nanoindentation, tensile deformation, and fracture in BCC tungsten. This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"23 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903120","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}

{"title":"An extensible open-source solution for research digitalisation in materials science","authors":"Victor Dudarev, Lars Banko, Alfred Ludwig","doi":"10.1038/s41524-025-01618-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01618-1","url":null,"abstract":"<p>Information technology and data science development stimulate transformation in many fields of scientific knowledge. In recent years, a large number of specialised systems for information and knowledge management have been created in materials science. However, the development and deployment of open adaptive systems for research support in materials science based on the acquisition, storage, and processing of different types of information remains unsolved. We propose <i>MatInf</i>—an extensible, open-source solution for research digitalisation in materials science based on an adaptive, flexible information management system for heterogeneous data sources. <i>MatInf</i> can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs. As an example, we demonstrate its application in high-throughput experimentation.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"97 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901465","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}

{"title":"Verification and validation of zero-point electron-phonon renormalization of the bandgap, mass enhancement, and spectral functions","authors":"Samuel Poncé, Jae-Mo Lihm, Cheol-Hwan Park","doi":"10.1038/s41524-025-01587-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01587-5","url":null,"abstract":"<p>Verification and validation of methods and first-principles software are at the core of computational solid-state physics but are too rarely addressed. We compare four first-principles codes: ABINIT, Quantum ESPRESSO, EPW, ZG, and three methods: (i) the Allen-Heine-Cardona theory using density functional perturbation theory (DFPT), (ii) the Allen-Heine-Cardona theory using Wannier function perturbation theory (WFPT), and (iii) an adiabatic non-perturbative frozen-phonon method. For these cases, we compute the real and imaginary parts of the electron-phonon self-energy in diamond and BAs, including dipoles and quadrupoles when interpolating. We find excellent agreement between software that implements the same formalism as well as good agreement between the DFPT and WFPT methods. Importantly, we find that the Deybe-Waller term is momentum dependent which impacts the mass enhancement, yielding approximate results when using the Luttinger approximations. Finally, we compare the electron-phonon spectral functions between ABINIT and EPW and find excellent agreement even away from the band edges.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"55 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901466","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}

{"title":"Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous","authors":"Weiqi Chen, Zhiyue Xu, Kang Wang, Lei Gao, Aisheng Song, Tianbao Ma","doi":"10.1038/s41524-025-01629-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01629-y","url":null,"abstract":"<p>Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects. A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications. Multiphases of hydrogenated carbon materials, from crystal to amorphous, with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions. Here, we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning, and construct a general-purpose pre-trained machine learning potential (MLP) for hydrogen-carbon systems. The pre-trained MLP is further efficiently transferred to three target spaces of deposition, friction and fracture with scale reliability. This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"93 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903122","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}

{"title":"High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks","authors":"Haoyi Tan, Yukun Teng, Guangcun Shan","doi":"10.1038/s41524-025-01617-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01617-2","url":null,"abstract":"<p>The removal of leaked radioactive iodine isotopes in humid air environments holds significant importance in nuclear waste management and nuclear accident mitigation. In this study, high-throughput computational screening and machine learning were combined to reveal the iodine capture performance of 1816 metal-organic framework (MOF) materials under humid air conditions. Initially, the relationship between the structural characteristics of MOF materials (including density, surface area and pore features) and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. Subsequently, two machine learning regression algorithms—Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOF materials. In addition to 6 structural features, 25 molecular features (encompassing the types of metal and ligand atoms as well as bonding modes) and 8 chemical features (including heat of adsorption and Henry’s coefficient) were incorporated to enhance the prediction accuracy of the machine learning algorithms. Feature importance was assessed to determine the relative influence of various features on iodine adsorption performance, in which the Henry’s coefficient and heat of adsorption to iodine were found the two most crucial chemical factors. Furthermore, four types of molecular fingerprints were introduced for providing comprehensive and detailed structural information of MOF materials. The 20 most significant Molecular ACCess Systems (MACCS) bits were picked out, revealing that the presence of six-membered ring structures and nitrogen atoms in the MOF framework were the key structural factors that enhanced iodine adsorption, followed by the presence of oxygen atoms. This work combined high-throughput computation, machine learning, and molecular fingerprints to comprehensively and systematically elucidate the multifaceted factors governing the iodine adsorption performance of MOFs in humid environments, establishing a robust and profound guideline framework for accelerating the screening and targeted design of high-performance MOF materials.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"15 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901472","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}

{"title":"Electron-mediated anharmonicity and its role in the Raman spectrum of graphene","authors":"Nina Girotto Erhardt, Aloïs Castellano, J. P. Alvarinhas Batista, Raffaello Bianco, Ivor Lončarić, Matthieu J. Verstraete, Dino Novko","doi":"10.1038/s41524-025-01610-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01610-9","url":null,"abstract":"<p>The Raman active G mode in graphene exhibits a strong coupling to electrons, yet the comprehensive treatment of this interaction in the calculation of its temperature-dependent Raman spectrum remains incomplete. In this study, we calculate the temperature dependence of the G-mode frequency and linewidth, and successfully explain the experimental trend by accounting for the contributions arising from the first-order electron-phonon coupling, electron-mediated phonon-phonon coupling, and standard lattice anharmonicity. The generality of our approach enables its broad applicability to study phonon dynamics in materials where both electron-phonon coupling and anharmonicity are important.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"43 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889482","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}

{"title":"Strain and ligand effects in the 1-D limit: reactivity of steps","authors":"Onyinyechukwu Goodness Njoku, Paige Fronczak, Kara Smeltz, Ian T. McCrum","doi":"10.1038/s41524-025-01616-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01616-3","url":null,"abstract":"<p>The predictive design of alloy (electro)catalysts is necessary to identify catalysts more active, selective, stable, and low-cost than the pure metals. Our fundamental understanding of the catalytic behavior of alloys is limited however as it is typically derived from that of flat, “pristine” surfaces, not the industrially-relevant, defect-rich surfaces found on nanoparticles. We use density functional theory (DFT) modeling to probe strain, ligand, and ensemble effects on transition metal surfaces with step-defects. We find the response of the step to strain and ligand effects is much smaller in magnitude and sometimes opposite in direction to that of a flat surface, due to the breaking of two-dimensional symmetry at the step. Insight gained from flat surfaces alone is therefore not sufficient to understand (alloy) nanoparticles; defect sites must be explicitly considered. We additionally find that the one-dimensional, bimetallic ensemble created by the selective decoration of step defects can break adsorbate scaling, yielding surface alloys with potentially enhanced catalytic performance.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"76 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876016","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}

{"title":"Photoinduced ferroelectric phase transition triggering photocatalytic water splitting","authors":"Jun Wen, Zhi-rui Luo, Lin-can Fang, Wen-xian Chen, Gui-lin Zhuang","doi":"10.1038/s41524-025-01601-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01601-w","url":null,"abstract":"<p>Utilizing two-dimensional (2D) ferroelectric semiconductors for photocatalytic water splitting (PWS) to produce clean hydrogen fuel shows promise but faces performance regulation challenges. This study employs real-time time-dependent density functional theory (rt-TDDFT) and first-principle calculations to propose a “one stone, two birds” strategy: light induces ferroelectric phase transitions and triggers PWS on monolayer Hf₂Ge₂S₆. Electronically, monolayer Hf₂Ge₂S₆ exhibits excellent stability, mechanical properties, an appropriate band gap, optimal band edge positions, and broad light absorption. Its ferroelectric (FE) phase promotes oxygen evolution reaction(OER), while the paraelectric (PE) phase enhances hydrogen evolution reaction(HER). Specifically, applying 10% compressive strain effectively suppresses OER on the FE phase, while a mere 2% tensile strain can induce complete spontaneity in HER on the PE phase. Finally, rt-TDDFT simulation results demonstrate that laser pulses can drive effective ion displacements of Ge atoms in monolayer Hf₂Ge₂S₆ and thereby generate the transition from FE to PE, which is attributed to the maintenance of charge distribution asymmetry through internal atomic electron transfers. More importantly, this recyclable ferroelectric photocatalyst, activated by light and electric fields, effectively prevents performance drawbacks from pure electric fields, demonstrating that a photoelectric alternating field can regulate PWS performance. These findings demonstrate that a photoelectric alternating field is an effective strategy to regulate photocatalytic performance for PWS.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"44 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876009","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}