npj Computational Materials最新文献_第7页

Theory of the divacancy in 4H-SiC: impact of Jahn-Teller effect on optical properties 4H-SiC中距离理论：Jahn-Teller效应对光学性质的影响

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01609-2

Vytautas Žalandauskas, Rokas Silkinis, Lasse Vines, Lukas Razinkovas, Marianne Etzelmüller Bathen

{"title":"Theory of the divacancy in 4H-SiC: impact of Jahn-Teller effect on optical properties","authors":"Vytautas Žalandauskas, Rokas Silkinis, Lasse Vines, Lukas Razinkovas, Marianne Etzelmüller Bathen","doi":"10.1038/s41524-025-01609-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01609-2","url":null,"abstract":"Understanding the optical properties of color centers in silicon carbide is essential for their use in quantum technologies, such as single-photon emission and spin-based qubits. In this work, first-principles calculations were employed using the r2SCAN density functional to investigate the electronic and vibrational properties of neutral divacancy configurations in 4H-SiC. Our approach addresses the dynamical Jahn–Teller effect in the excited states of axial divacancies. By explicitly solving the multimode dynamical Jahn–Teller problem, we compute emission and absorption lineshapes for axial divacancy configurations, providing insights into the complex interplay between electronic and vibrational degrees of freedom. The results show strong alignment with experimental data, underscoring the predictive power of the methodologies. Our calculations predict spontaneous symmetry breaking due to the pseudo Jahn–Teller effect in the excited state of the kh divacancy, accompanied by the lowest electron–phonon coupling among the four configurations and distinct polarizability. These unique properties facilitate its selective excitation, setting it apart from other divacancy configurations, and highlight its potential utility in quantum technology applications. These findings underscore the critical role of electron–phonon interactions and optical properties in spin defects with pronounced Jahn–Teller effects, offering valuable insights for the design and integration of quantum emitters for quantum technologies.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165159","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

Scalable training of neural network potentials for complex interfaces through data augmentation 基于数据增强的复杂界面神经网络电位的可扩展训练

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01651-0

In Won Yeu, Annika Stuke, Jon López-Zorrilla, James M. Stevenson, David R. Reichman, Richard A. Friesner, Alexander Urban, Nongnuch Artrith

{"title":"Scalable training of neural network potentials for complex interfaces through data augmentation","authors":"In Won Yeu, Annika Stuke, Jon López-Zorrilla, James M. Stevenson, David R. Reichman, Richard A. Friesner, Alexander Urban, Nongnuch Artrith","doi":"10.1038/s41524-025-01651-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01651-0","url":null,"abstract":"Artificial neural network (ANN) potentials enable accurate atomistic simulations of complex materials at unprecedented scales, but training them for potential energy surfaces (PES) of diverse chemical environments remains computationally intensive, especially when the PES gradients are trained on atomic force data. Here, we present an efficient methodology incorporating forces into ANN training by translating them to synthetic energy data using Gaussian process regression (GPR), leading to accurate PES models with fewer additional first-principles calculations and a reduced computational effort for training. We evaluated the method on hybrid density-functional theory data for ethylene carbonate (EC) molecules and their interfaces with Li metal, which are relevant for Li-metal batteries. The GPR-ANN potentials achieved an accuracy comparable to fully force-trained ANN potentials with a significantly reduced computational and memory overhead, establishing the method as a powerful and scalable framework for constructing high-fidelity ANN potentials for complex materials systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"45 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165158","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

Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis 由符号回归指导的材料发现工作流程，用于识别电催化的酸稳定氧化物

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-26 DOI: 10.1038/s41524-025-01596-4

Akhil S. Nair, Lucas Foppa, Matthias Scheffler

{"title":"Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis","authors":"Akhil S. Nair, Lucas Foppa, Matthias Scheffler","doi":"10.1038/s41524-025-01596-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01596-4","url":null,"abstract":"The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are often unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic regression approach. SISSO identifies analytical expressions correlated with a given materials property. These expressions depend on a few, key physical parameters, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. We combine bootstrap sampling with Monte-Carlo dropout of primary features to obtain different datasets, which are used to train multiple SISSO models of the ensembles. The ensemble strategy improves the model performance with the feature dropout procedure alleviating the overconfidence issues observed for the widely used bagging ensemble approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials-property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136974","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

Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy 基于从头算精度的可扩展机器学习光诱导有序无序相变方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-26 DOI: 10.1038/s41524-025-01614-5

Andrea Corradini, Giovanni Marini, Matteo Calandra

{"title":"Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy","authors":"Andrea Corradini, Giovanni Marini, Matteo Calandra","doi":"10.1038/s41524-025-01614-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01614-5","url":null,"abstract":"While machine learning excels in simulating material thermal properties, its application to order-disorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing potential energy surfaces, forces, and vibrational properties in the presence of a photoexcited electron-hole plasma. Here, we present a novel approach that combines constrained density functional theory with machine learning, yielding highly reliable interatomic potentials capable of capturing electron-hole plasma effects on structural properties. Applied to photoexcited silicon, our potential accurately reproduces the phonon dispersion of the crystal phase and allows for molecular dynamics simulations of tens of thousands of atoms. We show that, at low enough temperatures, the non-thermal melting transition is driven by a soft phonon and the formation of a double-well potential, at odds with thermal melting being strictly first order. Our method paves the way to large-scale, long-time simulations of light-induced order-disorder phase transitions with ab initio accuracy.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"142 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136973","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

Unified multimodal multidomain polymer representation for property prediction 聚合物性能预测的统一多模态多畴表示

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-26 DOI: 10.1038/s41524-025-01652-z

Qi Huang, Yedi Li, Lei Zhu, Qibin Zhao, Wenjie Yu

引用次数: 0

TiSe2 is a band insulator created by lattice fluctuations, not an excitonic insulator TiSe2是由晶格波动产生的能带绝缘体，而不是激子绝缘体

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-26 DOI: 10.1038/s41524-025-01631-4

Dimitar Pashov, Ross E. Larsen, Matthew D. Watson, Swagata Acharya, Mark van Schilfgaarde

{"title":"TiSe2 is a band insulator created by lattice fluctuations, not an excitonic insulator","authors":"Dimitar Pashov, Ross E. Larsen, Matthew D. Watson, Swagata Acharya, Mark van Schilfgaarde","doi":"10.1038/s41524-025-01631-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01631-4","url":null,"abstract":"TiSe2 is a narrow-gap insulator with a rich array of unique properties. In addition to being a superconductor under certain modifications, it is commonly thought to be a rare realisation of an excitonic insulator. Below 200 K, TiSe2 undergoes a transition from a high-symmetry ((Pbar{3}m1)) phase to a low-symmetry ((Pbar{3}c1)) charge density wave (CDW). Here we establish that it is indeed an insulator in both (Pbar{3}m1) and (Pbar{3}c1) phases. However, the insulating state is driven not by excitonic effects but by symmetry-breaking. In the CDW phase it is static. At high temperature, thermally driven instantaneous deviations from (Pbar{3}m1) break the symmetry on the characteristic time scale of a phonon. Even though the time-averaged lattice structure assumes (Pbar{3}m1) symmetry, the time-averaged energy band structure is closer to the CDW phase – a rare instance of a metal-insulator transition induced by dynamical symmetry breaking. We establish these conclusions from quasiparticle self-consistent GW (QSGW) and many-body calculations (QS(Gwidehat{W})), in combination with molecular dynamics simulations to capture the effects of thermal disorder. The many-body theory includes explicitly ladder diagrams in the polarizability, which incorporates excitonic effects in an ab initio manner. We find that the excitonic modification to the potential is weak, ruling out the possibility that TiSe2 is an excitonic insulator.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145975","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

Orbital-free density functionals based on real and reciprocal space separation 基于实空间分离和互反空间分离的无轨道密度泛函

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-24 DOI: 10.1038/s41524-025-01643-0

Bishal Thapa, Tracey G. Oellerich, Maria Emelianenko, Phanish Suryanarayana, Igor I. Mazin

引用次数: 0

Inverse design of catalytic active sites via interpretable topology-based deep generative models 基于可解释拓扑的深度生成模型的催化活性位点逆设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-24 DOI: 10.1038/s41524-025-01649-8

Bingxu Wang, Shisheng Zheng, Jie Wu, Jingyan Li, Feng Pan

{"title":"Inverse design of catalytic active sites via interpretable topology-based deep generative models","authors":"Bingxu Wang, Shisheng Zheng, Jie Wu, Jingyan Li, Feng Pan","doi":"10.1038/s41524-025-01649-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01649-8","url":null,"abstract":"The rational design of catalyst structures tailored to target performance is an ambitious and profoundly impactful goal. Key challenges include achieving refined representations of the three-dimensional structure of active sites and imbuing models with robust physical interpretability. Herein, we developed a topology-based variational autoencoder framework (PGH-VAEs) to enable the interpretable inverse design of catalytic active sites. Leveraging high-entropy alloys as a case, we demonstrate that persistent GLMY homology, an advanced topological algebraic analysis tool, enables the quantification of three-dimensional structural sensitivity and establishes correlations with adsorption properties. The multi-channel PGH-VAEs illustrate how coordination and ligand effects shape the latent space and influence the adsorption energies. Building on the inverse design results from PGH-VAEs, the strategies to optimize the composition and facet structures to maximize the proportion of optimal active sites are proposed. This interpretable inverse design framework can be extended to diverse systems, paving the way for AI-driven catalyst design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"131 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130244","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

Probabilistic phase labeling and lattice refinement for autonomous materials research 自主材料研究中的概率相位标记和晶格细化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-24 DOI: 10.1038/s41524-025-01627-0

Ming-Chiang Chang, Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Lan Zhou, John M. Gregoire, Carla P. Gomes, R. Bruce van Dover, Michael O. Thompson

{"title":"Probabilistic phase labeling and lattice refinement for autonomous materials research","authors":"Ming-Chiang Chang, Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Lan Zhou, John M. Gregoire, Carla P. Gomes, R. Bruce van Dover, Michael O. Thompson","doi":"10.1038/s41524-025-01627-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01627-0","url":null,"abstract":"X-ray diffraction (XRD) is a powerful method for determining a material’s crystal structure in high-throughput experimentation, and is widely being incorporated in artificially intelligent agents for autonomous scientific discovery. However, rapid, automated, and reliable analysis of XRD data at rates that match the pace of experimental measurements at a synchrotron source remains a major challenge. To address these issues, we developed CrystalShift for rapid and efficient probabilistic XRD phase labeling employing symmetry-constrained optimization, best-first tree search, and Bayesian model comparison. The algorithm estimates probabilities for phase combinations without requiring additional phase space information or training. We demonstrate that CrystalShift provides robust probability estimates, outperforming existing methods on synthetic and experimental datasets, and can be readily integrated into high-throughput experimental workflows. In addition to efficient phase labeling, CrystalShift offers quantitative insights into materials’ structural parameters, which facilitate both expert evaluation and AI-based modeling of the phase space, ultimately accelerating materials identification and discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"39 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130434","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

Scaling Law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions Sim2Real迁移学习在扩展计算材料数据库中用于现实世界预测的缩放定律

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-24 DOI: 10.1038/s41524-025-01606-5

Shunya Minami, Yoshihiro Hayashi, Stephen Wu, Kenji Fukumizu, Hiroki Sugisawa, Masashi Ishii, Isao Kuwajima, Kazuya Shiratori, Ryo Yoshida

{"title":"Scaling Law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions","authors":"Shunya Minami, Yoshihiro Hayashi, Stephen Wu, Kenji Fukumizu, Hiroki Sugisawa, Masashi Ishii, Isao Kuwajima, Kazuya Shiratori, Ryo Yoshida","doi":"10.1038/s41524-025-01606-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01606-5","url":null,"abstract":"To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations. Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch. This study demonstrates the scaling law of simulation-to-real (Sim2Real) transfer learning for several machine learning tasks in materials science. Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases. Observing the scaling behavior offers various insights for database development, such as determining the sample size necessary to achieve a desired performance, identifying equivalent sample sizes for physical and computational experiments, and guiding the design of data production protocols for downstream real-world tasks.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"8 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130437","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