npj Computational Materials最新文献_第10页

Transition state structure detection with machine learningś 基于机器学习的过渡状态结构检测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01693-4

Yitao Si, Yiding Ma, Tao Yu, Yifan Wu, Yingzhe Liu, Weipeng Lai, Zhixiang Zhang, Jinwen Shi, Liejin Guo, Oleg V. Prezhdo, Maochang Liu

{"title":"Transition state structure detection with machine learningś","authors":"Yitao Si, Yiding Ma, Tao Yu, Yifan Wu, Yingzhe Liu, Weipeng Lai, Zhixiang Zhang, Jinwen Shi, Liejin Guo, Oleg V. Prezhdo, Maochang Liu","doi":"10.1038/s41524-025-01693-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01693-4","url":null,"abstract":"Transition structure calculations via quantum chemistry methods have become a staple in modern chemical reaction research. Yet, success rates in optimizing transition structures rely heavily on rational initial guesses and expert supervision. We develop a machine learning approach that utilizes a bitmap representation of chemical structures to generate high-quality initial guesses for modeling transition states of chemical reactions. The core of the approach comprises a convolutional neural network methodology with a genetic algorithm. An extensive dataset derived from quantum chemistry computations is built, providing sufficient data on which the model can be trained, validated and tested. By applying the method to typical bi-molecular hydrogen abstraction reactions involving hydrofluorocarbons, hydrofluoroethers, and hydroxyl radicals—reactions critical in atmospheric fluoride degradation and global warming potential evaluation, yet extremely challenging to model, we achieve transition state optimizations with an impressive, verified success rate of 81.8% for hydrofluorocarbons and 80.9% for hydrofluoroethers. The reported work demonstrates the effectiveness of employing visual representation in chemical space exploration tasks and opens new avenues for the transition structure modeling.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533956","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 machine learning approach to designing and understanding tough, degradable polyamides 一种设计和理解坚韧、可降解聚酰胺的机器学习方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01696-1

Yoshifumi Amamoto, Chie Koganemaru, Ken Kojio, Atsushi Takahara, Sayoko Yamamoto, Kazuki Okazawa, Yuta Tsuji, Toshimitsu Aritake, Kei Terayama

{"title":"A machine learning approach to designing and understanding tough, degradable polyamides","authors":"Yoshifumi Amamoto, Chie Koganemaru, Ken Kojio, Atsushi Takahara, Sayoko Yamamoto, Kazuki Okazawa, Yuta Tsuji, Toshimitsu Aritake, Kei Terayama","doi":"10.1038/s41524-025-01696-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01696-1","url":null,"abstract":"The development of environmentally friendly plastics has received renewed attention for a sustainable society. Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers, the design of biodegradable polymers to overcome these issues is often difficult. In this study, we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 and α-amino acid segments that are mechanically tough and degradable. Multi-objective optimization based on Gaussian process regression for the degradation rate, strain at break, and Young’s modulus (the last two parameters correspond to toughness) suggested appropriate α-amino acid sequences for polyamides endowed with both properties. Ridge regression revealed that the physical factors associated with the sequences, as well as the higher-order multiblock-derived structures (such as the crystal lattice structure, melting points, and hydrogen bonding), were essential for endowing these polymers with satisfactory properties among the multimodal measurement/calculation data. Our method provides a useful approach for designing and understanding environment-friendly plastics and other materials with multiple properties based on machine learning techniques.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"19 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533951","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

Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling 用可微分渲染和生成建模解决纳米材料分割网络中的数据稀缺性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01702-6

Dennis Possart, Leonid Mill, Florian Vollnhals, Tor Hildebrand, Peter Suter, Mathis Hoffmann, Jonas Utz, Daniel Augsburger, Mareike Thies, Mingxuan Gu, Fabian Wagner, George Sarau, Silke Christiansen, Katharina Breininger

{"title":"Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling","authors":"Dennis Possart, Leonid Mill, Florian Vollnhals, Tor Hildebrand, Peter Suter, Mathis Hoffmann, Jonas Utz, Daniel Augsburger, Mareike Thies, Mingxuan Gu, Fabian Wagner, George Sarau, Silke Christiansen, Katharina Breininger","doi":"10.1038/s41524-025-01702-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01702-6","url":null,"abstract":"Nanomaterials’ properties, influenced by size, shape, and surface characteristics, are crucial for their technological, biological, and environmental applications. Accurate quantification of these materials is essential for advancing research. Deep learning segmentation networks offer precise, automated analysis, but their effectiveness depends on representative annotated datasets, which are difficult to obtain due to the high cost and manual effort required for imaging and annotation. To address this, we present DiffRenderGAN, a generative model that produces annotated synthetic data by integrating a differentiable renderer into a Generative Adversarial Network (GAN) framework. DiffRenderGAN optimizes rendering parameters to produce realistic, annotated images from non-annotated real microscopy images, reducing manual effort and improving segmentation performance compared to existing methods. Tested on ion and electron microscopy datasets, including titanium dioxide (TiO2), silicon dioxide (SiO2), and silver nanowires (AgNW), DiffRenderGAN bridges the gap between synthetic and real data, advancing the quantification and understanding of complex nanomaterial systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"47 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534060","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

Agent-based multimodal information extraction for nanomaterials 基于agent的纳米材料多模态信息提取

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-23 DOI: 10.1038/s41524-025-01674-7

R. Odobesku, K. Romanova, S. Mirzaeva, O. Zagorulko, R. Sim, R. Khakimullin, J. Razlivina, A. Dmitrenko, V. Vinogradov

{"title":"Agent-based multimodal information extraction for nanomaterials","authors":"R. Odobesku, K. Romanova, S. Mirzaeva, O. Zagorulko, R. Sim, R. Khakimullin, J. Razlivina, A. Dmitrenko, V. Vinogradov","doi":"10.1038/s41524-025-01674-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01674-7","url":null,"abstract":"Automating structured data extraction from scientific literature is a critical challenge with broad implications across domains. We introduce nanoMINER, a multi-agent system combining large language models and multimodal analysis to extract essential information from scientific research articles on nanomaterials. This system processes documents end-to-end, utilizing tools such as YOLO for visual data extraction and GPT-4o for linking textual and visual information. At its core, the ReAct agent orchestrates specialized agents to ensure comprehensive data extraction. We demonstrate the efficacy of the system by automating the assembly of nanomaterial and nanozyme datasets previously manually curated by domain experts. NanoMINER achieves high precision in extracting nanomaterial properties like chemical formulas, crystal systems, and surface characteristics. For nanozymes, we obtain near-perfect precision (0.98) for kinetic parameters and essential features such as Cmin and Cmax. To benchmark the system performance, we also compare nanoMINER to several baseline LLMs, including the most recent multimodal GPT-4.1, and show consistently higher extraction precision and recall. Our approach is extensible to other domains of materials science and fields like biomedicine, advancing data-driven research methodologies and automated knowledge extraction.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144341315","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 revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene 机器学习揭示了双角无序扭曲多层石墨烯中强声子局域化的巨大热导率降低

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-23 DOI: 10.1038/s41524-025-01678-3

Jingwen Wang, Zheng Zhu, Tianran Jiang, Ke Chen

{"title":"Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene","authors":"Jingwen Wang, Zheng Zhu, Tianran Jiang, Ke Chen","doi":"10.1038/s41524-025-01678-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01678-3","url":null,"abstract":"In two-dimensional (2D) layer-stacked materials, the twist angle between layers provides extensive freedom to explore novel physics and engineer remarkable thermal transport properties. We discovered that the cross-plane thermal conductivity of multilayer graphene can be effectively controlled by arranging the layers with two specific twist angles in a defined sequence. Disorderly aperiodic twisted graphene layers lead to the localization of phonons, substantially reducing the cross-plane thermal transport via the interference of coherent phonons. We employed non-equilibrium molecular dynamics simulations combined with machine learning approach, to study heat transport in the two-angle disordered multilayer stacks, and identified within the constrained structural space the optimal stacking sequence that can minimize the cross-plane thermal conductivity. Compared to pristine graphite, the optimized structure can reduce thermal conductivity by up to 80%. Through analysis of phonon transport properties across different structures, we revealed the underlying physical mechanism of phonon localization.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"269 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144341317","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

NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics 一个开放源代码的反微磁学节点有限差分代码

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-21 DOI: 10.1038/s41524-025-01688-1

C. Abert, F. Bruckner, A. Voronov, M. Lang, S. A. Pathak, S. Holt, R. Kraft, R. Allayarov, P. Flauger, S. Koraltan, T. Schrefl, A. Chumak, H. Fangohr, D. Suess

引用次数: 0

Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning 发现新的高压阶段-集成高通量DFT模拟，图神经网络和主动学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-20 DOI: 10.1038/s41524-025-01682-7

Ching-Chien Chen, Robert J. Appleton, Saswat Mishra, Kat Nykiel, Alejandro Strachan

{"title":"Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning","authors":"Ching-Chien Chen, Robert J. Appleton, Saswat Mishra, Kat Nykiel, Alejandro Strachan","doi":"10.1038/s41524-025-01682-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01682-7","url":null,"abstract":"Pressure-induced phase transformations in materials are of interest in a range of fields, including geophysics, planetary sciences, and shock physics. In addition, the high-pressure phases can exhibit desirable properties, eliciting interest in materials science. Despite its importance, the process of finding new high-pressure phases, either experimentally or computationally, is time-consuming and often driven by intuition. In this study, we use graph neural networks trained on density functional theory (DFT) equation of state data of 2258 materials and 7255 phases to identify potential phase transitions. The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations. Importantly, the new data is added to the training set, the model is refined, and a new cycle of discovery is started. Within 13 iterations, we discovered 28 new high-pressure stable phases (never synthesized through high-pressure routes nor reported in high-pressure computational works) and rediscovered 18 pressure-induced phase transitions. The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"59 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334922","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

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy 通过主动学习揭示扫描隧道显微镜中多尺度结构-性质的相关性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-19 DOI: 10.1038/s41524-025-01642-1

Ganesh Narasimha, Dejia Kong, Paras Regmi, Rongying Jin, Zheng Gai, Rama Vasudevan, Maxim Ziatdinov

{"title":"Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy","authors":"Ganesh Narasimha, Dejia Kong, Paras Regmi, Rongying Jin, Zheng Gai, Rama Vasudevan, Maxim Ziatdinov","doi":"10.1038/s41524-025-01642-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01642-1","url":null,"abstract":"Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn2As2, a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320082","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

Polymer design for solvent separations by integrating simulations, experiments and known physics via machine learning 通过机器学习集成模拟，实验和已知物理的溶剂分离聚合物设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-19 DOI: 10.1038/s41524-025-01681-8

Janhavi Nistane, Rohan Datta, Young Joo Lee, Harikrishna Sahu, Seung Soon Jang, Ryan Lively, Rampi Ramprasad

{"title":"Polymer design for solvent separations by integrating simulations, experiments and known physics via machine learning","authors":"Janhavi Nistane, Rohan Datta, Young Joo Lee, Harikrishna Sahu, Seung Soon Jang, Ryan Lively, Rampi Ramprasad","doi":"10.1038/s41524-025-01681-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01681-8","url":null,"abstract":"This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations. We focus on solvent diffusivity in polymers, a key factor in quantifying solvent transport. Traditional experimental and computational methods for determining diffusivity are time- and resource-intensive, while current machine learning (ML) models often lack accuracy outside their training domains. To overcome this, we fuse experimental and simulated diffusivity data to train physics-enforced multi-task ML models, achieving more robust predictions in unseen chemical spaces and outperforming single-task models in data-limited scenarios. Next, we address the challenge of identifying optimal membranes for a model toluene-heptane separation, identifying polyvinyl chloride (PVC) as the optimal membrane among 13,000 polymers, consistent with literature findings, thereby validating our methodology. Expanding our search, we screen 1 million publicly available and 7 million chemically recyclable polymers, identifying greener halogen-free alternatives to PVC. This capability is expected to advance membrane design for solvent separations.<figure></figure>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"240 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320008","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

Inverse mapping of properties to composition through generative modeling for designing molten salts 通过生成建模设计熔盐的属性到组成的逆映射

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-19 DOI: 10.1038/s41524-025-01638-x

Julian Barra, Rajni Chahal, Shubhojit Banerjee, Massimiliano Lupo Pasini, Stephan Irle, Stephen Lam

引用次数: 0