Hamed Taghavian, Viktor Vanoppen, Erik Berg, Peter Broqvist, Jens Sjölund
{"title":"Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations","authors":"Hamed Taghavian, Viktor Vanoppen, Erik Berg, Peter Broqvist, Jens Sjölund","doi":"10.1038/s41524-025-01735-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01735-x","url":null,"abstract":"<p>Metal anodes provide the highest energy density in batteries. However, they still suffer from electrode/electrolyte interface side reactions and dendrite growth, especially under fast-charging conditions. In this paper, we consider a phase-field model of electrodeposition in metal-anode batteries and provide a scalable, versatile framework for optimizing its chemical parameters. Our approach is based on Bayesian optimization and explores the parameter space with a high sample efficiency and a low computation complexity. We use this framework to find the optimal cell for suppressing dendrite growth and accelerating charging speed under constant voltage. We identify interfacial mobility as a key parameter, which should be maximized to inhibit dendrites without compromising the charging speed. The results are verified using extended simulations of dendrite evolution in charging half cells with lithium-metal anodes.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"48 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702063","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":"Design principles for density functionals using a linear expansion","authors":"Ayoub Aouina, Matteo Gatti, Lucia Reining","doi":"10.1038/s41524-025-01712-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01712-4","url":null,"abstract":"<p>Density Functional Theory is one of the most widely used theoretical approaches for the calculation of properties of materials, but the systematic development of new functionals with controllable accuracy is an ongoing challenge. We propose to use perturbation theory around the homogeneous electron gas in a way that is optimized using physical insight, and to combine it with the recently developed connector approach in order to satisfy an exact limit. In this way, we develop an explicit non-local density functional for the Kohn-Sham exchange correlation potential. First results for the self-consistently calculated charge density and potential for three prototype materials demonstrate which accuracy can be reached for the charge density, confirm the systematicity of the approach, and suggest directions for further improvement.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678069","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}
Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan
{"title":"Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine","authors":"Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan","doi":"10.1038/s41524-025-01724-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01724-0","url":null,"abstract":"<p>A long-standing challenge in tomography is the ‘missing wedge’ problem, which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints. This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image. To tackle this challenge, we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine, which integrates a convolutional neural network (CNN) with perceptional knowledge as a smart regularizer into an iterative solving engine. We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains, thereby achieving a physically coherent and visually enhanced result. We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques. All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail. Notably, it also improves the reconstruction in case of sparse projections, despite the network not being specifically trained for that. This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664632","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}
Pierre-Clément A. Simon, Jia-Hong Ke, Chao Jiang, Larry K. Aagesen, Wen Jiang, Stephen Novascone
{"title":"Multiscale, mechanistic modeling of cesium transport in silicon carbide for TRISO fuel performance prediction","authors":"Pierre-Clément A. Simon, Jia-Hong Ke, Chao Jiang, Larry K. Aagesen, Wen Jiang, Stephen Novascone","doi":"10.1038/s41524-025-01734-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01734-y","url":null,"abstract":"<p>Understanding cesium (Cs) transport in TRistructural ISOtropic (TRISO) particle fuel is crucial for predicting fission product release in high-temperature reactors. However, current challenges include significant scatter in diffusivity data and unexplained temperature-dependent diffusion regimes in the silicon carbide layer. This study addresses these challenges by developing a multiscale, mechanistic Cs transport model integrating atomistic simulations and phase field modeling. Our model quantifies temperature and grain size effects on Cs diffusivity, attributing experimentally observed regimes to a transition from bulk-dominated diffusivity at high temperatures to grain boundary-dominated diffusivity at lower temperatures. The model, validated against diffusion measurements and advanced gas reactor (AGR)-1 and AGR-2 post-irradiation fission product release data, enhances the predictive capability of the BISON fuel performance code. This study advances our understanding of Cs release from TRISO particles and its dependence on temperature and silicon carbide grain size, with implications for the safety and efficiency of high-temperature nuclear reactors.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664633","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}
Xi Chen, Yuchuang Cao, Jianghui Pan, Jiahao Dong, Changkai Luo, Xin Li
{"title":"Coupled lattice-charge-magnetic fluctuations for nonlocal flux mediated pairing in cuprate superconductors","authors":"Xi Chen, Yuchuang Cao, Jianghui Pan, Jiahao Dong, Changkai Luo, Xin Li","doi":"10.1038/s41524-025-01697-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01697-0","url":null,"abstract":"<p>Dynamic charge transfers, or charge flux oscillations, generated by anharmonic phonon coupling, have attracted increasing interest in cuprate superconductors. In this article, a new computational method is developed to analyze such charge fluxes along all atomic bonds for a given material, which unveils a surprising fact that cuprate materials with high superconducting transition temperature show a strong tendency to support global charge flux flows beyond local charge oscillations. Such fluxes further show a strong correlation with both the maximum superconducting transition temperature of different cuprate families and the strong magnetic fluctuations as well. Motivated by these findings, we construct a charge flux model derived from quantum field theory to evaluate the effective interactions mediated by these flux flows. Finally, we discuss the implications of this flux-driven pairing mechanism for the design of new high-T<sub>c</sub> superconductors, offering a potential strategy for discovering higher T<sub>c</sub> superconductive materials.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"659 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664619","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}
Xin Li, Zhixuan Huang, Shu Quan, Cheng Peng, Xiaoming Ma
{"title":"SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction","authors":"Xin Li, Zhixuan Huang, Shu Quan, Cheng Peng, Xiaoming Ma","doi":"10.1038/s41524-025-01719-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01719-x","url":null,"abstract":"<p>Small Language Models offer an efficient alternative for structured information extraction. We present <b>SLM-MATRIX</b>, a multi-path collaborative reasoning and verification framework based on SLMs, designed to extract material names, numerical values, and physical units from materials science literature. The framework integrates three complementary reasoning paths: a multi-agent collaborative path, a generator–discriminator path, and a dual cross-verification path. SLM-MATRIX achieves an accuracy of 92.85% on the BulkModulus dataset and reaches 77.68% accuracy on the MatSynTriplet dataset, both outperforming conventional methods and single-path models. Moreover, experiments on general reasoning benchmarks such as GSM8K and SVAMP validate the framework’s strong generalization capability. Ablation studies evaluate the effects of agent number, Mixture-of-Agents (MoA) depth, and discriminator design on overall performance. Overall, SLM-MATRIX presents an effective approach for high-quality material information extraction in resource-constrained and offers new insights into structured scientific text understanding tasks.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664621","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}
Mariia Radova, Wojciech G. Stark, Connor S. Allen, Reinhard J. Maurer, Albert P. Bartók
{"title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","authors":"Mariia Radova, Wojciech G. Stark, Connor S. Allen, Reinhard J. Maurer, Albert P. Bartók","doi":"10.1038/s41524-025-01727-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01727-x","url":null,"abstract":"<p>Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training data set remains a challenge, often requiring thousands of first-principles calculations to achieve high accuracy. Foundation models have started to emerge with the ambition to create universally applicable potentials across a wide range of materials. While foundation models can be robust and transferable, they do not yet achieve the accuracy required to predict reaction barriers, phase transitions, and material stability. This work demonstrates that foundation model potentials can reach chemical accuracy when fine-tuned using transfer learning with partially frozen weights and biases. For two challenging datasets on reactive chemistry at surfaces and stability and elastic properties of tertiary alloys, we show that frozen transfer learning with 10–20% of the data (hundreds of datapoints) achieves similar accuracies to models trained from scratch (on thousands of datapoints). Moreover, we show that an equally accurate, but significantly more efficient surrogate model can be built using the transfer learned potential as the ground truth. In combination, we present a simulation workflow for machine learning potentials that improves data efficiency and computational efficiency.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"677 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652617","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":"5d orbital induced room temperature quantum anomalous Hall effect in TbCl","authors":"Jianqi Zhong, Jianzhou Zhao, Jinyu Zou, Gang Xu","doi":"10.1038/s41524-025-01732-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01732-0","url":null,"abstract":"<p>Following the experimental realization of Quantum anomalous Hall (QAH) effect in thin films of chromium-doped (Bi,Sb)<sub>2</sub>Te<sub>3</sub>, enhancing the work temperature of QAH effect has emerged as a significant and challenging task. Here we demonstrate monolayer TbCl as a promising candidate to realize the room temperature QAH effect. Using DFT+U method, double-checked by HSE06 and DMFT calculations, we identify the Hall conductivity <i>G</i> = −<i>e</i><sup>2</sup>/<i>h</i> per layer in three-dimensional ferromagnetic insulator TbCl, which is a weakly stacked QAH layer. The monolayer TbCl inherits the magnetic and topological properties, exhibiting the QAH effect with Chern number <i>C</i> = −1. The large topological band gap reaches 42.8 meV, which is beyond room temperature. The extended 5<i>d</i> electrons lead to sizable exchange and superexchange interactions, resulting in a high Curie temperature <i>T</i><sub><i>c</i></sub> ~ 457 K. All these features demonstrate that monolayer TbCl will provide an ideal platform to realize the room temperature QAH effect.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652633","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":"Cross-modality material embedding loss for transferring knowledge between heterogeneous material descriptors","authors":"Gyoung S. Na","doi":"10.1038/s41524-025-01723-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01723-1","url":null,"abstract":"<p>Despite the remarkable successes of transfer learning in materials science, the practicality of existing transfer learning methods are still limited in real-world applications of materials science because they essentially assume the same material descriptors on source and target materials datasets. In other words, existing transfer learning methods cannot utilize the knowledge extracted from calculated crystal structures when analyzing experimental observations of real-world chemical experiments. We propose a transfer learning criterion, called <i>cross-modality material embedding loss</i> (CroMEL), to build a source feature extractor that can transfer knowledge extracted from calculated crystal structures to prediction models in target applications where only simple chemical compositions are accessible. The prediction models based on transfer learning with CroMEL showed state-of-the-art prediction accuracy on 14 experimental materials datasets in various chemical applications. In particular, the prediction models with CroMEL achieved <i>R</i><sup>2</sup>-scores greater than 0.95 in predicting the experimentally measured formation enthalpies and band gaps of the experimentally synthesized materials.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645644","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}
Pranoy Ray, Adam P. Generale, Nikhith Vankireddy, Yuichiro Asoma, Masataka Nakauchi, Haein Lee, Katsuhisa Yoshida, Yoshishige Okuno, Surya R. Kalidindi
{"title":"Refining coarse-grained molecular topologies: a Bayesian optimization approach","authors":"Pranoy Ray, Adam P. Generale, Nikhith Vankireddy, Yuichiro Asoma, Masataka Nakauchi, Haein Lee, Katsuhisa Yoshida, Yoshishige Okuno, Surya R. Kalidindi","doi":"10.1038/s41524-025-01729-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01729-9","url":null,"abstract":"<p>Molecular Dynamics (MD) simulations are vital for predicting the physical and chemical properties of molecular systems across various ensembles. While All-Atom (AA) MD provides high accuracy, its computational cost has spurred the development of Coarse-Grained MD (CGMD), which simplifies molecular structures into representative beads to reduce expense but sacrifice precision. CGMD methods like Martini3, calibrated against experimental data, generalize well across molecular classes but often fail to meet the accuracy demands of domain-specific applications. This work introduces a Bayesian Optimization-based approach to refine Martini3 topologies—specifically the bonded interaction parameters within a given coarse-grained mapping—for specialized applications, ensuring accuracy and efficiency. The resulting optimized CG potential accommodates any degree of polymerization, offering accuracy comparable to AA simulations while retaining the computational speed of CGMD. By bridging the gap between efficiency and accuracy, this method advances multiscale molecular simulations, enabling cost-effective molecular discovery for diverse scientific and technological fields.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645646","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}