Pub Date : 2026-01-17DOI: 10.1038/s41524-025-01948-0
Alice E. A. Allen, Emily Shinkle, Roxana Bujack, Nicholas Lubbers
The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.
{"title":"Optimal invariant sets for atomistic machine learning","authors":"Alice E. A. Allen, Emily Shinkle, Roxana Bujack, Nicholas Lubbers","doi":"10.1038/s41524-025-01948-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01948-0","url":null,"abstract":"The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993499","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}
Pub Date : 2026-01-17DOI: 10.1038/s41524-026-01956-8
Colin Gilgenbach, Menglin Zhu, James M. LeBeau
We present phaser, an open-source Python package that provides a unified interface to both conventional and autodifferentiation-based ptychographic algorithms. Features such as mixed-state probe, probe position correction, and multislice ptychography make experimental reconstructions practical and robust. Reconstructions are specified in a declarative format and can be run from a command line, Jupyter notebook, or web interface. Multiple computational backends are supported to provide maximum flexibility. We report reconstruction success for a variety of experimental datasets, and detail the effects of regularization on convergence and reconstruction quality. Reconstruction speed is benchmarked for single-slice and multislice reconstructions and compared to state-of-the-art packages. The software promises to speed the application and development of ptychographic methods for materials science.
{"title":"phaser: a unified and extensible framework for fast electron ptychography","authors":"Colin Gilgenbach, Menglin Zhu, James M. LeBeau","doi":"10.1038/s41524-026-01956-8","DOIUrl":"https://doi.org/10.1038/s41524-026-01956-8","url":null,"abstract":"We present phaser, an open-source Python package that provides a unified interface to both conventional and autodifferentiation-based ptychographic algorithms. Features such as mixed-state probe, probe position correction, and multislice ptychography make experimental reconstructions practical and robust. Reconstructions are specified in a declarative format and can be run from a command line, Jupyter notebook, or web interface. Multiple computational backends are supported to provide maximum flexibility. We report reconstruction success for a variety of experimental datasets, and detail the effects of regularization on convergence and reconstruction quality. Reconstruction speed is benchmarked for single-slice and multislice reconstructions and compared to state-of-the-art packages. The software promises to speed the application and development of ptychographic methods for materials science.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993486","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}
Pub Date : 2026-01-16DOI: 10.1038/s41524-026-01957-7
Xiumin Chen, Yunmin Chen, Jie Zhou
When training deep neural networks using first-principles calculation data to obtain potential functions for molecular dynamics simulations, extensive model capability evaluation work is required. However, the commonly used validation sets for model evaluation are limited by the high cost of obtaining first-principles data, making it difficult to comprehensively assess the strong generalization ability of deep neural network trained models, which requires coverage of a much larger space than the training set samples. This manuscript proposes a short bond evaluation method and conducts evaluation experiments using this method and the self-consistent field labeling evaluation method on multiple tasks under different structures generalization in two complex reaction systems. It also performs correlation analysis between the results of the two methods to validate and explain the applicability and effectiveness of the proposed method. Although this method has the necessary and insufficient characteristics, the results show that this method can accelerate the assessment of model generalization capabilities while maintaining the reliability of the evaluation results. Moreover, this method can particularly accelerate the high-accuracy filter of poor-performing models, thereby helping to improve the convergence speed during the model training iteration process. At the same time, it achieves a significant reduction in evaluation costs.
{"title":"Short bond evaluation method for rapidly assessing the generalization ability of deep neural network potential function models and its effectiveness verification","authors":"Xiumin Chen, Yunmin Chen, Jie Zhou","doi":"10.1038/s41524-026-01957-7","DOIUrl":"https://doi.org/10.1038/s41524-026-01957-7","url":null,"abstract":"When training deep neural networks using first-principles calculation data to obtain potential functions for molecular dynamics simulations, extensive model capability evaluation work is required. However, the commonly used validation sets for model evaluation are limited by the high cost of obtaining first-principles data, making it difficult to comprehensively assess the strong generalization ability of deep neural network trained models, which requires coverage of a much larger space than the training set samples. This manuscript proposes a short bond evaluation method and conducts evaluation experiments using this method and the self-consistent field labeling evaluation method on multiple tasks under different structures generalization in two complex reaction systems. It also performs correlation analysis between the results of the two methods to validate and explain the applicability and effectiveness of the proposed method. Although this method has the necessary and insufficient characteristics, the results show that this method can accelerate the assessment of model generalization capabilities while maintaining the reliability of the evaluation results. Moreover, this method can particularly accelerate the high-accuracy filter of poor-performing models, thereby helping to improve the convergence speed during the model training iteration process. At the same time, it achieves a significant reduction in evaluation costs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"35 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993487","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}
Pub Date : 2026-01-15DOI: 10.1038/s41524-025-01916-8
Tiago F. T. Cerqueira, Haichen Wang, Silvana Botti, Miguel A. L. Marques
We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-property relationships. Our proposed material encoding has two components: one representing the crystal structure and the other characterizing the chemical composition, which we call Pettifor embedding. For the latter, we construct a non-orthogonal space where each axis represents a chemical element and where the angle between the axes quantifies a measure of the similarity between them. The chemical composition is then defined by the point on the unit sphere in this non-orthogonal space. We show that the Pettifor embeddings systematically outperform other commonly used elemental embeddings in compositional machine learning models. Using the Pettifor embeddings to define a distance metric and applying dimension reduction techniques, we construct a two-dimensional global map of the space of thermodynamically stable crystalline compounds. Despite their simplicity, such maps succeed in providing a physical separation of material classes according to basic physical properties.
{"title":"A non-orthogonal representation for materials based on chemical similarity","authors":"Tiago F. T. Cerqueira, Haichen Wang, Silvana Botti, Miguel A. L. Marques","doi":"10.1038/s41524-025-01916-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01916-8","url":null,"abstract":"We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-property relationships. Our proposed material encoding has two components: one representing the crystal structure and the other characterizing the chemical composition, which we call Pettifor embedding. For the latter, we construct a non-orthogonal space where each axis represents a chemical element and where the angle between the axes quantifies a measure of the similarity between them. The chemical composition is then defined by the point on the unit sphere in this non-orthogonal space. We show that the Pettifor embeddings systematically outperform other commonly used elemental embeddings in compositional machine learning models. Using the Pettifor embeddings to define a distance metric and applying dimension reduction techniques, we construct a two-dimensional global map of the space of thermodynamically stable crystalline compounds. Despite their simplicity, such maps succeed in providing a physical separation of material classes according to basic physical properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"45 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968816","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}
Pub Date : 2026-01-15DOI: 10.1038/s41524-026-01959-5
Shengtao Jiang, Arthur C. Campello, Wei He, Jiajia Wen, Daniel M. Pajerowski, Young S. Lee, Hong-Chen Jiang
Spin-1/2 kagome antiferromagnets are leading candidates for realizing quantum spin liquid (QSL) ground states. While QSL ground states are predicted for the pure Heisenberg model, understanding the robustness of the QSL to additional interactions that may be present in real materials is a forefront question in the field. Here we employ large-scale density-matrix renormalization group simulations to investigate the effects of next-nearest neighbor exchange couplings J2 and Dzyaloshinskii-Moriya interactions D, which are relevant to understanding the prototypical kagome materials herbertsmithite and Zn-barlowite. By utilizing clusters as large as XC12 and extrapolating the results to the thermodynamic limit, we precisely delineate the scope of the QSL phase, which remains robust across an expanded parameter range of J2 and D. Direct comparison of the simulated static and dynamic spin structure factors with inelastic neutron scattering reveals the parameter space of the Hamiltonians for herbertsmithite and Zn-barlowite, and, importantly, provides compelling evidence that both materials exist within the QSL phase. These results establish a powerful convergence of theory and experiment in this most elusive state of matter.
{"title":"Quantifying the phase diagram and Hamiltonian of S = 1/2 kagome antiferromagnets: bridging theory and experiment","authors":"Shengtao Jiang, Arthur C. Campello, Wei He, Jiajia Wen, Daniel M. Pajerowski, Young S. Lee, Hong-Chen Jiang","doi":"10.1038/s41524-026-01959-5","DOIUrl":"https://doi.org/10.1038/s41524-026-01959-5","url":null,"abstract":"Spin-1/2 kagome antiferromagnets are leading candidates for realizing quantum spin liquid (QSL) ground states. While QSL ground states are predicted for the pure Heisenberg model, understanding the robustness of the QSL to additional interactions that may be present in real materials is a forefront question in the field. Here we employ large-scale density-matrix renormalization group simulations to investigate the effects of next-nearest neighbor exchange couplings J2 and Dzyaloshinskii-Moriya interactions D, which are relevant to understanding the prototypical kagome materials herbertsmithite and Zn-barlowite. By utilizing clusters as large as XC12 and extrapolating the results to the thermodynamic limit, we precisely delineate the scope of the QSL phase, which remains robust across an expanded parameter range of J2 and D. Direct comparison of the simulated static and dynamic spin structure factors with inelastic neutron scattering reveals the parameter space of the Hamiltonians for herbertsmithite and Zn-barlowite, and, importantly, provides compelling evidence that both materials exist within the QSL phase. These results establish a powerful convergence of theory and experiment in this most elusive state of matter.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"266 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968814","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}
Pub Date : 2026-01-15DOI: 10.1038/s41524-025-01938-2
Rogério Almeida Gouvêa, Pierre-Paul De Breuck, Tatiane Pretto, Gian-Marco Rignanese, Marcos José Leite Santos
This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent representations from a diverse suite of pretrained graph-neural network (GNN) models—including structure-based (MEGNet), composition-based (ROOST), and equivariant (ORB) graph networks—with computationally efficient, GNN-approximated descriptors and novel features from symbolic regression. Our approach combines the chemical transparency of traditional feature-based models with the predictive power of deep learning architectures. When augmenting the feature-based model MODNet on Matbench tasks, this method yields significant error reductions and elevates its performance to be competitive with, and in several cases superior to, state-of-the-art end-to-end GNNs, with accuracy increases exceeding 40% for multiple tasks. An integrated interpretability module, employing surrogate models and symbolic regression, decodes the latent GNN-derived descriptors into explicit, physically meaningful formulas. This unified framework advances materials informatics by providing a high-performance, transparent tool that aligns with the principles of explainable AI, paving the way for more targeted and autonomous materials discovery.
{"title":"Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability","authors":"Rogério Almeida Gouvêa, Pierre-Paul De Breuck, Tatiane Pretto, Gian-Marco Rignanese, Marcos José Leite Santos","doi":"10.1038/s41524-025-01938-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01938-2","url":null,"abstract":"This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent representations from a diverse suite of pretrained graph-neural network (GNN) models—including structure-based (MEGNet), composition-based (ROOST), and equivariant (ORB) graph networks—with computationally efficient, GNN-approximated descriptors and novel features from symbolic regression. Our approach combines the chemical transparency of traditional feature-based models with the predictive power of deep learning architectures. When augmenting the feature-based model MODNet on Matbench tasks, this method yields significant error reductions and elevates its performance to be competitive with, and in several cases superior to, state-of-the-art end-to-end GNNs, with accuracy increases exceeding 40% for multiple tasks. An integrated interpretability module, employing surrogate models and symbolic regression, decodes the latent GNN-derived descriptors into explicit, physically meaningful formulas. This unified framework advances materials informatics by providing a high-performance, transparent tool that aligns with the principles of explainable AI, paving the way for more targeted and autonomous materials discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"17 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968817","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}
Pub Date : 2026-01-14DOI: 10.1038/s41524-025-01889-8
Siyu Chen, Yao Wei, Bartomeu Monserrat, Jan M. Tomczak, Samuel Poncé
Rare-earth superhydrides have attracted considerable attention because of their high critical superconducting temperature under extreme pressures. They are known to have localized valence electrons, implying strong electronic correlations. However, such many-body effects are rarely included in first-principles studies of rare-earth superhydrides because of the complexity of their high-pressure phases. In this work, we use a combined density functional theory and dynamical mean-field theory approach to study both electrons and phonons in the prototypical rare-earth superhydride CeH9, shedding light on the impact of electronic correlations on its critical temperature for phonon-mediated superconductivity. Our findings indicate that electronic correlations result in a larger electronic density at the Fermi level, a bigger superconducting gap, and softer vibrational modes associated with hydrogen atoms. Together, the inclusion of these correlation signatures increases the Migdal-Eliashberg superconducting critical temperature from 47 K to 96 K, close to the measured 95 K. Our results reconcile experimental observations and theoretical predictions for CeH9 and herald a path towards the quantitative modeling of phonon-mediated superconductivity for interacting electron systems.
{"title":"Impact of electronic correlations on the superconductivity of high-pressure CeH9","authors":"Siyu Chen, Yao Wei, Bartomeu Monserrat, Jan M. Tomczak, Samuel Poncé","doi":"10.1038/s41524-025-01889-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01889-8","url":null,"abstract":"Rare-earth superhydrides have attracted considerable attention because of their high critical superconducting temperature under extreme pressures. They are known to have localized valence electrons, implying strong electronic correlations. However, such many-body effects are rarely included in first-principles studies of rare-earth superhydrides because of the complexity of their high-pressure phases. In this work, we use a combined density functional theory and dynamical mean-field theory approach to study both electrons and phonons in the prototypical rare-earth superhydride CeH9, shedding light on the impact of electronic correlations on its critical temperature for phonon-mediated superconductivity. Our findings indicate that electronic correlations result in a larger electronic density at the Fermi level, a bigger superconducting gap, and softer vibrational modes associated with hydrogen atoms. Together, the inclusion of these correlation signatures increases the Migdal-Eliashberg superconducting critical temperature from 47 K to 96 K, close to the measured 95 K. Our results reconcile experimental observations and theoretical predictions for CeH9 and herald a path towards the quantitative modeling of phonon-mediated superconductivity for interacting electron systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"21 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968818","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}
Pub Date : 2026-01-14DOI: 10.1038/s41524-025-01932-8
Lilac Macmillan, Eduardo Costa Girão, Vincent Meunier
We introduce the embedding tensor Φ, a rank-3, purely integer tensor that elevates faces and edges to the same footing as vertices to provide a unique, coordinate-free representation of any three-connected two-dimensional carbon lattice. Defined by the incidence of vertices, edges, and polygonal faces, Φ obeys simple summation rules derived from Euler characteristics. Casting Φ into a flag graph enables exact, tolerance-free identification of wallpaper symmetries. Building on this algebraic framework, we develop an iterative add-dimer search that generates all structures with NF faces from all crystals with NF − 1 faces, while automatically discarding duplicates via tensor isomorphism and spotting non-primitive cells through symmetry checks. Exploiting symmetry keeps the combinatorial growth of dimer insertions tractable even for NF > 5, transforming an otherwise exponential search into a practically feasible approach for high-throughput exploration. Once candidate topologies are enumerated, approximate real-space coordinates and lattice vectors can be reconstructed analytically from Φ and sparse crossing matrices, providing initial geometries for electronic or vibrational calculations. The method delivers an end-to-end pipeline from exhaustive, symmetry-aware enumeration to metadata tagging and coordinate generation, while requiring only integer arithmetic.
{"title":"Graph embedding tensor: unifying topological description, symmetry detection, and structure generation for two-dimensional carbon allotropes","authors":"Lilac Macmillan, Eduardo Costa Girão, Vincent Meunier","doi":"10.1038/s41524-025-01932-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01932-8","url":null,"abstract":"We introduce the embedding tensor Φ, a rank-3, purely integer tensor that elevates faces and edges to the same footing as vertices to provide a unique, coordinate-free representation of any three-connected two-dimensional carbon lattice. Defined by the incidence of vertices, edges, and polygonal faces, Φ obeys simple summation rules derived from Euler characteristics. Casting Φ into a flag graph enables exact, tolerance-free identification of wallpaper symmetries. Building on this algebraic framework, we develop an iterative add-dimer search that generates all structures with NF faces from all crystals with NF − 1 faces, while automatically discarding duplicates via tensor isomorphism and spotting non-primitive cells through symmetry checks. Exploiting symmetry keeps the combinatorial growth of dimer insertions tractable even for NF > 5, transforming an otherwise exponential search into a practically feasible approach for high-throughput exploration. Once candidate topologies are enumerated, approximate real-space coordinates and lattice vectors can be reconstructed analytically from Φ and sparse crossing matrices, providing initial geometries for electronic or vibrational calculations. The method delivers an end-to-end pipeline from exhaustive, symmetry-aware enumeration to metadata tagging and coordinate generation, while requiring only integer arithmetic.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"56 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968821","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}
High-entropy layered oxides are promising sodium-ion battery (SIB) cathodes, yet the fundamental role of conformational entropy in stacking phase preference remains unclear. Here, we combine density functional theory (DFT), ab initio molecular dynamics (AIMD), and a fine-tuned CHGNet machine-learning interatomic potential (MLIP) to investigate representative high-entropy (Na0.8Ni0.2Fe0.2Co0.2Mn0.2Ti0.2O2) and low-entropy (Na0.8Mn0.6Co0.4O2) layered oxides in both O3 and P2 phases. A three-stage Monte Carlo sampling strategy was developed to explore transition-metal arrangements, Na/vacancy distributions, and representative low-energy conformations. The fine-tuned CHGNet achieved near-DFT accuracy while enabling large-scale sampling at orders of magnitude lower cost. Our analyses reveal that high-entropy oxides exhibit stronger Na–TMO2 interactions, broader O–TM bond length distributions, and smaller interlayer distance ratios compared with their low-entropy counterparts. These structural features favor O3-phase stabilization in cases where conventional ionic-potential descriptors are insufficient to clearly distinguish between O3- and P2-type layered oxides. Bond-length analyses further indicate that Jahn–Teller distortions in Mn are mitigated in high-entropy oxides, contributing to enhanced structural stability. This study establishes conformational entropy as a decisive factor, alongside Na ionic and cationic potentials, in governing stacking phase stability, and highlights the power of MLIP-accelerated modeling for exploring high-entropy materials and guiding the rational design of next-generation SIB cathodes.
{"title":"Probing entropic control of stacking phase preference in layered oxide cathodes for sodium-ion batteries via machine-learning potentials","authors":"Liang-Ting Wu, Zhong-Lun Li, Shih-Ying Yen, Payam Kaghazchi, Jyh-Chiang Jiang","doi":"10.1038/s41524-025-01954-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01954-2","url":null,"abstract":"High-entropy layered oxides are promising sodium-ion battery (SIB) cathodes, yet the fundamental role of conformational entropy in stacking phase preference remains unclear. Here, we combine density functional theory (DFT), ab initio molecular dynamics (AIMD), and a fine-tuned CHGNet machine-learning interatomic potential (MLIP) to investigate representative high-entropy (Na0.8Ni0.2Fe0.2Co0.2Mn0.2Ti0.2O2) and low-entropy (Na0.8Mn0.6Co0.4O2) layered oxides in both O3 and P2 phases. A three-stage Monte Carlo sampling strategy was developed to explore transition-metal arrangements, Na/vacancy distributions, and representative low-energy conformations. The fine-tuned CHGNet achieved near-DFT accuracy while enabling large-scale sampling at orders of magnitude lower cost. Our analyses reveal that high-entropy oxides exhibit stronger Na–TMO2 interactions, broader O–TM bond length distributions, and smaller interlayer distance ratios compared with their low-entropy counterparts. These structural features favor O3-phase stabilization in cases where conventional ionic-potential descriptors are insufficient to clearly distinguish between O3- and P2-type layered oxides. Bond-length analyses further indicate that Jahn–Teller distortions in Mn are mitigated in high-entropy oxides, contributing to enhanced structural stability. This study establishes conformational entropy as a decisive factor, alongside Na ionic and cationic potentials, in governing stacking phase stability, and highlights the power of MLIP-accelerated modeling for exploring high-entropy materials and guiding the rational design of next-generation SIB cathodes.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"81 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968819","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}
Pub Date : 2026-01-13DOI: 10.1038/s41524-025-01928-4
Alberto Carta, Anwesha Panda, Claude Ederer
Calculations combining density functional theory (DFT) and dynamical mean-field theory (DMFT) for transition metal (TM) oxides and similar compounds usually focus on improving the description of the TM d states. Here, we emphasize the importance of also accounting for corrections of the ligand p states. We demonstrate that focusing exclusively on improving the description of the TM d states results in difficulties to obtain the correct insulating behavior for a variety of materials, and requires to use values for the local interaction parameters that are inconsistent with values obtained using, e.g., the constrained random phase approximation (cRPA). We demonstrate that, to a large part, these inconsistencies arise from using local/semi-local DFT as starting point for computing interaction parameters, and we show that applying a simple empirical correction to the low energy states not included in the correlated subspace results in improved values for the interaction parameters that then allow to obtain the correct insulating behavior. Moreover, we show that applying an approximate but realistic Hartree-Fock-like correction specifically to the O p orbitals, when they are explicitly included in the DMFT subspace, significantly improves the quantitative accuracy of the DFT+DMFT description for prototypical Mott insulators, including LaTiO3, LaVO3, and the perovskite rare-earth nickelates (RNiO3).
{"title":"Importance of ligand on-site interactions for the description of Mott-insulators in DFT+DMFT","authors":"Alberto Carta, Anwesha Panda, Claude Ederer","doi":"10.1038/s41524-025-01928-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01928-4","url":null,"abstract":"Calculations combining density functional theory (DFT) and dynamical mean-field theory (DMFT) for transition metal (TM) oxides and similar compounds usually focus on improving the description of the TM d states. Here, we emphasize the importance of also accounting for corrections of the ligand p states. We demonstrate that focusing exclusively on improving the description of the TM d states results in difficulties to obtain the correct insulating behavior for a variety of materials, and requires to use values for the local interaction parameters that are inconsistent with values obtained using, e.g., the constrained random phase approximation (cRPA). We demonstrate that, to a large part, these inconsistencies arise from using local/semi-local DFT as starting point for computing interaction parameters, and we show that applying a simple empirical correction to the low energy states not included in the correlated subspace results in improved values for the interaction parameters that then allow to obtain the correct insulating behavior. Moreover, we show that applying an approximate but realistic Hartree-Fock-like correction specifically to the O p orbitals, when they are explicitly included in the DMFT subspace, significantly improves the quantitative accuracy of the DFT+DMFT description for prototypical Mott insulators, including LaTiO3, LaVO3, and the perovskite rare-earth nickelates (RNiO3).","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"21 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956359","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}
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