Pub Date : 2026-01-21DOI: 10.1038/s41524-025-01937-3
Robert G. Palgrave
Recently, Gazzarrini et al. observed a ‘rule of four’, where the number of atoms in a primitive unit cell for inorganic materials taken from large databases has been observed to favour multiples of four1. The number of atoms in a primitive cell is given by the product of the number of atoms in a formula unit (nF) and the number of formula units per primitive cell (Z). Here it is shown the rule of four can be explained by taking into account the most probable values of nF and Z in inorganic materials datasets.
{"title":"A possible explanation for the Rule of Four in Inorganic Materials","authors":"Robert G. Palgrave","doi":"10.1038/s41524-025-01937-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01937-3","url":null,"abstract":"Recently, Gazzarrini et al. observed a ‘rule of four’, where the number of atoms in a primitive unit cell for inorganic materials taken from large databases has been observed to favour multiples of four1. The number of atoms in a primitive cell is given by the product of the number of atoms in a formula unit (nF) and the number of formula units per primitive cell (Z). Here it is shown the rule of four can be explained by taking into account the most probable values of nF and Z in inorganic materials datasets.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"38 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006141","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 : 2026-01-20DOI: 10.1038/s41524-026-01965-7
Oier Arcelus, Javier Carrasco
Dislocation dynamics at the atomic scale play a significant role in phase transformations and mechanical degradation of layered cathode materials in Na-ion batteries (NIBs), yet their fundamental behavior remains poorly understood. Here, we employ first-principle calculations to investigate dislocation-mediated processes in a range of O3- and O'3-type layered transition metal (TM) oxides, Na(TM)O₂, with TM = Ti, Cr, Mn, Fe, Co, and Ni. Generalized stacking fault (gamma)-surfaces are computed to quantify the influence of TM chemistry on stacking sequence energetics. These (gamma)-surfaces, combined with elastic tensor data, inform a semi-discrete variational Peierls–Nabarro model to characterize dislocation core structures and Peierls stresses. Our results reveal narrow dislocation cores and partial splitting behaviors governed by the γ-surface topology and material elasticity. We further propose a dislocation-driven mechanism for the O3(leftrightarrow)P3 phase transformation, wherein partial dislocation motion facilitates the broadening of stacking faults during desodiation. This work establishes a detailed first-principles computational framework for understanding dislocation-mediated degradation pathways in layered oxides, offering atomistic-scale insights for the design of more robust NIB cathode materials.
{"title":"First-principles computation of dislocation structures and stress-driven phase transformations in layered oxides for Na-ion batteries","authors":"Oier Arcelus, Javier Carrasco","doi":"10.1038/s41524-026-01965-7","DOIUrl":"https://doi.org/10.1038/s41524-026-01965-7","url":null,"abstract":"Dislocation dynamics at the atomic scale play a significant role in phase transformations and mechanical degradation of layered cathode materials in Na-ion batteries (NIBs), yet their fundamental behavior remains poorly understood. Here, we employ first-principle calculations to investigate dislocation-mediated processes in a range of O3- and O'3-type layered transition metal (TM) oxides, Na(TM)O₂, with TM = Ti, Cr, Mn, Fe, Co, and Ni. Generalized stacking fault (gamma)-surfaces are computed to quantify the influence of TM chemistry on stacking sequence energetics. These (gamma)-surfaces, combined with elastic tensor data, inform a semi-discrete variational Peierls–Nabarro model to characterize dislocation core structures and Peierls stresses. Our results reveal narrow dislocation cores and partial splitting behaviors governed by the γ-surface topology and material elasticity. We further propose a dislocation-driven mechanism for the O3(leftrightarrow)P3 phase transformation, wherein partial dislocation motion facilitates the broadening of stacking faults during desodiation. This work establishes a detailed first-principles computational framework for understanding dislocation-mediated degradation pathways in layered oxides, offering atomistic-scale insights for the design of more robust NIB cathode materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006144","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 : 2026-01-20DOI: 10.1038/s41524-025-01955-1
Abigail N. Poteshman, Francesco Ricci, Jeffrey B. Neaton
Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et al., Phys. Rev. B 102, 045141 (2020)] has been proposed as an efficient and reliable alternative, though it has yet to be widely deployed. Here, we assess Berry flux diagonalization using ab initio calculations of a large set of materials, introducing and validating heuristics that ensure branch alignment with a minimal number of intermediate interpolated structures. Our automated implementation of Berry flux diagonalization succeeds in cases where prior interpolation-based workflows fail due to band-gap closures or branch ambiguities. Benchmarking with ab initio calculations of 176 candidate ferroelectrics, we demonstrate the efficacy of the approach on a broad range of insulating materials and obtain accurate effective polarization values with fewer interpolated structures than prior automated interpolation-based workflows. Our real-space heuristics that can predict gauge stability a priori from ionic displacements enable a general automated framework for reliable polarization calculations and efficient high-throughput screening of chemically and structurally diverse polar insulators. These results establish Berry flux diagonalization as a robust and efficient method to compute the effective polarization of solids and to accelerate the data-driven discovery of functional polar materials.
在没有外部外加电场的情况下，电极化是极性材料的一个关键特性，但在现代极化理论中，基于标准插值的从头计算方法来计算极化差异，对自动化高通量计算提出了挑战。草莓通量对角化[J]。博尼尼等人，物理学。Rev. B 102, 045141(2020)]已被提出作为一种高效可靠的替代方案，尽管它尚未被广泛部署。在这里，我们使用大量材料的从头计算来评估Berry通量对角化，引入并验证了确保分支对准的最小数量的中间插值结构的启发式方法。在先前基于插值的工作流由于带隙关闭或分支模糊而失败的情况下，我们的Berry通量对角化自动化实现成功。通过从头算176种候选铁电体的基准测试，我们证明了该方法在广泛的绝缘材料上的有效性，并且与之前基于自动插值的工作流程相比，使用更少的插值结构获得准确的有效极化值。我们的真实空间启发式方法可以从离子位移中先验地预测测量稳定性，从而为可靠的极化计算和化学和结构不同的极性绝缘体的高效高通量筛选提供了一个通用的自动化框架。这些结果表明，Berry通量对角化是计算固体有效极化和加速功能极性材料的数据驱动发现的一种稳健而有效的方法。
{"title":"High-throughput computation of electric polarization in solids via Berry flux diagonalization","authors":"Abigail N. Poteshman, Francesco Ricci, Jeffrey B. Neaton","doi":"10.1038/s41524-025-01955-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01955-1","url":null,"abstract":"Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et al., Phys. Rev. B 102, 045141 (2020)] has been proposed as an efficient and reliable alternative, though it has yet to be widely deployed. Here, we assess Berry flux diagonalization using ab initio calculations of a large set of materials, introducing and validating heuristics that ensure branch alignment with a minimal number of intermediate interpolated structures. Our automated implementation of Berry flux diagonalization succeeds in cases where prior interpolation-based workflows fail due to band-gap closures or branch ambiguities. Benchmarking with ab initio calculations of 176 candidate ferroelectrics, we demonstrate the efficacy of the approach on a broad range of insulating materials and obtain accurate effective polarization values with fewer interpolated structures than prior automated interpolation-based workflows. Our real-space heuristics that can predict gauge stability a priori from ionic displacements enable a general automated framework for reliable polarization calculations and efficient high-throughput screening of chemically and structurally diverse polar insulators. These results establish Berry flux diagonalization as a robust and efficient method to compute the effective polarization of solids and to accelerate the data-driven discovery of functional polar materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"263 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006145","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 : 2026-01-20DOI: 10.1038/s41524-025-01933-7
Anurag Bajpai, Jaemin Wang, Dierk Raabe
Processing history imprints metallic glasses (MGs), yet whether compositional complexity desensitizes structure and mechanics to quench rate remains unresolved. We use large-scale molecular dynamics along a controlled Cu-Zr complexity ladder, Cu50Zr50, Cu47.5Zr47.5Al5, and Cu45Zr45Al5Ti5, vitrified over 1011–1015 K·s−1 and probed by spherical nanoindentation. Additionally, composition-resolved CuxZr100−x sweep (x = 40–65 at.%) and a microalloying series Cu50-z/2Zr50-z/2Alz, (z = 1–5 at.%) disentangle configurational entropy-driven effects from enthalpic and structural covariates. Atomic free volume is obtained from radical-Voronoi tessellation; non-affine rearrangements are quantified by Falk–Langer ({D}_{min }^{2}) field and clustered in three dimensions. Three quantitative descriptors capture the dispersion of free volume and its quench rate sensitivity as a function of compositional complexity. Increasing compositional complexity narrows free-volume distributions across quench rates and systematically reduces the fast-slow disparity. A two-axis reconciliation emerges: within binary Cu-Zr, configurational entropy peaks near equiatomic and minimizes rate sensitivity, whereas across alloy families (binary→ternary→quaternary), increased species diversity and size/enthalpy mismatch further suppress sensitivity. Structure-property co-variation is consistent: at fixed rate, hardness, modulus and elastic recovery increase, while serration density, STZ number density, and plastic-zone volume decrease. Radial-distribution metrics and indentation-induced icosahedral losses corroborate enhanced short/medium-range stability. Compositional complexity thus provides a quantitative lever for processing-tolerant, high-performance Cu-Zr-based MGs.
加工历史印记金属玻璃（mg），但是否成分复杂性脱敏的结构和力学淬火速度仍未解决。我们沿着可控的Cu-Zr复杂性阶梯进行了大规模的分子动力学研究，Cu50Zr50, Cu47.5Zr47.5Al5和Cu45Zr45Al5Ti5，在1011-1015 K·s−1的温度下玻璃化，并用球形纳米压痕探测。此外，成分分辨CuxZr100−x扫描（x = 40-65 at）。%) and a microalloying series Cu50-z/2Zr50-z/2Alz, (z = 1–5 at.%) disentangle configurational entropy-driven effects from enthalpic and structural covariates. Atomic free volume is obtained from radical-Voronoi tessellation; non-affine rearrangements are quantified by Falk–Langer ({D}_{min }^{2}) field and clustered in three dimensions. Three quantitative descriptors capture the dispersion of free volume and its quench rate sensitivity as a function of compositional complexity. Increasing compositional complexity narrows free-volume distributions across quench rates and systematically reduces the fast-slow disparity. A two-axis reconciliation emerges: within binary Cu-Zr, configurational entropy peaks near equiatomic and minimizes rate sensitivity, whereas across alloy families (binary→ternary→quaternary), increased species diversity and size/enthalpy mismatch further suppress sensitivity. Structure-property co-variation is consistent: at fixed rate, hardness, modulus and elastic recovery increase, while serration density, STZ number density, and plastic-zone volume decrease. Radial-distribution metrics and indentation-induced icosahedral losses corroborate enhanced short/medium-range stability. Compositional complexity thus provides a quantitative lever for processing-tolerant, high-performance Cu-Zr-based MGs.
{"title":"Compositional complexity buffers free-volume sensitivity and serrated flow in metallic glasses","authors":"Anurag Bajpai, Jaemin Wang, Dierk Raabe","doi":"10.1038/s41524-025-01933-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01933-7","url":null,"abstract":"Processing history imprints metallic glasses (MGs), yet whether compositional complexity desensitizes structure and mechanics to quench rate remains unresolved. We use large-scale molecular dynamics along a controlled Cu-Zr complexity ladder, Cu50Zr50, Cu47.5Zr47.5Al5, and Cu45Zr45Al5Ti5, vitrified over 1011–1015 K·s−1 and probed by spherical nanoindentation. Additionally, composition-resolved CuxZr100−x sweep (x = 40–65 at.%) and a microalloying series Cu50-z/2Zr50-z/2Alz, (z = 1–5 at.%) disentangle configurational entropy-driven effects from enthalpic and structural covariates. Atomic free volume is obtained from radical-Voronoi tessellation; non-affine rearrangements are quantified by Falk–Langer ({D}_{min }^{2}) field and clustered in three dimensions. Three quantitative descriptors capture the dispersion of free volume and its quench rate sensitivity as a function of compositional complexity. Increasing compositional complexity narrows free-volume distributions across quench rates and systematically reduces the fast-slow disparity. A two-axis reconciliation emerges: within binary Cu-Zr, configurational entropy peaks near equiatomic and minimizes rate sensitivity, whereas across alloy families (binary→ternary→quaternary), increased species diversity and size/enthalpy mismatch further suppress sensitivity. Structure-property co-variation is consistent: at fixed rate, hardness, modulus and elastic recovery increase, while serration density, STZ number density, and plastic-zone volume decrease. Radial-distribution metrics and indentation-induced icosahedral losses corroborate enhanced short/medium-range stability. Compositional complexity thus provides a quantitative lever for processing-tolerant, high-performance Cu-Zr-based MGs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006143","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 : 2026-01-19DOI: 10.1038/s41524-025-01946-2
Gang Seob Jung, Lei Cheng
Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DFT) reference data. To efficiently capture transitional configurations, we combine enhanced-sampling molecular dynamics with empirical potentials, data distillation, and pretraining on short-range atomic energies (A-Train), followed by transfer learning with DFT-relabeled datasets. This hierarchical approach improves transferability across polymorphs and stress states. We further introduce effective charge separation, treating long-range Coulombic terms analytically while short-range residual interactions are learned by the NNP. The optimal effective charges fall in the range 0.5–1.0 qe, consistent with dielectric-screened values derived from formal charges but distinct from Bader estimates. Motivated by this observation, we propose a simple data-driven protocol in which effective charges are optimized by comparing DFT reference energies with explicit Coulomb calculations, without additional NNP training. This strategy improves accuracy and transferability in DFT-level predictions of energies, forces, and stress. Together, these results provide a practical charge-selection framework for robust NNP development in ionic solids, enabling reliable simulation of polymorphic phase transformations and non-equilibrium dynamics.
{"title":"Neural network potentials with effective charge separation for non-equilibrium dynamics of ionic solids: a ZnO case study","authors":"Gang Seob Jung, Lei Cheng","doi":"10.1038/s41524-025-01946-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01946-2","url":null,"abstract":"Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DFT) reference data. To efficiently capture transitional configurations, we combine enhanced-sampling molecular dynamics with empirical potentials, data distillation, and pretraining on short-range atomic energies (A-Train), followed by transfer learning with DFT-relabeled datasets. This hierarchical approach improves transferability across polymorphs and stress states. We further introduce effective charge separation, treating long-range Coulombic terms analytically while short-range residual interactions are learned by the NNP. The optimal effective charges fall in the range 0.5–1.0 qe, consistent with dielectric-screened values derived from formal charges but distinct from Bader estimates. Motivated by this observation, we propose a simple data-driven protocol in which effective charges are optimized by comparing DFT reference energies with explicit Coulomb calculations, without additional NNP training. This strategy improves accuracy and transferability in DFT-level predictions of energies, forces, and stress. Together, these results provide a practical charge-selection framework for robust NNP development in ionic solids, enabling reliable simulation of polymorphic phase transformations and non-equilibrium dynamics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"276 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006146","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/s41524-026-01960-y
Dongbin Shin, Fabijan Pavošević, Nicolas Tancogne-Dejean, Michele Buzzi, Emil Viñas Boström, Angel Rubio
Recent studies of organic molecular solids have focused on their complex phase diagram and on light-induced phenomena, including a Mott insulating state, a spin liquid phase, and light-enhanced superconductivity. However, discrepancies between experiments and first-principles calculations for the κ-(BEDT-TTF)2X family hinder a comprehensive understanding of their properties. Here, we revisit the electronic structure of κ-(BEDT-TTF)2Cu2(CN)3 with a recently developed method for applying the Hubbard U potential on generalized orbital states, within the framework of density functional theory, to correct the orbital energy levels of the molecular solid. Our work focuses on the electronic structure of κ-(BEDT-TTF)2Cu2(CN)3, whose insulating state originates from an energy gap between the highest occupied and the lowest unoccupied molecular orbital states of the BEDT-TTF dimers, which constitute the periodic unit of the molecular solid. Our calculations provide results in alignment with experiments for band gaps, optical conductivities, and evolution of the metal-insulator transition as a function of pressure. Especially, the observed superconducting dome of κ-(BEDT-TTF)2Cu2(CN)3, which derives from the flat band state at the Fermi level, is qualitatively reproduced. Additionally, we construct a new low-energy lattice model based on our first-principles computed band structure that can be exploited to address many-body physics, such as quantum spin liquid states and double-holon dynamics. Our work can be extended to achieve deeper insight into the complex phase diagram and light-induced phenomena in the κ-(BEDT-TTF)2X family and other complex organic molecular solids.
{"title":"Origin of the insulating phase and metal-insulator transition in the organic molecular solid κ-(BEDT-TTF)2Cu2(CN)3","authors":"Dongbin Shin, Fabijan Pavošević, Nicolas Tancogne-Dejean, Michele Buzzi, Emil Viñas Boström, Angel Rubio","doi":"10.1038/s41524-026-01960-y","DOIUrl":"https://doi.org/10.1038/s41524-026-01960-y","url":null,"abstract":"Recent studies of organic molecular solids have focused on their complex phase diagram and on light-induced phenomena, including a Mott insulating state, a spin liquid phase, and light-enhanced superconductivity. However, discrepancies between experiments and first-principles calculations for the κ-(BEDT-TTF)2X family hinder a comprehensive understanding of their properties. Here, we revisit the electronic structure of κ-(BEDT-TTF)2Cu2(CN)3 with a recently developed method for applying the Hubbard U potential on generalized orbital states, within the framework of density functional theory, to correct the orbital energy levels of the molecular solid. Our work focuses on the electronic structure of κ-(BEDT-TTF)2Cu2(CN)3, whose insulating state originates from an energy gap between the highest occupied and the lowest unoccupied molecular orbital states of the BEDT-TTF dimers, which constitute the periodic unit of the molecular solid. Our calculations provide results in alignment with experiments for band gaps, optical conductivities, and evolution of the metal-insulator transition as a function of pressure. Especially, the observed superconducting dome of κ-(BEDT-TTF)2Cu2(CN)3, which derives from the flat band state at the Fermi level, is qualitatively reproduced. Additionally, we construct a new low-energy lattice model based on our first-principles computed band structure that can be exploited to address many-body physics, such as quantum spin liquid states and double-holon dynamics. Our work can be extended to achieve deeper insight into the complex phase diagram and light-induced phenomena in the κ-(BEDT-TTF)2X family and other complex organic molecular solids.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"22 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993495","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/s41524-025-01948-0
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}
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