Pub Date : 2026-03-05DOI: 10.1038/s41524-026-02021-0
Kyaw Hla Saing Chak, Bipin Bhattarai, Andrew C. Meng, Yijia Gu
Ferroelectric wurtzite ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}) shows significant promise due to its ferroelectric properties, scalability, and compatibility with semiconductor platforms. We develop an integrated thermodynamic modeling framework that couples CALPHAD, first-principles calculations, and Landau-Devonshire theory to predict phase stability and ferroelectric behavior in ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}). CALPHAD quantifies the solubility limit in wurtzite and delineates the critical phase boundary for supersaturation, offering insights into phase separation relevant for synthesis and processing. First-principles calculations provide composition-dependent structural, elastic, and ferroelectric properties, enabling parameterization of Landau-Devonshire ferroelectric model for wurtzite ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}) single crystals. Extending the framework to epitaxial thin films, we show how composition and biaxial strain jointly influence phase stability and room temperature functional properties. Large biaxial tensile strain stabilizes the wurtzite phase with high Mg content in thin films, unlike the equilibrium two-phase mixture with very limited Mg solubility. Meanwhile, tensile epitaxial strain reduces polarization but enhances dielectric and piezoelectric responses by driving a polar-to-nonpolar transition within the accessible composition range. Together, these results demonstrate that both chemical modification and strain engineering are essential for enabling and tuning ferroelectricity in ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}). Our unified approach establishes a comprehensive thermodynamic framework for the predictive design of strain-tunable wurtzite ferroelectrics.
{"title":"Integrated thermodynamic modeling of composition and strain tunable ferroelectricity in Wurtzite Zn1-xMgxO","authors":"Kyaw Hla Saing Chak, Bipin Bhattarai, Andrew C. Meng, Yijia Gu","doi":"10.1038/s41524-026-02021-0","DOIUrl":"https://doi.org/10.1038/s41524-026-02021-0","url":null,"abstract":"Ferroelectric wurtzite ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}) shows significant promise due to its ferroelectric properties, scalability, and compatibility with semiconductor platforms. We develop an integrated thermodynamic modeling framework that couples CALPHAD, first-principles calculations, and Landau-Devonshire theory to predict phase stability and ferroelectric behavior in ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}). CALPHAD quantifies the solubility limit in wurtzite and delineates the critical phase boundary for supersaturation, offering insights into phase separation relevant for synthesis and processing. First-principles calculations provide composition-dependent structural, elastic, and ferroelectric properties, enabling parameterization of Landau-Devonshire ferroelectric model for wurtzite ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}) single crystals. Extending the framework to epitaxial thin films, we show how composition and biaxial strain jointly influence phase stability and room temperature functional properties. Large biaxial tensile strain stabilizes the wurtzite phase with high Mg content in thin films, unlike the equilibrium two-phase mixture with very limited Mg solubility. Meanwhile, tensile epitaxial strain reduces polarization but enhances dielectric and piezoelectric responses by driving a polar-to-nonpolar transition within the accessible composition range. Together, these results demonstrate that both chemical modification and strain engineering are essential for enabling and tuning ferroelectricity in ({{rm{Zn}}}_{1{-}{rm{x}}}{{rm{Mg}}}_{{rm{x}}}{rm{O}}). Our unified approach establishes a comprehensive thermodynamic framework for the predictive design of strain-tunable wurtzite ferroelectrics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"54 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147351157","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}
Accurate prediction of shallow donor electron binding energies is critical for device modeling, dopant activation, and donor-based quantum technologies. Traditional beyond-DFT approaches are prohibitively expensive for the large supercells needed to capture the extended, hydrogenic wavefunctions, while semi-local DFT underestimates band gaps and suffers from delocalization errors. We present a simple protocol for shallow donors based on the DFT-1/2 approximate quasiparticle correction that maintains the computational cost of standard DFT and enables supercells up to thousands of atoms. This approach provides a straightforward and reproducible workflow that delivers reliable donor binding energies with minimal computational overhead. Applied to group-V donors in Si, the method yields binding energies in close agreement with experiment. We found that, for Si:Bi, it is essential to include spin-orbit coupling to achieve near-experimental values with a difference of only 4 meV. For arsenic, the method yields excellent agreement with experiment, with a difference of only 0.3 meV. For antimony, the results match experiment to within 5 meV, and for phosphorus, the deviation is within 8 meV. To demonstrate its generality, we further validate the methodology by applying it to hydrogen donors in ZnO, confirming its broad applicability to semiconductor systems.
{"title":"An accurate DFT-1/2 approach for shallow defect states: efficient calculation of donor binding energies in silicon","authors":"Joshua Claes, Bart Partoens, Dirk Lamoen, Marcelo Marques, Lara K. Teles","doi":"10.1038/s41524-026-02003-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02003-2","url":null,"abstract":"Accurate prediction of shallow donor electron binding energies is critical for device modeling, dopant activation, and donor-based quantum technologies. Traditional beyond-DFT approaches are prohibitively expensive for the large supercells needed to capture the extended, hydrogenic wavefunctions, while semi-local DFT underestimates band gaps and suffers from delocalization errors. We present a simple protocol for shallow donors based on the DFT-1/2 approximate quasiparticle correction that maintains the computational cost of standard DFT and enables supercells up to thousands of atoms. This approach provides a straightforward and reproducible workflow that delivers reliable donor binding energies with minimal computational overhead. Applied to group-V donors in Si, the method yields binding energies in close agreement with experiment. We found that, for Si:Bi, it is essential to include spin-orbit coupling to achieve near-experimental values with a difference of only 4 meV. For arsenic, the method yields excellent agreement with experiment, with a difference of only 0.3 meV. For antimony, the results match experiment to within 5 meV, and for phosphorus, the deviation is within 8 meV. To demonstrate its generality, we further validate the methodology by applying it to hydrogen donors in ZnO, confirming its broad applicability to semiconductor systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"54 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346802","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 : 2026-03-04DOI: 10.1038/s41524-026-01962-w
Niraj K. Nepal, Lin-Lin Wang
We present a workflow that iteratively combines ab-initio calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which have been largely overlooked in previous studies. Electron-phonon coupling (EPC) properties and critical temperature (Tc) of 417 boron, carbon, and borocarbide compounds have been calculated with density functional perturbation theory (DFPT) and isotropic Eliashberg approximation. Our study addresses Tc convergence of Brillouin zone sampling with an ansatz test, stabilizing imaginary phonon modes for significant EPC contributions, and comparing the performance of two ML models, especially when including compounds of dynamical instability. We predict a few promising superconducting compounds with formation energy just above the ground state convex hull, such as Ca5B3N6 (35 K), TaNbC2 (28.4 K), Nb3B3C (16.4 K), Y2B3C2 (4.0 K), Pd3CaB (7.0 K), MoRuB2 (15.6 K), RuVB2 (15.0 K), RuSc3C4 (6.6 K) among others.
{"title":"Machine-learning guided search for phonon-mediated superconductivity in boron and carbon compounds","authors":"Niraj K. Nepal, Lin-Lin Wang","doi":"10.1038/s41524-026-01962-w","DOIUrl":"https://doi.org/10.1038/s41524-026-01962-w","url":null,"abstract":"We present a workflow that iteratively combines ab-initio calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which have been largely overlooked in previous studies. Electron-phonon coupling (EPC) properties and critical temperature (Tc) of 417 boron, carbon, and borocarbide compounds have been calculated with density functional perturbation theory (DFPT) and isotropic Eliashberg approximation. Our study addresses Tc convergence of Brillouin zone sampling with an ansatz test, stabilizing imaginary phonon modes for significant EPC contributions, and comparing the performance of two ML models, especially when including compounds of dynamical instability. We predict a few promising superconducting compounds with formation energy just above the ground state convex hull, such as Ca5B3N6 (35 K), TaNbC2 (28.4 K), Nb3B3C (16.4 K), Y2B3C2 (4.0 K), Pd3CaB (7.0 K), MoRuB2 (15.6 K), RuVB2 (15.0 K), RuSc3C4 (6.6 K) among others.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"130 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346803","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 : 2026-03-03DOI: 10.1038/s41524-026-02011-2
Aleksandr Poliukhin, Nicola Colonna, Francesco Libbi, Samuel Poncé, Nicola Marzari
Electron-phonon coupling is a key interaction that governs diverse physical processes such as carrier transport, superconductivity, and optical absorption. Calculating such interactions from first-principles with methods beyond density-functional theory remains a challenge. We introduce here a finite-difference framework for computing electron-phonon couplings for any electronic structure method that provides eigenvalues and eigenvectors, and showcase applications for hybrid and Koopmans functionals, and GW many-body perturbation theory. Our approach introduces a novel projectability scheme based on eigenvalue differences and bypasses many of the limitations of the direct finite difference methods. It also leverages symmetries to reduce the number of independent atomic displacements, decreasing overall computational cost. This approach enables seamless integration with established first-principles codes for generating displaced supercells, performing Wannier interpolations, and evaluating transport properties. Applications to silicon and gallium arsenide show that advanced electronic-structure functionals predict different electron-phonon couplings and modify band curvatures, resulting in much more accurate estimates of intrinsic carrier drift mobilities and effective masses. In general, our method provides a robust and accessible framework for calculating the electron-phonon properties with state-of-the-art beyond DFT methods.
{"title":"Carrier mobilities and electron-phonon interactions beyond DFT","authors":"Aleksandr Poliukhin, Nicola Colonna, Francesco Libbi, Samuel Poncé, Nicola Marzari","doi":"10.1038/s41524-026-02011-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02011-2","url":null,"abstract":"Electron-phonon coupling is a key interaction that governs diverse physical processes such as carrier transport, superconductivity, and optical absorption. Calculating such interactions from first-principles with methods beyond density-functional theory remains a challenge. We introduce here a finite-difference framework for computing electron-phonon couplings for any electronic structure method that provides eigenvalues and eigenvectors, and showcase applications for hybrid and Koopmans functionals, and GW many-body perturbation theory. Our approach introduces a novel projectability scheme based on eigenvalue differences and bypasses many of the limitations of the direct finite difference methods. It also leverages symmetries to reduce the number of independent atomic displacements, decreasing overall computational cost. This approach enables seamless integration with established first-principles codes for generating displaced supercells, performing Wannier interpolations, and evaluating transport properties. Applications to silicon and gallium arsenide show that advanced electronic-structure functionals predict different electron-phonon couplings and modify band curvatures, resulting in much more accurate estimates of intrinsic carrier drift mobilities and effective masses. In general, our method provides a robust and accessible framework for calculating the electron-phonon properties with state-of-the-art beyond DFT methods.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346808","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 : 2026-03-03DOI: 10.1038/s41524-026-01996-0
Lu Chen, Yunjia Huang, Hanyue Zhang, Ruoyu Li, Hui Mei, Junqin Shi, Zhe Liu, Feng Zhou, Weimin Liu, Xiaoli Fan
Searching for superlubric heterostructures composed of transitional metal dichalcogenides monolayers is challenging due to the variety of constituent elements. In this study, a two-step machine learning approach based on domain features is employed to efficiently tackle this challenging task. Machine learning models are trained to predict complex domain features from structural features. Bayesian optimization is then used to search for superlubricants. Machine learning models are iteratively rechained based on a small number of high-accuracy calculations, saving computational time and ensuring accuracy. MoS2/WS2, MoS2/VS2, and NiS2/NbSSe heterostructures have been identified as superlubric heterostructures and confirmed through theoretical calculations. Under 1 ~ 5 N, the experimental friction coefficients at the interface of MoS2/WS2 are 12% ~ 36% lower compared to MoS2/MoSe2, which has previously been proven to exhibit superlubricity. These results validate the effectiveness of the two-step machine learning approach in searching for superlubric heterostructures in a significantly reduced time.
{"title":"Domain features-informed two-step machine learning: accelerating the search for superlubric heterostructures","authors":"Lu Chen, Yunjia Huang, Hanyue Zhang, Ruoyu Li, Hui Mei, Junqin Shi, Zhe Liu, Feng Zhou, Weimin Liu, Xiaoli Fan","doi":"10.1038/s41524-026-01996-0","DOIUrl":"https://doi.org/10.1038/s41524-026-01996-0","url":null,"abstract":"Searching for superlubric heterostructures composed of transitional metal dichalcogenides monolayers is challenging due to the variety of constituent elements. In this study, a two-step machine learning approach based on domain features is employed to efficiently tackle this challenging task. Machine learning models are trained to predict complex domain features from structural features. Bayesian optimization is then used to search for superlubricants. Machine learning models are iteratively rechained based on a small number of high-accuracy calculations, saving computational time and ensuring accuracy. MoS2/WS2, MoS2/VS2, and NiS2/NbSSe heterostructures have been identified as superlubric heterostructures and confirmed through theoretical calculations. Under 1 ~ 5 N, the experimental friction coefficients at the interface of MoS2/WS2 are 12% ~ 36% lower compared to MoS2/MoSe2, which has previously been proven to exhibit superlubricity. These results validate the effectiveness of the two-step machine learning approach in searching for superlubric heterostructures in a significantly reduced time.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"19 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346805","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 : 2026-03-03DOI: 10.1038/s41524-026-02030-z
Xuhong Li, Mo Zhou, Yongteng Wei, Tengfei Cao, Xiaoli Fan
Magnetic skyrmions hold great promise as information carriers in spintronics, yet their practical implementation is impeded by the skyrmion Hall effect (SkHE). Combining symmetry analysis, first-principles calculations, and atomic spin simulations, we demonstrate hybrid skyrmions in the multiferroic monolayer TcIrGe2Se6. It hosts a mixed Dzyaloshinskii-Moriya interaction (DMI) containing both parallel and perpendicular components. This unique DMI stabilizes hybrid skyrmions exhibiting a nearly vanishing skyrmion Hall angle, thereby suppressing the SkHE. Benefiting from strong DMI and high Curie temperature (330 K), these hybrid skyrmions maintain stable across a wide temperature and magnetic field range. The intrinsic magnetoelectric coupling enables electrical control of skyrmion chirality and current-driven motion through ferroelectric switching, while strain engineering permits continuous helicity modulation and induces a topological transition to bimerons. Our work establishes TcIrGe2Se6 as a promising platform for hybrid skyrmions and provides a multimodal control scheme, integrating electrical switching for chirality and strain engineering for helicity.
{"title":"Hybrid magnetic skyrmions with near-zero Hall angle and electrical switchability in a 2D multiferroic","authors":"Xuhong Li, Mo Zhou, Yongteng Wei, Tengfei Cao, Xiaoli Fan","doi":"10.1038/s41524-026-02030-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02030-z","url":null,"abstract":"Magnetic skyrmions hold great promise as information carriers in spintronics, yet their practical implementation is impeded by the skyrmion Hall effect (SkHE). Combining symmetry analysis, first-principles calculations, and atomic spin simulations, we demonstrate hybrid skyrmions in the multiferroic monolayer TcIrGe2Se6. It hosts a mixed Dzyaloshinskii-Moriya interaction (DMI) containing both parallel and perpendicular components. This unique DMI stabilizes hybrid skyrmions exhibiting a nearly vanishing skyrmion Hall angle, thereby suppressing the SkHE. Benefiting from strong DMI and high Curie temperature (330 K), these hybrid skyrmions maintain stable across a wide temperature and magnetic field range. The intrinsic magnetoelectric coupling enables electrical control of skyrmion chirality and current-driven motion through ferroelectric switching, while strain engineering permits continuous helicity modulation and induces a topological transition to bimerons. Our work establishes TcIrGe2Se6 as a promising platform for hybrid skyrmions and provides a multimodal control scheme, integrating electrical switching for chirality and strain engineering for helicity.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"130 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346807","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 : 2026-03-03DOI: 10.1038/s41524-026-01968-4
Federico Orlando, Andrea Droghetti, Lorenzo Varrassi, Giuseppe Cuono, Cesare Franchini, Paolo Barone, Antimo Marrazzo, Marco Gibertini, Srdjan Stavrić, Silvia Picozzi
Two-dimensional (2D) magnets host a wide range of exotic magnetic textures, whose low-energy excitations and finite-temperature properties are typically described by effective spin models based on Heisenberg-like Hamiltonians. A key challenge in this framework is the reliable determination, from ab initio calculations, of exchange parameters and their anisotropic components, crucial for stabilising long-range order. Among the strategies proposed for this task, the energy-mapping method, based on total-energy calculations within Density Functional Theory (DFT), is the most widely adopted, but typically requires laborious, multi-step procedures. To overcome this limitation, we introduce AMaRaNTA (Automating Magnetic paRAmeters iN a Tensorial Approach), a computational package that systematically automates the energy-mapping method, through its “four-state” formulation, to extract exchange and anisotropy parameters in 2D magnets. In its current implementation, AMaRaNTA returns the nearest-neighbour exchange tensor, complemented by scalar parameters for second- and third-nearest-neighbour exchange interactions as well as single-ion anisotropy. Together, these provide a minimal yet sufficient set of parameters to capture magnetic frustration and anisotropies, essential for stabilising several observed magnetic states in 2D materials. Applied to a representative subset of the Materials Cloud 2D Structure database, AMaRaNTA demonstrates robust and reproducible screening of magnetic interactions, with clear potential for high-throughput simulations.
二维（2D）磁体拥有广泛的奇异磁织构，其低能激发和有限温度性质通常由基于类海森堡哈密顿量的有效自旋模型描述。在这个框架中，一个关键的挑战是可靠地确定交换参数及其各向异性成分，从从头开始计算，这对稳定远程秩序至关重要。在为这项任务提出的策略中，基于密度泛函理论（DFT）中的总能量计算的能量映射方法是最广泛采用的，但通常需要费力的多步骤过程。为了克服这一限制，我们引入了AMaRaNTA (Automating Magnetic paRAmeters iN a Tensorial Approach)，这是一个计算包，通过其“四态”公式系统地自动化能量映射方法，以提取二维磁体中的交换和各向异性参数。在目前的实现中，AMaRaNTA返回最近邻交换张量，并补充了第二和第三近邻交换相互作用以及单离子各向异性的标量参数。总之，这些提供了一组最小但足够的参数来捕获磁挫折和各向异性，这对于稳定二维材料中几种观察到的磁状态至关重要。应用于Materials Cloud 2D Structure数据库的代表性子集，AMaRaNTA展示了强大的、可重复的磁相互作用筛选，具有高通量模拟的明显潜力。
{"title":"AMaRaNTA: automated first-principles exchange parameters in 2D magnets","authors":"Federico Orlando, Andrea Droghetti, Lorenzo Varrassi, Giuseppe Cuono, Cesare Franchini, Paolo Barone, Antimo Marrazzo, Marco Gibertini, Srdjan Stavrić, Silvia Picozzi","doi":"10.1038/s41524-026-01968-4","DOIUrl":"https://doi.org/10.1038/s41524-026-01968-4","url":null,"abstract":"Two-dimensional (2D) magnets host a wide range of exotic magnetic textures, whose low-energy excitations and finite-temperature properties are typically described by effective spin models based on Heisenberg-like Hamiltonians. A key challenge in this framework is the reliable determination, from ab initio calculations, of exchange parameters and their anisotropic components, crucial for stabilising long-range order. Among the strategies proposed for this task, the energy-mapping method, based on total-energy calculations within Density Functional Theory (DFT), is the most widely adopted, but typically requires laborious, multi-step procedures. To overcome this limitation, we introduce AMaRaNTA (Automating Magnetic paRAmeters iN a Tensorial Approach), a computational package that systematically automates the energy-mapping method, through its “four-state” formulation, to extract exchange and anisotropy parameters in 2D magnets. In its current implementation, AMaRaNTA returns the nearest-neighbour exchange tensor, complemented by scalar parameters for second- and third-nearest-neighbour exchange interactions as well as single-ion anisotropy. Together, these provide a minimal yet sufficient set of parameters to capture magnetic frustration and anisotropies, essential for stabilising several observed magnetic states in 2D materials. Applied to a representative subset of the Materials Cloud 2D Structure database, AMaRaNTA demonstrates robust and reproducible screening of magnetic interactions, with clear potential for high-throughput simulations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"61 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346834","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 : 2026-03-02DOI: 10.1038/s41524-026-01993-3
Mingyu Liu, Zian Mao, Zhu Liu, Jintao Guo, Haoran Zhang, Xi Huang, Chun Cheng, Jun Ding, Jian Hui, Shufen Chu, Xiaoqin Zeng, Yujun Xie
Four-dimensional scanning transmission electron microscopy (4D-STEM) is a high-throughput automated data acquisition technique with great potential for real-time data collection and analysis in automated STEM. However, its practical implementation is limited by challenges in data preprocessing, which hinder the timely and accurate interpretation of the large amounts of data it generates. Issues like pervasive noise, beam center drift, and elliptical distortions during high-throughput acquisition inevitably degrade diffraction patterns, leading to systematic errors in quantitative measurements. Conventional calibration algorithms are often material-specific and fail to provide a robust, generalizable solution. In this work, we introduce 4D-PreNet, an end-to-end deep-learning pipeline that integrates attention-enhanced U-Net and ResNet architectures to simultaneously perform denoising, center calibration, and ellipse calibration. The network is trained on extensive simulated datasets that cover a broad range of noise levels, drift magnitudes, and distortion types, thereby enabling generalization to experimental data obtained under different acquisition conditions. Quantitative evaluations demonstrate that 4D-PreNet reduces mean squared error by up to 50% in denoising and achieves sub-pixel center localization with average errors below 0.04 pixels. Compared to conventional algorithms, 4D-PreNet shows improved noise suppression and accurate restoration of diffraction features, enabling reliable real-time analysis of 4D-STEM data and supporting automated STEM workflows.
{"title":"A Unified preprocessing framework for high-throughput diffraction pattern analysis","authors":"Mingyu Liu, Zian Mao, Zhu Liu, Jintao Guo, Haoran Zhang, Xi Huang, Chun Cheng, Jun Ding, Jian Hui, Shufen Chu, Xiaoqin Zeng, Yujun Xie","doi":"10.1038/s41524-026-01993-3","DOIUrl":"https://doi.org/10.1038/s41524-026-01993-3","url":null,"abstract":"Four-dimensional scanning transmission electron microscopy (4D-STEM) is a high-throughput automated data acquisition technique with great potential for real-time data collection and analysis in automated STEM. However, its practical implementation is limited by challenges in data preprocessing, which hinder the timely and accurate interpretation of the large amounts of data it generates. Issues like pervasive noise, beam center drift, and elliptical distortions during high-throughput acquisition inevitably degrade diffraction patterns, leading to systematic errors in quantitative measurements. Conventional calibration algorithms are often material-specific and fail to provide a robust, generalizable solution. In this work, we introduce 4D-PreNet, an end-to-end deep-learning pipeline that integrates attention-enhanced U-Net and ResNet architectures to simultaneously perform denoising, center calibration, and ellipse calibration. The network is trained on extensive simulated datasets that cover a broad range of noise levels, drift magnitudes, and distortion types, thereby enabling generalization to experimental data obtained under different acquisition conditions. Quantitative evaluations demonstrate that 4D-PreNet reduces mean squared error by up to 50% in denoising and achieves sub-pixel center localization with average errors below 0.04 pixels. Compared to conventional algorithms, 4D-PreNet shows improved noise suppression and accurate restoration of diffraction features, enabling reliable real-time analysis of 4D-STEM data and supporting automated STEM workflows.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"69 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346806","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 : 2026-02-28DOI: 10.1038/s41524-026-02015-y
Chenlei Xu, Tianhao Su, Jie Xiong, Yue Wu, Shuya Dong, Tian Jiang, Mengwei He, Shuai Chen, Tong-Yi Zhang
Accurate crystal structure determination underpins materials discovery, yet powder X-ray diffraction (XRD) analysis still depends on expert-driven, iterative fitting that limits scalability for high-throughput and autonomous experiments. We introduce XRD-Crystal Contrastive Pretraining (XCCP), a physics-guided contrastive learning framework that aligns PXRD patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry inference. XCCP employs a dual-expert XRD encoder with a Kolmogorov-Arnold Network (KAN) projection head. A low-angle branch captures long-length-scale signatures, while a wide-angle branch encodes dense, symmetry-governed fingerprints. Attribution and perturbation analyses show that the KAN head concentrates evidence on physically meaningful Bragg reflections rather than background-dominated regions, improving robustness to peak-shape variations. We further introduce similarity-based confidence scores to flag potentially unreliable predictions in open-set settings. Without elemental priors, XCCP achieves 46.42% top-1 accuracy for structure retrieval and 60.85% accuracy for space-group identification. When chemical composition is available for elemental pre-screening, performance increases to 88.98% and 93.39%, respectively. XCCP also generalizes to compositionally similar multi-principal element alloys and enables zero-shot transfer to experimental patterns. These results establish XCCP as an interpretable, confidence-aware, and scalable paradigm for XRD analysis, enabling high-throughput screening, rapid candidate shortlisting, and integration with autonomous laboratory workflows.
{"title":"KAN-enhanced contrastive learning: the accelerator of crystal structure identification from XRD patterns","authors":"Chenlei Xu, Tianhao Su, Jie Xiong, Yue Wu, Shuya Dong, Tian Jiang, Mengwei He, Shuai Chen, Tong-Yi Zhang","doi":"10.1038/s41524-026-02015-y","DOIUrl":"https://doi.org/10.1038/s41524-026-02015-y","url":null,"abstract":"Accurate crystal structure determination underpins materials discovery, yet powder X-ray diffraction (XRD) analysis still depends on expert-driven, iterative fitting that limits scalability for high-throughput and autonomous experiments. We introduce XRD-Crystal Contrastive Pretraining (XCCP), a physics-guided contrastive learning framework that aligns PXRD patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry inference. XCCP employs a dual-expert XRD encoder with a Kolmogorov-Arnold Network (KAN) projection head. A low-angle branch captures long-length-scale signatures, while a wide-angle branch encodes dense, symmetry-governed fingerprints. Attribution and perturbation analyses show that the KAN head concentrates evidence on physically meaningful Bragg reflections rather than background-dominated regions, improving robustness to peak-shape variations. We further introduce similarity-based confidence scores to flag potentially unreliable predictions in open-set settings. Without elemental priors, XCCP achieves 46.42% top-1 accuracy for structure retrieval and 60.85% accuracy for space-group identification. When chemical composition is available for elemental pre-screening, performance increases to 88.98% and 93.39%, respectively. XCCP also generalizes to compositionally similar multi-principal element alloys and enables zero-shot transfer to experimental patterns. These results establish XCCP as an interpretable, confidence-aware, and scalable paradigm for XRD analysis, enabling high-throughput screening, rapid candidate shortlisting, and integration with autonomous laboratory workflows.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"57 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147320183","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}
Determining the number of constituent atoms in metallic nanoclusters (NCs) directly from imaging is key to understanding how atomicity governs their size-dependent properties. Scanning transmission electron microscopy (STEM), which captures real-space images of materials with tunable magnification down to the atomic scale, provides an invaluable means to probe such structures. However, despite these advantages, automated and accurate identification of NC atomicity remains challenging, requiring robust extraction of features such as projected shape and contrast distribution from imaging data. To address this challenge, we present a deep learning framework that classifies platinum NCs (Ptn; n = 19, 30, 41, 55, 70) using high-resolution aberration-corrected STEM images. A convolutional neural network extracts structural features that are separable in UMAP (Uniform Manifold Approximation and Projection) space, with class-specific focus visualized using Grad-CAM (Gradient-weighted Class Activation Mapping). The model achieves high accuracy, even for mixed-atomicity samples (n = 19, 41, 70) on a shared substrate. To address domain shift, we apply fine-tuning with high-confidence pseudo-labels, significantly recovering performance. A dual-channel model integrating Local Contrast Normalization (LCN) filtering achieves a coefficient of determination of R² = 0.94 ± 0.03, outperforming size-based classification. This framework automates atomic-scale classification from STEM images and advances autonomous workflows via real-time analysis and machine learning based decisions.
直接从成像中确定金属纳米团簇（nc）中组成原子的数量是理解原子性如何控制其尺寸依赖特性的关键。扫描透射电子显微镜（STEM）可以捕获材料的真实空间图像，其放大倍数可调至原子尺度，为探测此类结构提供了宝贵的手段。然而，尽管有这些优势，自动准确识别NC原子性仍然具有挑战性，需要从成像数据中提取投影形状和对比度分布等特征。为了应对这一挑战，我们提出了一个深度学习框架，该框架使用高分辨率像差校正的STEM图像对白金nc （Ptn; n = 19、30、41、55、70）进行分类。卷积神经网络提取UMAP（均匀流形逼近和投影）空间中可分离的结构特征，使用梯度加权类激活映射（Gradient-weighted Class Activation Mapping）将特定类的焦点可视化。即使对于共享衬底上的混合原子性样品（n = 19,41,70），该模型也具有很高的精度。为了解决领域转移问题，我们使用高置信度伪标签进行微调，显著恢复了性能。结合局部对比度归一化（LCN）滤波的双通道模型的决定系数R²= 0.94±0.03，优于基于尺寸的分类。该框架可以从STEM图像中自动进行原子尺度的分类，并通过实时分析和基于机器学习的决策推进自主工作流程。
{"title":"Interpretable deep learning for atomicity classification of platinum nanoclusters in STEM images","authors":"Keizo Tsukamoto, Naoyuki Hirata, Masahide Tona, Atsushi Nakajima","doi":"10.1038/s41524-026-02014-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02014-z","url":null,"abstract":"Determining the number of constituent atoms in metallic nanoclusters (NCs) directly from imaging is key to understanding how atomicity governs their size-dependent properties. Scanning transmission electron microscopy (STEM), which captures real-space images of materials with tunable magnification down to the atomic scale, provides an invaluable means to probe such structures. However, despite these advantages, automated and accurate identification of NC atomicity remains challenging, requiring robust extraction of features such as projected shape and contrast distribution from imaging data. To address this challenge, we present a deep learning framework that classifies platinum NCs (Ptn; n = 19, 30, 41, 55, 70) using high-resolution aberration-corrected STEM images. A convolutional neural network extracts structural features that are separable in UMAP (Uniform Manifold Approximation and Projection) space, with class-specific focus visualized using Grad-CAM (Gradient-weighted Class Activation Mapping). The model achieves high accuracy, even for mixed-atomicity samples (n = 19, 41, 70) on a shared substrate. To address domain shift, we apply fine-tuning with high-confidence pseudo-labels, significantly recovering performance. A dual-channel model integrating Local Contrast Normalization (LCN) filtering achieves a coefficient of determination of R² = 0.94 ± 0.03, outperforming size-based classification. This framework automates atomic-scale classification from STEM images and advances autonomous workflows via real-time analysis and machine learning based decisions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147320184","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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