npj Computational Materials最新文献_第10页

Superconductivity of metastable dihydrides at ambient pressure 环境压力下可蜕变二酸酐的超导性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-09 DOI: 10.1038/s41524-024-01359-7

Heejung Kim, Ina Park, J. H. Shim, D. Y. Kim

{"title":"Superconductivity of metastable dihydrides at ambient pressure","authors":"Heejung Kim, Ina Park, J. H. Shim, D. Y. Kim","doi":"10.1038/s41524-024-01359-7","DOIUrl":"https://doi.org/10.1038/s41524-024-01359-7","url":null,"abstract":"Hydrogen in metals is a significant research area with far-reaching implications, encompassing diverse fields such as hydrogen storage, metal-insulator transitions, and the recently emerging phenomenon of room-temperature superconductivity under high pressure. Hydrogen atoms pose challenges in experiments as they are nearly invisible, and they are considered within ideal crystalline structures in theoretical predictions, which hampers research on the formation of metastable hydrides. Here, we propose pressure-induced hydrogen migration from tetrahedral (T-) site to octahedral (O-) site, forming ({{rm{LaH}}}_{x}^{{rm{O}}}{{rm{H}}}_{2-x}^{{rm{T}}}) in cubic LaH2. Under decompression, it retains ({{rm{H}}}_{x}^{{rm{O}}}) occupancy, and is dynamically stable even at ambient pressure, enabling a synthesis route of metastable dihydrides via compression-decompression process. We predict that the electron-phonon coupling strength of ({{rm{LaH}}}_{x}^{{rm{O}}}{{rm{H}}}_{2-x}^{{rm{T}}}) is enhanced with increasing x, and the associated Tc reaches up to 10.8 K at ambient pressure. Furthermore, we calculated stoichiometric hydrogen migration threshold pressure (Pc) for various lanthanides dihydrides (RH2, where R = Y, Sc, Nd, and Lu), and found an inversely linear relation between Pc and ionic radii of R. We propose that the highest Tc in the face-centered-cubic dihydride system can be realized by optimizing the O/T-site occupancies.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"56 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Magnetic order in the computational 2D materials database (C2DB) from high throughput spin spiral calculations 通过高通量自旋螺旋计算获得计算二维材料数据库 (C2DB) 中的磁序

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-04 DOI: 10.1038/s41524-024-01318-2

Joachim Sødequist, Thomas Olsen

引用次数: 0

Unsupervised learning-aided extrapolation for accelerated design of superalloys 无监督学习辅助推断法加速超合金设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-04 DOI: 10.1038/s41524-024-01358-8

Weijie Liao, Ruihao Yuan, Xiangyi Xue, Jun Wang, Jinshan Li, Turab Lookman

{"title":"Unsupervised learning-aided extrapolation for accelerated design of superalloys","authors":"Weijie Liao, Ruihao Yuan, Xiangyi Xue, Jun Wang, Jinshan Li, Turab Lookman","doi":"10.1038/s41524-024-01358-8","DOIUrl":"https://doi.org/10.1038/s41524-024-01358-8","url":null,"abstract":"Machine learning has been widely used to guide the search for new materials by learning the patterns underlying available data. However, the pure prediction-oriented search is often biased to interpolation due to the limited data in a large unexplored space. Here we present a sampling framework towards extrapolation, that integrates unsupervised clustering, interpretable analysis, and similarity evaluation to sample target candidates with improved properties from a vast search space. Using the design of superalloys with improved ({gamma }^{{prime} })-phase solvus temperature (({T}_{{gamma }^{{prime} }})) as a model case, we start with sparse data, and by a few experiments, we find nine new superalloys with chemistries distinct to those in the training data. Three of them show improved ({T}_{{gamma }^{{prime} }}) by about 50 °C, a large enhancement for superalloys. Moreover, we find two features characterizing mismatch in atomic size and mixing enthalpy linearly effect. This work demonstrates the capability of unsupervised learning to search for new materials when limited data is available.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"82 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy 用于快速扫描透射电子显微镜的支撑纳米粒子中原子柱的定位和分割

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-03 DOI: 10.1038/s41524-024-01360-0

Henrik Eliasson, Rolf Erni

引用次数: 0

Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys 机器学习的二卤化过渡金属 Mo1-xWxS2-2ySe2y 合金原子间电位

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-03 DOI: 10.1038/s41524-024-01357-9

Anas Siddiqui, Nicholas D. M. Hine

引用次数: 0

Atomic resolution coherent x-ray imaging with physics-based phase retrieval 基于物理相位检索的原子分辨率相干 X 射线成像

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-02 DOI: 10.1038/s41524-024-01340-4

Jason Meziere, Abigail Hardy Carpenter, Anastasios Pateras, Ross Harder, Richard L. Sandberg

{"title":"Atomic resolution coherent x-ray imaging with physics-based phase retrieval","authors":"Jason Meziere, Abigail Hardy Carpenter, Anastasios Pateras, Ross Harder, Richard L. Sandberg","doi":"10.1038/s41524-024-01340-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01340-4","url":null,"abstract":"Coherent x-ray imaging and scattering from accelerator based sources such as synchrotrons continue to impact biology, medicine, technology, and materials science. Many synchrotrons around the world are currently undergoing major upgrades to increase their available coherent x-ray flux by approximately two orders of magnitude. The improvement of synchrotrons may enable imaging of materials in operando at the atomic scale which may revolutionize battery and catalysis technologies. Current algorithms used for phase retrieval in coherent x-ray imaging are based on the projection onto sets method. These traditional iterative phase retrieval methods will become more computationally expensive as they push towards atomic resolution and may struggle to converge. Additionally, these methods do not incorporate physical information that may additionally constrain the solution. In this work, we present an algorithm which incorporates molecular dynamics into Bragg coherent diffraction imaging (BCDI). This algorithm, which we call PRAMMol (Phase Retrieval with Atomic Modeling and Molecular Dynamics) combines statistical techniques with molecular dynamics to solve the phase retrieval problem. We present several examples where our algorithm is applied to simulated coherent diffraction from 3D crystals and show convergence to the correct solution at the atomic scale.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"78 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141877672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting 高效探索设计空间的小型数据集机器学习方法：用于水分离的 ZnTe 基高熵合金工程学

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-07-30 DOI: 10.1038/s41524-024-01341-3

Seung-Hyun Victor Oh, Su-Hyun Yoo, Woosun Jang

{"title":"Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting","authors":"Seung-Hyun Victor Oh, Su-Hyun Yoo, Woosun Jang","doi":"10.1038/s41524-024-01341-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01341-3","url":null,"abstract":"Aiming toward a sustainable energy era, the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied. One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying. However, the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space, providing an opportunity for machine learning (ML) approaches to help accelerate the discovery of new multicomponent alloy materials. A conventional prerequisite for ML approaches is a large database of accurate material properties, which may require exhaustive computational and/or experimental resources. This study demonstrates that the screening of solid-solution alloys (up to hexanary systems) can be performed using a small database to minimize (and optimize) the number of high-level computational calculations. Specifically, we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method (α-method). Furthermore, we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"45 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM) 用于高分辨率透射电子显微镜（HRTEM）机器学习的强大合成数据生成框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-07-29 DOI: 10.1038/s41524-024-01336-0

Luis Rangel DaCosta, Katherine Sytwu, C. K. Groschner, M. C. Scott

{"title":"A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)","authors":"Luis Rangel DaCosta, Katherine Sytwu, C. K. Groschner, M. C. Scott","doi":"10.1038/s41524-024-01336-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01336-0","url":null,"abstract":"Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"117 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141790938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian blacksmithing: discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys 贝叶斯铁匠技术：发现高熵难熔合金的热机械性能和变形机制

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-07-28 DOI: 10.1038/s41524-024-01353-z

Jacob Startt, Megan J. McCarthy, Mitchell A. Wood, Sean Donegan, Rémi Dingreville

{"title":"Bayesian blacksmithing: discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys","authors":"Jacob Startt, Megan J. McCarthy, Mitchell A. Wood, Sean Donegan, Rémi Dingreville","doi":"10.1038/s41524-024-01353-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01353-z","url":null,"abstract":"Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between elements. This study introduces a multi-objective Bayesian optimization approach guiding molecular dynamics simulations for discovering high-performance refractory alloys with both targeted intrinsic static thermomechanical properties and also deformation mechanisms occurring during dynamic loading. The objective functions are aiming for excellent thermomechanical stability via a high bulk modulus, a low thermal expansion, a high heat capacity, and for a resilient deformation mechanism maximizing the retention of the BCC phase after shock loading. Contrasting two optimization procedures, we show that the Pareto-optimal solutions are confined to a small performance space when the property objectives display a cooperative relationship. Conversely, the Pareto front is much broader in the performance space when these properties have antagonistic relationships. Density functional theory simulations validate these findings and unveil underlying atomic-bond changes driving property improvements.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"34 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phonon-mediated unconventional superconductivity in rhombohedral stacked multilayer graphene 斜方体堆叠多层石墨烯中声子介导的非常规超导性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-07-27 DOI: 10.1038/s41524-024-01345-z

Emil Viñas Boström, Ammon Fischer, Jonas B. Profe, Jin Zhang, Dante M. Kennes, Angel Rubio

{"title":"Phonon-mediated unconventional superconductivity in rhombohedral stacked multilayer graphene","authors":"Emil Viñas Boström, Ammon Fischer, Jonas B. Profe, Jin Zhang, Dante M. Kennes, Angel Rubio","doi":"10.1038/s41524-024-01345-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01345-z","url":null,"abstract":"Understanding the origin of superconductivity in correlated two-dimensional materials is a key step in leveraging material engineering techniques for next-generation nanoscale devices. While it is widely accepted that phonons fluctuations only mediate conventional (s-wave) superconductivity, the common phenomenology of superconductivity in Bernal bilayer and rhombohedral trilayer graphene, as well as in a large family of graphene-based moiré systems, suggests a common superconducting mechanism across these platforms. In particular, in all these platforms some superconducting regions violate the Pauli limit, indicating unconventional superconductivity, naively ruling out conventional phonon-mediated pairing as the underlying mechanism. Here we combine first principles simulations with effective low-energy theories to investigate the superconducting mechanism and pairing symmetry in rhombohedral stacked graphene multilayers. We find that phonon-mediated superconductivity explains the main experimental findings, namely the displacement field and doping level dependence of the critical temperature, and the presence of two superconducting regions with different pairing symmetries that depend on the parent normal state. In particular, we find that intra-valley phonon scattering favors a triplet f-wave pairing when combined with electronic correlations stabilizing a spin- and valley-polarized normal state. We also propose a so far unexplored superconducting region at higher hole doping densities nh ≈ 4 × 1012 cm−2, and demonstrate how this highly hole-doped regime can be reached in heterostructures consisting of monolayer α-RuCl3 and rhombohedral trilayer graphene. Our findings promote phonon-mediated pairing as a strong contender to explain superconductivity across a wide range of graphene platforms, and demonstrate that phonons can, in fact, stabilize unconventional superconducting orders.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0