检索结果-南通市图书馆

Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2021年第1期7卷 731-738页

作者： Pierre-Paul De Breuck Geoffroy Hautier gian-marco rignanese UCLouvain Institute of Condensed Matter and Nanosciences(IMCN)Louvain-la-NeuveBelgium Dartmouth College Thayer School of EngineeringHanoverNHUSA

In order to make accurate predictions of material properties,current machine-learning approaches generally require large amounts of data,which are often not available in *** this work,MODNet,an all-round framework,is presented which relies on a feedforward neural network,the selection of physically meaningful features,and when applicable,*** to being faster in terms of training time,this approach is shown to outperform current graph-network models on small *** particular,the vibrational entropy at 305 K of crystals is predicted with a mean absolute test error of 0.009 meV/K/atom(four times lower than previous studies).Furthermore,joint learning reduces the test error compared to single-target learning and enables the prediction of multiple properties at once,such as temperature ***,the selection algorithm highlights the most important features and thus helps to understand the underlying physics.

关键词： learning prediction property

A simple denoising approach to exploit multi-fidelity data for machine learning materials properties

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2022年第1期8卷 2226-2238页

作者： Xiaotong Liu Pierre-Paul De Breuck Linghui Wang gian-marco rignanese Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Information Science and Technology UniversityNo.35 Beisihuan Middle RoadBeijing100101BeijingP.R.China School of Computer Beijing Information Science and Technology UniversityNo.35 Beisihuan Middle RoadBeijing100101BeijingP.R.China Institute of Condensed Matter and Nanosciences UCLouvainChemin desÉtoiles 8Louvain-la-Neuve1348Belgium School of Materials Science and Engineering Northwestern Polytechnical UniversityNo.127 Youyi West RoadXi’an710072ShaanxiP.R.China

Machine-learning models have recently encountered enormous success for predicting the properties of *** are often trained based on data that present various levels of accuracy,with typically much less high-than low-fidelity *** order to extract as much information as possible from all available data,we here introduce an approach which aims to improve the quality of the data through *** investigate the possibilities that it offers in the case of the prediction of the band gap using both limited experimental data and density-functional theory relying on different exchange-correlation *** analyzing the raw data thoroughly,we explore different ways to combine the data into training sequences and analyze the effect of the chosen *** also study the effect of applying the denoising procedure several times until ***,we compare our approach with various existing methods to exploit multi-fidelity data and show that it provides an interesting improvement.

关键词： approach learning exploit

Transparent conducting materials discovery using high-throughput computing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2019年第1期5卷 606-618页

作者： Guillaume Brunin Francesco Ricci Viet-Anh Ha gian-marco rignanese Geoffroy Hautier UCLouvain Institut de la Matière Condensée et des Nanosciences(IMCN)Chemin desÉtoiles 8Louvain-la-Neuve 1348Belgium

Transparent conducting materials(TCMs)are required in many applications from solar cells to transparent *** high performance materials combining the antagonistic properties of transparency and conductivity has been challenging especially for p-type ***,high-throughput ab initio computational screening has emerged as a formidable tool for accelerating materials *** this review,we discuss how this approach has been applied for identifying *** provide a brief overview of the different materials properties of importance for TCMs(e.g.,dopability,effective mass,and transparency)and present the ab initio techniques available to assess *** focus on the accuracy of the methodologies as well as their suitability for high-throughput ***,we review the different high-throughput computational studies searching for new TCMs and discuss their differences in terms of methodologies and main findings.

关键词： transparency conducting suitability

Phonon-limited mobility for electrons and holes in highly-strained silicon

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2024年第1期10卷 673-683页

作者： Nicolas Roisin Guillaume Brunin gian-marco rignanese Denis Flandre Jean-Pierre Raskin Samuel Poncé Institute of Information and Communication Technologies Electronics and Applied MathematicsUniversitécatholique de LouvainLouvain-la-NeuveBelgium European Theoretical Spectroscopy Facility Institute of Condensed Matter and NanosciencesUniversitécatholique de LouvainLouvain-la-NeuveBelgium Matgenix A6K Advanced Engineering CenterCharleroiBelgium WEL Research Institute WavreBelgium

Strain engineering is a widely used technique for enhancing the mobility of charge carriers in semiconductors,but its effect is not fully *** this work,we perform first-principles calculations to explore the variations of the mobility of electrons and holes in silicon upon deformation by uniaxial strain up to 2%in the[100]crystal direction.

关键词： mobility strained electrons

High-throughput computational discovery of In_(2)Mn_(2)O_(7) as a high Curie temperature ferromagnetic semiconductor for spintronics

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2019年第1期5卷 529-535页

作者： Wei Chen Janine George Joel B.Varley gian-marco rignanese Geoffroy Hautier Institute of Condensed Matter and Nanoscicence(IMCN) Universitécatholique de Louvain1348 Louvain-la-NeuveBelgium Lawrence Livermore National Laboratory LivermoreCA 94550USA

Materials combining strong ferromagnetism and good semiconducting properties are highly desirable for spintronic applications(e.g.,in spin-filtering devices).In this work,we conduct a search for concentrated ferromagnetic semiconductors through highthroughput computational *** screening reveals the limited availability of semiconductors combining ferromagnetism and a low effective *** identify the manganese pyrochlore oxide In_(2)Mn_(2)O_(7) as especially promising for spin transport as it combines low electron effective mass(0.29m0),a large exchange splitting of the conduction band(1.1 eV),stability in air,and a Curie temperature(about 130 K)among the highest of concentrated ferromagnetic *** rationalise the high performance of In_(2)Mn_(2)O_(7) by the unique combination of a pyrochlore lattice favouring ferromagnetism with an adequate alignment of O–2p,Mn–3d,and In–5s forming a dispersive conduction band while enhancing the Curie temperature.

关键词： temperature ferromagnetic semiconductors

High-throughput calculations of charged point defect properties with semi-local density functional theory— performance benchmarks for materials screening applications

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2023年第1期9卷 1628-1639页

作者： Danny Broberg Kyle Bystrom Shivani Srivastava Diana Dahliah Benjamin A.D.Williamson Leigh Weston David O.Scanlon gian-marco rignanese Shyam Dwaraknath Joel Varley Kristin A.Persson Mark Asta Geoffroy Hautier Materials Sciences Division Lawrence Berkeley National Laboratory1 Cyclotron RoadBerkeleyCA 94720USA Department of Materials Science and Engineering University of CaliforniaBerkeleyCA 94720USA John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Department of Physics An-Najah National UniversityNablusPalestine Institute of Condensed Matter and Nanosciences UniversitéCatholique de LouvainChemin desÉtoiles 8B-1348 Louvain-la-NeuveBelgium Department of Materials Science and Engineering NTNU Norwegian University of Science and TechnologyTrondheimNorway Energy Technologies Area Lawrence Berkeley National Laboratory1 Cyclotron RoadBerkeleyCA 94720USA Department of Chemistry University College London20 Gordon StreetLondon WC1H 0AJUK Thomas Young Centre University College LondonGower StreetLondon WC1E 6BTUK Diamond Light Source Ltd. Diamond HouseHarwell Science and Innovation CampusDidcotOxfordshire OX110DEUK Lawrence Livermore National Laboratory LivermoreCA 94550USA Molecular Foundry Lawrence Berkeley National Laboratory1 Cyclotron RoadBerkeleyCA 94720USA Thayer School of Engineering Dartmouth CollegeHanoverNH 03755USA

Calculations of point defect energetics with Density Functional Theory(DFT)can provide valuable insight into several optoelectronic,thermodynamic,and kinetic *** calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional *** applications of DFT-based high-throughput computation for data-driven materials discovery,point defect properties are of interest,yet are currently excluded from available materials *** work presents a benchmark analysis of automated,semi-local point defect calculations with a-posteriori corrections,compared to 245“gold standard”hybrid calculations previously *** consider three different a-posteriori correction sets implemented in an automated workflow,and evaluate the qualitative and quantitative differences among four different categories of defect information:thermodynamic transition levels,formation energies,Fermi levels,and dopability *** highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods,while also demonstrating the limits of quantitative accuracy.

关键词： properties defect correction

Systematic assessment of various universal machine-learning interatomic potentials

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Materials Genome Engineering Advances 2024年第3期2卷 59-70页

作者： Haochen Yu Matteo giantomassi Giuliana Materzanini Junjie Wang gian-marco rignanese Institute of Condensed Matter and Nanosciences Universitécatholique de LouvainLouvain-la-NeuveBelgium State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi'anShaanxiChina WEL Research Institute WavreBelgium

Machine-learning interatomic potentials have revolutionized materials modeling at the atomic *** to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length *** recently,various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of *** this paper,we review and evaluate four different universal machine-learning interatomic potentials(uMLIPs),all based on graph neural network architectures which have demonstrated transferability from one chemical system to *** evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials *** this comprehensive evaluation,we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems,offer recommendations for model selection and optimization,and stimulate discussion on potential areas for improvement in current machinelearning methodologies in materials science.

关键词： formation energy geometry optimization machine learning phonons,universal machine-learning interatomic potentials verification

Common workflows for computing material properties using different quantum engines

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2021年第1期7卷 1202-1213页

作者： Sebastiaan P.Huber Emanuele Bosoni Marnik Bercx Jens Bröder Augustin Degomme Vladimir Dikan Kristjan Eimre Espen Flage-Larsen Alberto Garcia Luigi Genovese Dominik Gresch Conrad Johnston Guido Petretto Samuel Poncé gian-marco rignanese Christopher J.Sewell Berend Smit Vasily Tseplyaev Martin Uhrin Daniel Wortmann Aliaksandr V.Yakutovich Austin Zadoks Pezhman Zarabadi-Poor Bonan Zhu Nicola Marzari Giovanni Pizzi Theory and Simulation of Materials(THEOS)and National Centre for Computational Design and Discovery of Novel Materials(MARVEL) École Polytechnique Fédérale de LausanneLausanneSwitzerland Institut de Ciència de Materials de Barcelona ICMAB-CSICBellaterraSpain Peter Grünberg Institut and Institute for Advanced Simulation Forschungszentrum JülichJülichGermany Department of Physics RWTH Aachen UniversityAachenGermany CEA IRIG-MEM-L_SimUniv.Grenoble-AlpesGrenobleFrance Nanotech@surfaces Laboratory Swiss Federal Laboratories for Materials Science and Technology(Empa)DübendorfSwitzerland SINTEF Industry Materials PhysicsOsloNorway Department of Physics University of OsloOsloNorway Microsoft Station Q University of CaliforniaSanta BarbaraCAUSA Atomistic Simulation Centre School of Mathematics and PhysicsQueen’s University BelfastBelfastUK UCLouvain Institut de la Matière Condensée et des Nanosciences(IMCN)Louvain-la-NeuveBelgium Laboratory of Molecular Simulation(LSMO) Institut des sciences et ingénierie chimiques(ISIC)École Polytechnique Fédérale de Lausanne(EPFL)ValaisSionSwitzerland Department of Chemistry Claverton DownUniversity of BathBathUK The Faraday Institution DidcotUK Department of Chemistry University College LondonLondonUK

The prediction of material properties based on density-functional theory has become routinely common,thanks,in part,to the steady increase in the number and robustness of available simulation *** plurality of codes and methods is both a boon and a *** providing great opportunities for cross-verification,these packages adopt different methods,algorithms,and paradigms,making it challenging to choose,master,and efficiently use *** demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and *** introduce design rules for reusable,code-agnostic,workflow interfaces to compute well-defined material properties,which we implement for eleven quantum engines and use to compute various material *** implementation encodes carefully selected simulation parameters and workflow logic,making the implementer’s expertise of the quantum engine directly available to *** workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

关键词： quantum routine material