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Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

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Research 2020年第1期2020卷 978-985页

作者： Maxwell TDylla Alexander Dunn shashwat anand Anubhav Jain G.Jeffrey Snyder Department of Materials Science and Engineering Northwestern UniversityIL 60208USA Department of Materials Science and Engineering UC BerkeleyCA 94720USA Lawrence Berkeley National Laboratory Energy Technologies AreaCA 94720USA

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band ***,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic *** all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or ***,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band *** do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence ***,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the *** rules can be used to engineer band structures with band convergence and high valley degeneracy.

关键词： structure valence bands

Empirical modeling of dopability in diamond-like semiconductors

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npj Computational Materials 2018年第1期4卷 69-76页

作者： Samuel A.Miller Maxwell Dylla shashwat anand Kiarash G.ordiz G.Jeffrey Snyder Eric S.Toberer Northwestern University EvanstonIL 60208USA Colorado School of Mines GoldenCO 80401USA National Renewable Energy Laboratory GoldenCO 80401USA

Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications(e.g.,thermoelectrics,transparent conductors,photovoltaics).This barrier has been particularly pernicious in the realm of high-throughput property prediction,where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational *** this work,we explore the application of machine learning for high-throughput carrier concentration range *** the model within diamond-like semiconductors,the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to *** data were analyzed using various statistical and machine learning *** predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p-and n-type *** model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational ***,dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.

关键词： diamond transparent semiconductors

A Convergent Understanding of Charged Defects

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Accounts of Materials Research 2022年第7期3卷 685-696页

作者： shashwat anand Michael Y.Toriyama Chris Wolverton Sossina M.Haile G.Jeffrey Snyder Department of Materials Science and Engineering Northwestern UniversityEvanstonIllinois 60208United States Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCalifornia 94720United States

CONSPECTUS:Historically,defects in semiconductors and ionic conductors have been studied using very different *** the solid-state ionics community,nonstoichiometry and defect thermochemistry are often probed directly through *** dependency of defect concentrations on chemical conditions(typically oxygen pressure)are modeled using a physical chemistry framework and compactly represented by the well-known Brouwer diagrams.

关键词： defect chemistry ionic

Soft Handover Probability Determination Considering New Direction of Motion

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Communications and Network 2013年第2期5卷 178-183页

作者： Basant Kumar anand Mohan shashwat Pathak Department of Electronics and Communication Engineering Motilal Nehru National Institute of Technology Allahabad India

This paper presents prior determination of soft handover probability considering new direction of motion of mobile station (MS) coinciding with gravitation point of cells. Our simulation results for 3-cell scenario and considered new direction of MS motion can be potentially used as advance input to soft handover algorithms to minimize number of handovers.

关键词： Handover Threshold Soft Handover Soft Handover Probability WCDMA Networks