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| 1 | New frontiers for the materials genome initiative显示文摘The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts. | Juan J.de Pablo Nicholas E.Jackson Michael A.Webb Long-Qing Chen Joel E.Moore Dane Morgan Ryan Jacobs Tresa Pollock Darrell G.Schlom Eric S.Toberer James Analytis Ismaila Dabo Dean M.DeLongchamp Gregory A.Fiete Gregory M.Grason Geoffroy Hautier Yifei Mo Krishna Rajan Evan J.Reed Efrain Rodriguez Vladan Stevanovic Jin Suntivich Katsuyo Thornton Ji-Cheng Zhao | 2019 | npj Computational Materials2019，，1： | 7 |
| 2 | Empirical modeling of dopability in diamond-like semiconductors显示文摘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 cost.In this work,we explore the application of machine learning for high-throughput carrier concentration range prediction.Bounding the model within diamond-like semiconductors,the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary.The data were analyzed using various statistical and machine learning methods.Accurate 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 dopability.The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts.Finally,dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials. | Samuel A.Miller Maxwell Dylla Shashwat Anand Kiarash G.ordiz G.Jeffrey Snyder Eric S.Toberer | 2018 | npj Computational Materials2018，，1： | 1 |
| 3 | Publisher Correction:First-principles calculation of intrinsic defect chemistry and self-doping in PbTe显示文摘 | Anuj Goyal Prashun Gorai Eric S.Toberer Vladan Stevanovic | 2017 | npj Computational Materials2017，，1： | 0 |
| 4 | First-principles calculation of intrinsic defect chemistry and self-doping in PbTe显示文摘Semiconductor dopability is inherently limited by intrinsic defect chemistry.In many thermoelectric materials,narrow band gaps due to strong spin-orbit interactions make accurate atomic level predictions of intrinsic defect chemistry and self-doping computationally challenging.Here we use different levels of theory to model point defects in PbTe,and compare and contrast the results against each other and a large body of experimental data.We find that to accurately reproduce the intrinsic defect chemistry and known self-doping behavior of PbTe,it is essential to(a)go beyond the semi-local GGA approximation to density functional theory,(b)include spin-orbit coupling,and(c)utilize many-body GW theory to describe the positions of individual band edges.The hybrid HSE functional with spin-orbit coupling included,in combination with the band edge shifts from G0W0 is the only approach that accurately captures both the intrinsic conductivity type of PbTe as function of synthesis conditions as well as the measured charge carrier concentrations,without the need for experimental inputs.Our results reaffirm the critical role of the position of individual band edges in defect calculations,and demonstrate that dopability can be accurately predicted in such challenging narrow band gap materials. | Anuj Goyal Prashun Gorai Eric S.Toberer Vladan Stevanovic | 2017 | npj Computational Materials2017，，1： | 0 |
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