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Autor:
Houlong L. Zhuang, Vinit Sharma, Kamal Choudhary, Brian L. DeCost, Ruth Pachter, Andrew C. E. Reid, Albert V. Davydov, Evan J. Reed, Sergei V. Kalinin, Pinar Acar, Jason R. Hattrick-Simpers, Gowoon Cheon, Ankit Agrawal, David Vanderbilt, A. Gilad Kusne, Subhasish Mandal, Francesca Tavazza, Ghanshyam Pilania, Zachary T. Trautt, Kevin F. Garrity, Jie Jiang, Angela R. Hight Walker, Karin M. Rabe, Kristjan Haule, Andrea Centrone, Bobby G. Sumpter, Adam J. Biacchi, Xiaofeng Qian
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-13 (2020)
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) tech
Autor:
David A. Winkler, Salvy P. Russo, Udo Bach, Andrew J. Christofferson, Mykhailo Klymenko, Nastaran Meftahi
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-8 (2020)
Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of the best materials difficult. Here, we show that machine-lea
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-7 (2020)
Cu3SbSe3 that exhibits distinct liquid-like sublattice due to the heterogeneous bonding environment has emerged as a promising low cost superionic semiconductor with intrinsic ultralow thermal conductivity. However, the relationship between atomic dy
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-9 (2020)
Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a giv
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-9 (2020)
The explosive rise of silicon photonics has led to renewed interest in the electro-optic (EO) or Pockels effect due to its potential uses in many next generation device applications. To find materials with a strong EO response in thin film form, whic
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-8 (2020)
Integrating artificial intelligence (AI) and computer science together with current approaches in material synthesis and optimization will act as an effective approach for speeding up the discovery of high-performance photoactive materials in organic
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-9 (2020)
Creation of a partially filled intermediate band in a photovoltaic absorber material is an appealing concept for increasing the quantum efficiency of solar cells. Recently, we showed that formation of a partially filled intermediate band through dopi
Autor:
Chuang Dong, Zhen Li, Qing Wang, Beibei Jiang, Peter K. Liaw, Biaojie Yan, Fei Yang, Wei Xu, Pengcheng Zhang
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-11 (2020)
The present work formulated a materials design approach, a cluster-formula-embedded machine learning (ML) model, to search for body-centered-cubic (BCC) β-Ti alloys with low Young’s modulus (E) in the Ti–Mo–Nb–Zr–Sn–Ta system. The charac
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-9 (2020)
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image featu
Autor:
Pedro Borlido, Fabien Tran, Miguel A. L. Marques, Silvana Botti, Ahmad W. Huran, Jonathan Schmidt
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-17 (2020)
We conducted a large-scale density-functional theory study on the influence of the exchange-correlation functional in the calculation of electronic band gaps of solids. First, we use the large materials data set that we have recently proposed to benc