Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Cheon, Gowoon"'
Autor:
Yang, Samuel J., Li, Shutong, Venugopalan, Subhashini, Tshitoyan, Vahe, Aykol, Muratahan, Merchant, Amil, Cubuk, Ekin Dogus, Cheon, Gowoon
Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such models require training data. While automated data extraction from sci
Externí odkaz:
http://arxiv.org/abs/2311.13778
Crystal structure search is a long-standing challenge in materials design. We present a dataset of more than 100,000 structural relaxations of potential battery anode materials from randomized structures using density functional theory calculations.
Externí odkaz:
http://arxiv.org/abs/2012.02920
The work function is the key surface property that determines how much energy is required for an electron to escape the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron
Externí odkaz:
http://arxiv.org/abs/2011.10905
Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspectio
Externí odkaz:
http://arxiv.org/abs/2010.04815
Autor:
Choudhary, Kamal, Garrity, Kevin F., Reid, Andrew C. E., DeCost, Brian, Biacchi, Adam J., Walker, Angela R. Hight, Trautt, Zachary, Hattrick-Simpers, Jason, Kusne, A. Gilad, Centrone, Andrea, Davydov, Albert, Jiang, Jie, Pachter, Ruth, Cheon, Gowoon, Reed, Evan, Agrawal, Ankit, Qian, Xiaofeng, Sharma, Vinit, Zhuang, Houlong, Kalinin, Sergei V., Sumpter, Bobby G., Pilania, Ghanshyam, Acar, Pinar, Mandal, Subhasish, Haule, Kristjan, Vanderbilt, David, Rabe, Karin, Tavazza, Francesca
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
Externí odkaz:
http://arxiv.org/abs/2007.01831
We compile data and machine learned models of solid Li-ion electrolyte performance to assess the state of materials discovery efforts and build new insights for future efforts. Candidate electrolyte materials must satisfy several requirements, chief
Externí odkaz:
http://arxiv.org/abs/1904.08996
We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid Li superi
Externí odkaz:
http://arxiv.org/abs/1808.02470
Publikováno v:
Phys. Rev. B 98, 014107 (2018)
In this work, we present a high-throughput first-principles study of elastic properties of bulk and monolayer materials mainly using the vdW-DF-optB88 functional. We discuss the trends on the elastic response with respect to changes in dimensionality
Externí odkaz:
http://arxiv.org/abs/1804.01033
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