Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Gus L. W. Hart"'
Autor:
Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev, Livia B. Pártay, Noam Bernstein, Gábor Csányi, Gus L. W. Hart
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
Abstract We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interat
Externí odkaz:
https://doaj.org/article/47b2072bae1e4a3da976b0281a49afb1
Publikováno v:
npj Computational Materials, Vol 3, Iss 1, Pp 1-7 (2017)
Machine learning: Modelling atomic systems to make property predictions A method for representing atomic systems for machine learning is shown that can provide access to the physical properties of these systems. Machine learning is a powerful tool fo
Externí odkaz:
https://doaj.org/article/b71a7a9a5df64438a53ef378aa2fd64e
Publikováno v:
Frontiers in Materials, Vol 6 (2019)
The atomic structure of grain boundaries plays a defining but poorly understood role in the properties they exhibit. Due to the complex nature of these structures, machine learning is a natural tool for extracting meaningful relationships and new phy
Externí odkaz:
https://doaj.org/article/aa1baa2249494bd5bcd04b69b1242cd8
Publikováno v:
Physical Review X, Vol 3, Iss 4, p 041035 (2013)
We report a comprehensive study of the binary systems of the platinum-group metals with the transition metals, using high-throughput first-principles calculations. These computations predict stability of new compounds in 28 binary systems where no co
Externí odkaz:
https://doaj.org/article/cf0187faf1f54334b4eac0afa40726af
Publikováno v:
Nature Reviews Materials. 6:730-755
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy
Autor:
Matthew J. Patrick, Gregory S. Rohrer, Ooraphan Chirayutthanasak, Sutatch Ratanaphan, Eric R. Homer, Gus L. W. Hart, Yekaterina Epshteyn, Katayun Barmak
Publikováno v:
Acta Materialia. 242:118476
Grain boundary character distributions (GBCD) are routinely measured from bulk microcrystalline samples by electron backscatter diffraction (EBSD) and serial sectioning can be used to reconstruct relative grain boundary energy distributions (GBED) ba
Autor:
Apurva Mehta, Carlos León, Kedar Manandhar, Jamie L. Weaver, Drew Stasak, Takeshi Sunaoshi, Gus L. W. Hart, Huilong Hou, Suchismita Sarker, Saydul Sardar, Dylan Kirsch, Ichiro Takeuchi, Muye Xiao, Yaoyu Ren, John P. Lemmon
Publikováno v:
ACS Applied Energy Materials. 3:2547-2555
To realize high specific capacity Li-metal batteries, a protection layer for the Li-metal anode is needed. We are carrying out combinatorial screening of Li-alloy thin films as the protection layer...
Publikováno v:
Computational Materials Science. 156:148-156
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our approach sig
Publikováno v:
Computational Materials Science. 153:424-430
Most DFT practitioners use regular grids (Monkhorst-Pack, MP) for integrations in the Brillioun zone. Although regular grids are the natural choice and easy to generate, more general grids whose generating vectors are not merely integer divisions of
Autor:
Jeremy J. Jorgensen, Gus L. W. Hart
Density functional theory (DFT) codes are commonly treated as a "black box" in high-throughput screening of materials, with users opting for the default values of the input parameters. Often, non-experts may not sufficiently consider the effect of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f0664a754b56a9606009f5eaa28e736