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• W2996868133 endingPage "109483" @default.
• W2996868133 startingPage "109483" @default.
• W2996868133 abstract "Materials properties such as defect diffusion and/or dissociation, mechanical fracture and void nucleation, under extreme temperatures and pressures, are all governed by the interactions between individual and/or groups of atoms. Computational tools have been instrumental in understanding the atomistic properties of materials at these length scales. Over the past few decades, these tools have been dominated by two levels of theory: quantum mechanics (QM) based methods and semi-empirical/classical methods. The former are time-intensive, but accurate and versatile, while the latter methods are fast but are significantly limited in veracity, versatility and transferability. Machine learning (ML) algorithms, in tandem with quantum mechanical methods such as density functional theory, have the potential to bridge the gap between these two chasms due to their (i) low cost, (ii) accuracy, (iii) transferability, and (iv) ability to be iteratively improved. In this work, we prescribe a new paradigm in which potential energy, atomic forces, and stresses are rapidly predicted by independent machine learning models, all while retaining the accuracy of quantum mechanics. This platform has been used to study thermal, vibrational, and diffusive properties of bulk Platinum, highlighting the framework’s ability to reliably predict materials properties under dynamic conditions. We then compare our ML framework to both QM, where applicable, and several Embedded Atom Method (EAM) potentials. We conclude this work by reflecting upon the current state of ML in materials science for atomistic simulations." @default.
• W2996868133 created "2020-01-10" @default.
• W2996868133 creator A5003850818 @default.
• W2996868133 creator A5013065381 @default.
• W2996868133 creator A5037217491 @default.
• W2996868133 date "2020-03-01" @default.
• W2996868133 modified "2023-09-29" @default.
• W2996868133 title "Machine learning models for the prediction of energy, forces, and stresses for Platinum" @default.
• W2996868133 cites W1234070621 @default.
• W2996868133 cites W1513260206 @default.
• W2996868133 cites W1967827461 @default.
• W2996868133 cites W1970127494 @default.
• W2996868133 cites W1972135189 @default.
• W2996868133 cites W1973193278 @default.
• W2996868133 cites W1973836541 @default.
• W2996868133 cites W1978183953 @default.
• W2996868133 cites W1979544533 @default.
• W2996868133 cites W1981078459 @default.
• W2996868133 cites W1988330065 @default.
• W2996868133 cites W1989715904 @default.
• W2996868133 cites W1990000601 @default.
• W2996868133 cites W1992149671 @default.
• W2996868133 cites W1994522695 @default.
• W2996868133 cites W1998623472 @default.
• W2996868133 cites W2003556887 @default.
• W2996868133 cites W2015975170 @default.
• W2996868133 cites W2019465613 @default.
• W2996868133 cites W2020786104 @default.
• W2996868133 cites W2025374639 @default.
• W2996868133 cites W2025444507 @default.
• W2996868133 cites W2030976617 @default.
• W2996868133 cites W2031430816 @default.
• W2996868133 cites W2033086537 @default.
• W2996868133 cites W2036113194 @default.
• W2996868133 cites W2048528541 @default.
• W2996868133 cites W2055050546 @default.
• W2996868133 cites W2064221461 @default.
• W2996868133 cites W2069070582 @default.
• W2996868133 cites W2069483681 @default.
• W2996868133 cites W2071514571 @default.
• W2996868133 cites W2074616700 @default.
• W2996868133 cites W2078402226 @default.
• W2996868133 cites W2083222334 @default.
• W2996868133 cites W2083630784 @default.
• W2996868133 cites W2084053963 @default.
• W2996868133 cites W2086513155 @default.
• W2996868133 cites W2087983709 @default.
• W2996868133 cites W2098736175 @default.
• W2996868133 cites W2112261363 @default.
• W2996868133 cites W2117894566 @default.
• W2996868133 cites W2122427541 @default.
• W2996868133 cites W2127644822 @default.
• W2996868133 cites W2128873947 @default.
• W2996868133 cites W2147415793 @default.
• W2996868133 cites W2151399017 @default.
• W2996868133 cites W2155155530 @default.
• W2996868133 cites W2225949634 @default.
• W2996868133 cites W2230728100 @default.
• W2996868133 cites W2292646695 @default.
• W2996868133 cites W2410722695 @default.
• W2996868133 cites W2415372084 @default.
• W2996868133 cites W2530960271 @default.
• W2996868133 cites W2566642125 @default.
• W2996868133 cites W2584994763 @default.
• W2996868133 cites W2585152223 @default.
• W2996868133 cites W2592939032 @default.
• W2996868133 cites W2605925159 @default.
• W2996868133 cites W2755837508 @default.
• W2996868133 cites W2764267192 @default.
• W2996868133 cites W2766793638 @default.
• W2996868133 cites W2780553247 @default.
• W2996868133 cites W2795584281 @default.
• W2996868133 cites W2806268474 @default.
• W2996868133 cites W2883528235 @default.
• W2996868133 cites W2888664978 @default.
• W2996868133 cites W2903591455 @default.
• W2996868133 cites W2921533983 @default.
• W2996868133 cites W2949470943 @default.
• W2996868133 cites W2963071675 @default.
• W2996868133 cites W2966975829 @default.
• W2996868133 cites W2969422465 @default.
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• W2996868133 cites W4249528159 @default.
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• W2996868133 doi "https://doi.org/10.1016/j.commatsci.2019.109483" @default.
• W2996868133 hasPublicationYear "2020" @default.
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