Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Jonathan P Mailoa"'
Autor:
Lars A Bratholm, Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, Addison Howard, Sanghoon Kim, Zico Kolter, Risi Kondor, Mordechai Kornbluth, Youhan Lee, Youngsoo Lee, Jonathan P Mailoa, Thanh Tu Nguyen, Milos Popovic, Goran Rakocevic, Walter Reade, Wonho Song, Luka Stojanovic, Erik H Thiede, Nebojsa Tijanic, Andres Torrubia, Devin Willmott, Craig P Butts, David R Glowacki
Publikováno v:
PLoS ONE, Vol 16, Iss 7, p e0253612 (2021)
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in adva
Externí odkaz:
https://doaj.org/article/7bd641a416ff403b90f3c0a0e9726773
Autor:
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
An E(3)-equivariant deep learning interatomic potential is introduced for accelerating molecular dynamics simulations. The method obtains state-of-the-art accuracy, can faithfully describe dynamics of complex systems with remarkable sample efficiency
Externí odkaz:
https://doaj.org/article/00967ba51d9e4740b8ca005b21c889fc
Autor:
Hemi H. Gandhi, David Pastor, Tuan T. Tran, Stefan Kalchmair, Lachlan A. Smillie, Jonathan P. Mailoa, Ruggero Milazzo, Enrico Napolitani, Marko Loncar, James S. Williams, Michael J. Aziz, Eric Mazur
Publikováno v:
AIP Advances, Vol 10, Iss 7, Pp 075028-075028-6 (2020)
Obtaining short-wavelength-infrared (SWIR; 1.4 μm–3.0 μm) room-temperature photodetection in a low-cost, group IV semiconductor is desirable for numerous applications. We demonstrate a non-equilibrium method for hyperdoping germanium with seleniu
Externí odkaz:
https://doaj.org/article/fa3be7a8a9254ba99d46f6ff635d8021
Autor:
LingJun Wu, ZhenMing Xu, ZiXuan Wang, ZiJian Chen, ZhiChao Huang, Chao Peng, XiangDong Pei, XiangGuo Li, Jonathan P. Mailoa, Chang-Yu Hsieh, Tao Wu, Xue-Feng Yu, HaiTao Zhao
Publikováno v:
Science China Technological Sciences. 65:2274-2296
Publikováno v:
Chemistry of Materials. 33:6662-6670
Autor:
Charles Forsberg, Jonathan P. Mailoa, Qing-Jie Li, Ju Li, Stephen T. Lam, Ronald G. Ballinger
Publikováno v:
Journal of Materials Chemistry A. 9:1784-1794
Interest in molten salts has increased significantly over the last decade due to their potential application in various clean-energy technologies including hydrogen generation, solar heat storage, advanced fission nuclear power plants, and compact fu
Autor:
Daniel A. Gribble, Madhusudan Tyagi, Scott Mullin, Nitash P. Balsara, Hiroshi Watanabe, Georgy Samsonidze, Boris Kozinsky, Jonathan P. Mailoa, Katrina Irene S. Mongcopa
Publikováno v:
ACS macro letters. 7(4)
Quasi-elastic neutron scattering experiments on mixtures of poly(ethylene oxide) and lithium bis(trifluoromethane)sulfonimide salt, a standard polymer electrolyte, led to the quantification of the effect of salt on segmental dynamics in the 1–10 A
Publikováno v:
Journal of Power Sources. 428:27-36
Strong ionic interactions in concentrated ionic liquids is shown to result in significant correlations and deviations from ideal solution behavior. We use rigorous concentrated solution theory coupled with molecular dynamics simulations to compute an
Autor:
Nicola Molinari, Jonathan P. Mailoa, Boris Kozinsky, William A. Goddard, Jeffrey C. Grossman, Francesco Faglioni, Eric Fadel, Boris V. Merinov, Georgy Samsonidze
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-10 (2019)
Nature Communications
Nature Communications
Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemist
Autor:
Boris Kozinsky, Chris Wolverton, Cheol Woo Park, Mordechai Kornbluth, Jonathan P. Mailoa, Jonathan Vandermause
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically