Zobrazeno 1 - 10
of 2 506
pro vyhledávání: '"Kornbluth, A."'
Autor:
Tianyu Ying, Alice Dien, Kurt Kornbluth, Christopher W Simmons, Irwin R. Donis-González, Edward S. Spang
Publikováno v:
ACS Omega, Vol 9, Iss 36, Pp 38142-38152 (2024)
Externí odkaz:
https://doaj.org/article/6103ad115424435faa6c10a8bc78d5b6
Autor:
Shmilovich, Kirill, Willmott, Devin, Batalov, Ivan, Kornbluth, Mordechai, Mailoa, Jonathan, Kolter, J. Zico
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous works focuses on generating predictions for only a fix
Externí odkaz:
http://arxiv.org/abs/2205.06133
Autor:
Musaelian, Albert, Batzner, Simon, Johansson, Anders, Sun, Lixin, Owen, Cameron J., Kornbluth, Mordechai, Kozinsky, Boris
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message passing has lead to a paradigm
Externí odkaz:
http://arxiv.org/abs/2204.05249
Autor:
Batzner, Simon, Musaelian, Albert, Sun, Lixin, Geiger, Mario, Mailoa, Jonathan P., Kornbluth, Mordechai, Molinari, Nicola, Smidt, Tess E., Kozinsky, Boris
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aw
Externí odkaz:
http://arxiv.org/abs/2101.03164
Publikováno v:
Journal of Sports Medicine and Allied Health Sciences: Official Journal of the Ohio Athletic Trainers' Association, Vol 10, Iss 1 (2024)
Externí odkaz:
https://doaj.org/article/03239a7e45d94df6a1da5587ca6030bc
A community-powered search of machine learning strategy space to find NMR property prediction models
Autor:
Bratholm, Lars A., Gerrard, Will, Anderson, Brandon, Bai, Shaojie, Choi, Sunghwan, Dang, Lam, Hanchar, Pavel, Howard, Addison, Huard, Guillaume, Kim, Sanghoon, Kolter, Zico, Kondor, Risi, Kornbluth, Mordechai, Lee, Youhan, Lee, Youngsoo, Mailoa, Jonathan P., Nguyen, Thanh Tu, Popovic, Milos, Rakocevic, Goran, Reade, Walter, Song, Wonho, Stojanovic, Luka, Thiede, Erik H., Tijanic, Nebojsa, Torrubia, Andres, Willmott, Devin, Butts, Craig P., Glowacki, David R., participants, Kaggle
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:
http://arxiv.org/abs/2008.05994
Publikováno v:
Phys. Rev. E 103, 032309 (2021)
Carreras, Dobson and colleagues have studied empirical data on the sizes of the blackouts in real grids and modeled them by computer simulations using the direct current approximation. They have found that the resulting blackout sizes are distributed
Externí odkaz:
http://arxiv.org/abs/2008.01141
Autor:
Park, Cheol Woo, Kornbluth, Mordechai, Vandermause, Jonathan, Wolverton, Chris, Kozinsky, Boris, Mailoa, Jonathan P.
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
Externí odkaz:
http://arxiv.org/abs/2007.14444
Autor:
Molinari, Nicola, Xie, Yu, Leifer, Ian, Marcolongo, Aris, Kornbluth, Mordechai, Kozinsky, Boris
Publikováno v:
Phys. Rev. Lett. 127, 025901 (2021)
Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displa
Externí odkaz:
http://arxiv.org/abs/2007.08734
Autor:
Emily C. Matchett, Jacki Kornbluth
Publikováno v:
Frontiers in Immunology, Vol 14 (2023)
IntroductionOver the last decade, there have been many advancements in the therapeutic treatment of multiple myeloma (MM), including the use of natural killer (NK) cells. However, despite promising results from clinical trials, there are concerns ove
Externí odkaz:
https://doaj.org/article/aa1811fcd8754264847f86b8141c2bbf