Zobrazeno 1 - 10
of 496
pro vyhledávání: '"Witt, William"'
Autor:
Batatia, Ilyes, Benner, Philipp, Chiang, Yuan, Elena, Alin M., Kovács, Dávid P., Riebesell, Janosh, Advincula, Xavier R., Asta, Mark, Avaylon, Matthew, Baldwin, William J., Berger, Fabian, Bernstein, Noam, Bhowmik, Arghya, Blau, Samuel M., Cărare, Vlad, Darby, James P., De, Sandip, Della Pia, Flaviano, Deringer, Volker L., Elijošius, Rokas, El-Machachi, Zakariya, Falcioni, Fabio, Fako, Edvin, Ferrari, Andrea C., Genreith-Schriever, Annalena, George, Janine, Goodall, Rhys E. A., Grey, Clare P., Grigorev, Petr, Han, Shuang, Handley, Will, Heenen, Hendrik H., Hermansson, Kersti, Holm, Christian, Jaafar, Jad, Hofmann, Stephan, Jakob, Konstantin S., Jung, Hyunwook, Kapil, Venkat, Kaplan, Aaron D., Karimitari, Nima, Kermode, James R., Kroupa, Namu, Kullgren, Jolla, Kuner, Matthew C., Kuryla, Domantas, Liepuoniute, Guoda, Margraf, Johannes T., Magdău, Ioan-Bogdan, Michaelides, Angelos, Moore, J. Harry, Naik, Aakash A., Niblett, Samuel P., Norwood, Sam Walton, O'Neill, Niamh, Ortner, Christoph, Persson, Kristin A., Reuter, Karsten, Rosen, Andrew S., Schaaf, Lars L., Schran, Christoph, Shi, Benjamin X., Sivonxay, Eric, Stenczel, Tamás K., Svahn, Viktor, Sutton, Christopher, Swinburne, Thomas D., Tilly, Jules, van der Oord, Cas, Varga-Umbrich, Eszter, Vegge, Tejs, Vondrák, Martin, Wang, Yangshuai, Witt, William C., Zills, Fabian, Csányi, Gábor
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and hum
Externí odkaz:
http://arxiv.org/abs/2401.00096
Autor:
Kovács, Dávid Péter, Moore, J. Harry, Browning, Nicholas J., Batatia, Ilyes, Horton, Joshua T., Kapil, Venkat, Witt, William C., Magdău, Ioan-Bogdan, Cole, Daniel J., Csányi, Gábor
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required
Externí odkaz:
http://arxiv.org/abs/2312.15211
Autor:
Witt, William C., van der Oord, Cas, Gelžinytė, Elena, Järvinen, Teemu, Ross, Andres, Darby, James P., Ho, Cheuk Hin, Baldwin, William J., Sachs, Matthias, Kermode, James, Bernstein, Noam, Csányi, Gábor, Ortner, Christoph
We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion (Drautz, 2019). As the latter provides a complete description of atomic
Externí odkaz:
http://arxiv.org/abs/2309.03161
Autor:
Salzbrenner, Pascal T., Joo, Se Hun, Conway, Lewis J., Cooke, Peter I. C., Zhu, Bonan, Matraszek, Milosz P., Witt, William C., Pickard, Chris J.
Publikováno v:
J. Chem. Phys. 159, 144801 (2023)
Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP
Externí odkaz:
http://arxiv.org/abs/2306.06475
Density-potential functional theory (DPFT) is an alternative formulation of orbital-free density functional theory that may be suitable for modeling the electronic structure of large systems. To date, DPFT has been applied mainly to quantum gases in
Externí odkaz:
http://arxiv.org/abs/2304.10059
Amorphous alumina is employed ubiquitously as a high-dielectric-constant material in electronics, and its thermal-transport properties are of key relevance for heat management in electronic chips and devices. Experiments show that the thermal conduct
Externí odkaz:
http://arxiv.org/abs/2303.08637
Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, accelerating the development process and reducing the maintenance burden. Fur
Externí odkaz:
http://arxiv.org/abs/2212.03231
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Autor:
Young, Cheryl, Boyce, Colleen, Winkler, Jennifer, Holian, Susan, Greenberg, Nancy, George, Shirley, Kwon, Alyson, Dorsey, Brenda, Da Costa, Ana Raquel, Witt, William, Grays, Daryl, Gapasin, Arren, Bernal, Paula, Floyd, Jeffrey, Goldstein, Eric, Howe, Leslie, Lee, Myounghee, Marron, Jennifer, Brooks, Kelly, Turek, Lisa, Farley, Patricia, Frels, Shantel, Booth, Delores, Gall, Jason, Carlton, Kevin, Albright, Gabriela, Amharref, Nadia, Atallah, Kandace, Banappagari, Sashikanth, Bastani, Niutish, Blackstock, Daniel, Boonyaratanakornkit, Bobby, Carey, Elizabeth, Charlton, Adam, Chaudhuri, Rajoshi, Caringal, Alegria M., Chen, Mingzhong, Chen, Peifeng, Cheng, Wei, Gowetski, Daniel, Gulla, Krishana, Hastings, Erica, Horwitz, Joe, Ivleva, Vera, Kordella, Dan, Kueltzo, Lisa A., Lagler, Sara, Le, Matt, Lee, James, Lei, Paula, Li, Yile, Nagy, Attila, Patel, Aakash, Runsewe, Peyi, Shadrick, Will, Shetty, Shamitha, Wang, Hairong, Webber, Calvin, Vejzagic, Farah, Yang, Yoo-Jung, Lyke, Kirsten E *, Berry, Andrea A, Mason, Kaitlin, Idris, Azza H, O'Callahan, Mark, Happe, Myra, Strom, Larisa, Berkowitz, Nina M, Guech, Mercy, Hu, Zonghui, Castro, Mike, Basappa, Manjula, Wang, Lu, Low, Kwang, Holman, LaSonji A, Mendoza, Floreliz, Gordon, Ingelise J, Plummer, Sarah H, Trofymenko, Olga, Strauss, Kathleen S, Joshi, Sudhaunshu, Shrestha, Biraj, Adams, Matthew, Chagas, Andrezza Campos, Murphy, Jittawadee R, Stein, Judy, Hickman, Somia, McDougal, Andrew, Lin, Bob, Narpala, Sandeep R, Vazquez, Sandra, Serebryannyy, Leonid, McDermott, Adrian, Gaudinski, Martin R, Capparelli, Edmund V, Coates, Emily E, Wu, Richard L, Ledgerwood, Julie E, Dropulic, Lesia K, Seder, Robert A
Publikováno v:
In The Lancet Infectious Diseases May 2023 23(5):578-588