Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Rosen, Andrew"'
Autor:
Elena, Alin Marin, Kamath, Prathami Divakar, Inizan, Théo Jaffrelot, Rosen, Andrew S., Zanca, Federica, Persson, Kristin A.
Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical s
Externí odkaz:
http://arxiv.org/abs/2412.02877
Autor:
Fu, Xiang, Rosen, Andrew, Bystrom, Kyle, Wang, Rui, Musaelian, Albert, Kozinsky, Boris, Smidt, Tess, Jaakkola, Tommi
In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly accelerating charge density prediction, yet existing appro
Externí odkaz:
http://arxiv.org/abs/2405.19276
Autor:
Yuan, Eric C. -Y., Kumar, Anup, Guan, Xingyi, Hermes, Eric D., Rosen, Andrew S., Zádor, Judit, Head-Gordon, Teresa, Blau, Samuel M.
Identifying transition states -- saddle points on the potential energy surface connecting reactant and product minima -- is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train an equivariant n
Externí odkaz:
http://arxiv.org/abs/2405.02247
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:
Rothchild, Daniel, Rosen, Andrew S., Taw, Eric, Robinson, Connie, Gonzalez, Joseph E., Krishnapriyan, Aditi S.
We present an investigation into diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potent
Externí odkaz:
http://arxiv.org/abs/2311.01491
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modular nature has enabled the use of template-based methods to generate hyp
Externí odkaz:
http://arxiv.org/abs/2310.10732
Structured information extraction from complex scientific text with fine-tuned large language models
Autor:
Dunn, Alexander, Dagdelen, John, Walker, Nicholas, Lee, Sanghoon, Rosen, Andrew S., Ceder, Gerbrand, Persson, Kristin, Jain, Anubhav
Intelligently extracting and linking complex scientific information from unstructured text is a challenging endeavor particularly for those inexperienced with natural language processing. Here, we present a simple sequence-to-sequence approach to joi
Externí odkaz:
http://arxiv.org/abs/2212.05238
Autor:
Rosen, Andrew M.
Publikováno v:
Online access via UMI.
Thesis (M.S.)--State University of New York at Binghamton, Department of Psychology, 2008.
Includes bibliographical references.
Includes bibliographical references.
Externí odkaz:
http://proxy.binghamton.edu/login?url=http://wwwlib.umi.com/dissertations/fullcit/1454363
Autor:
Berquist, Eric, Dumi, Amanda, Upadhyay, Shiv, Abarbanel, Omri D., Cho, Minsik, Gaur, Sagar, Cano Gil, Victor Hugo, Hutchison, Geoffrey R., Lee, Oliver S., Rosen, Andrew S., Schamnad, Sanjeed, Schneider, Felipe S. S., Steinmann, Casper, Stolyarchuk, Maxim, Vandezande, Jonathon E., Zak, Weronika, Langner, Karol M.
Publikováno v:
Journal of Chemical Physics; 7/28/2024, Vol. 161 Issue 4, p1-12, 12p