Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Batzner, Simon"'
Autor:
Yang, Sherry, Batzner, Simon, Gao, Ruiqi, Aykol, Muratahan, Gaunt, Alexander L., McMorrow, Brendan, Rezende, Danilo J., Schuurmans, Dale, Mordatch, Igor, Cubuk, Ekin D.
Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from t
Externí odkaz:
http://arxiv.org/abs/2409.06762
Autor:
Goodwin, Zachary A. H., Wenny, Malia B., Yang, Julia H., Cepellotti, Andrea, Ding, Jingxuan, Bystrom, Kyle, Duschatko, Blake R., Johansson, Anders, Sun, Lixin, Batzner, Simon, Musaelian, Albert, Mason, Jarad A., Kozinsky, Boris, Molinari, Nicola
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' as they can be mixed to precisely tailor the physiochemical properties.
Externí odkaz:
http://arxiv.org/abs/2403.01980
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory. Predicting th
Externí odkaz:
http://arxiv.org/abs/2310.01117
Autor:
Phuthi, Mgcini Keith, Yao, Archie Mingze, Batzner, Simon, Musaelian, Albert, Kozinsky, Boris, Cubuk, Ekin Dogus, Viswanathan, Venkatasubramanian
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at w
Externí odkaz:
http://arxiv.org/abs/2305.06925
This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combination of innovative model architecture, massive parallelization, and mod
Externí odkaz:
http://arxiv.org/abs/2304.10061
Autor:
Owen, Cameron J., Torrisi, Steven B., Xie, Yu, Batzner, Simon, Bystrom, Kyle, Coulter, Jennifer, Musaelian, Albert, Sun, Lixin, Kozinsky, Boris
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across
Externí odkaz:
http://arxiv.org/abs/2302.12993
Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of t
Externí odkaz:
http://arxiv.org/abs/2211.09866
Autor:
Batatia, Ilyes, Batzner, Simon, Kovács, Dávid Péter, Musaelian, Albert, Simm, Gregor N. C., Drautz, Ralf, Ortner, Christoph, Kozinsky, Boris, Csányi, Gábor
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic Cluster Expansion (ACE), which unified many of the earlier ideas aroun
Externí odkaz:
http://arxiv.org/abs/2205.06643
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 Lutz.
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references.
Machine-Learning Interatomic Force-Fields have shown
Cataloged from PDF version of thesis.
Includes bibliographical references.
Machine-Learning Interatomic Force-Fields have shown
Externí odkaz:
https://hdl.handle.net/1721.1/122525