Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Pederson, Ryan"'
Autor:
Pederson, Ryan, Burke, Kieron
Exact conditions have long been used to guide the construction of density functional approximations. But hundreds of empirical-based approximations tailored for chemistry are in use, many of which neglect these conditions in their design. We analyze
Externí odkaz:
http://arxiv.org/abs/2303.01766
Autor:
Mortveit, Henning S., Pederson, Ryan
Publikováno v:
Bulletin of Mathematical Biology 81.5 (2019): 1442-1460
We present mathematical techniques for exhaustive studies of long-term dynamics of asynchronous biological system models. Specifically, we extend the notion of $\kappa$-equivalence developed for graph dynamical systems to support systematic analysis
Externí odkaz:
http://arxiv.org/abs/2208.11732
Autor:
Crisostomo, Steven, Pederson, Ryan, Kozlowski, John, Kalita, Bhupalee, Cancio, Antonio C., Datchev, Kiril, Wasserman, Adam, Song, Suhwan, Burke, Kieron
We explore a variety of unsolved problems in density functional theory, where mathematicians might prove useful. We give the background and context of the different problems, and why progress toward resolving them would help those doing computations
Externí odkaz:
http://arxiv.org/abs/2207.05794
Over the past decade machine learning has made significant advances in approximating density functionals, but whether this signals the end of human-designed functionals remains to be seen. Ryan Pederson, Bhupalee Kalita and Kieron Burke discuss the r
Externí odkaz:
http://arxiv.org/abs/2205.01591
Publikováno v:
Phys. Rev. B 105, 245138 (2022)
We present conditional probability (CP) density functional theory (DFT) as a formally exact theory. In essence, CP-DFT determines the ground-state energy of a system by finding the CP density from a series of independent Kohn-Sham (KS) DFT calculatio
Externí odkaz:
http://arxiv.org/abs/2203.09647
Autor:
Pederson, Ryan, Kozlowski, John, Song, Ruyi, Beall, Jackson, Ganahl, Martin, Hauru, Markus, Lewis, Adam G. M., Yao, Yi, Mallick, Shrestha Basu, Blum, Volker, Vidal, Guifre
We demonstrate the use of Google's cloud-based Tensor Processing Units (TPUs) to accelerate and scale up conventional (cubic-scaling) density functional theory (DFT) calculations. Utilizing 512 TPU cores, we accomplish the largest such DFT computatio
Externí odkaz:
http://arxiv.org/abs/2202.01255
Kohn-Sham regularizer (KSR) is a differentiable machine learning approach to finding the exchange-correlation functional in Kohn-Sham density functional theory (DFT) that works for strongly correlated systems. Here we test KSR for weak correlation. W
Externí odkaz:
http://arxiv.org/abs/2110.14846
Autor:
Li, Li, Hoyer, Stephan, Pederson, Ryan, Sun, Ruoxi, Cubuk, Ekin D., Riley, Patrick, Burke, Kieron
Publikováno v:
Phys. Rev. Lett. 126, 036401 (2021)
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures. Prior knowledge embedded in the physics computation itself rarely
Externí odkaz:
http://arxiv.org/abs/2009.08551
Autor:
McCarty, Ryan J., Perchak, Dennis, Pederson, Ryan, Evans, Robert, Qiu, Yiheng, White, Steven R., Burke, Kieron
Publikováno v:
Phys. Rev. Lett. 125, 266401 (2020)
Density functional calculations can fail for want of an accurate exchange-correlation approximation. The energy can instead be extracted from a sequence of density functional calculations of conditional probabilities (CP-DFT). Simple CP approximation
Externí odkaz:
http://arxiv.org/abs/2007.01890
We investigate how different chemical environment influences magnetic properties of terbium(III) (Tb)-based single-molecule magnets (SMMs), using first-principles relativistic multireference methods. Recent experiments showed that Tb-based SMMs can h
Externí odkaz:
http://arxiv.org/abs/1905.10635