Zobrazeno 1 - 10
of 10 348
pro vyhledávání: '"Suryanarayana, A."'
Autor:
Ohana, Ruben, McCabe, Michael, Meyer, Lucas, Morel, Rudy, Agocs, Fruzsina J., Beneitez, Miguel, Berger, Marsha, Burkhart, Blakesley, Dalziel, Stuart B., Fielding, Drummond B., Fortunato, Daniel, Goldberg, Jared A., Hirashima, Keiya, Jiang, Yan-Fei, Kerswell, Rich R., Maddu, Suryanarayana, Miller, Jonah, Mukhopadhyay, Payel, Nixon, Stefan S., Shen, Jeff, Watteaux, Romain, Blancard, Bruno Régaldo-Saint, Rozet, François, Parker, Liam H., Cranmer, Miles, Ho, Shirley
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the effi
Externí odkaz:
http://arxiv.org/abs/2412.00568
Autor:
Tian, Tian, Timmerman, Lucas R, Kumar, Shashikant, Comer, Ben, Medford, Andrew J, Suryanarayana, Phanish
Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides advantages in ha
Externí odkaz:
http://arxiv.org/abs/2411.18024
Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explore a common scenario in omics, where statistically independent cross-s
Externí odkaz:
http://arxiv.org/abs/2410.07501
We present a formalism for developing cyclic and helical symmetry-informed machine learned force fields (MLFFs). In particular, employing the smooth overlap of atomic positions descriptors with the polynomial kernel method, we derive cyclic and helic
Externí odkaz:
http://arxiv.org/abs/2408.07554
We present a framework for computing the shock Hugoniot using on-the-fly machine learned force field (MLFF) molecular dynamics simulations. In particular, we employ an MLFF model based on the kernel method and Bayesian linear regression to compute th
Externí odkaz:
http://arxiv.org/abs/2407.15290
Autor:
Jing, Xin, Suryanarayana, Phanish
We present an efficient real space formalism for hybrid exchange-correlation functionals in generalized Kohn-Sham density functional theory (DFT). In particular, we develop an efficient representation for any function of the real space finite-differe
Externí odkaz:
http://arxiv.org/abs/2406.16998
We present a spectral scheme for atomic structure calculations in pseudopotential Kohn-Sham density functional theory. In particular, after applying an exponential transformation of the radial coordinates, we employ global polynomial interpolation on
Externí odkaz:
http://arxiv.org/abs/2406.00534
We propose mixed boundary conditions for 3d conformal gravity consistent with variational principle in its second-order formalism that admit the chiral $\Lambda$-$\mathfrak{bms}_4$ algebra as their asymptotic symmetry algebra. This algebra is one of
Externí odkaz:
http://arxiv.org/abs/2405.20244
We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 ele
Externí odkaz:
http://arxiv.org/abs/2404.07961
Autor:
Suryanarayana, A., author
Publikováno v:
The Finance-Innovation Nexus: Implications for Socio-Economic Development