Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Kumar, Shashikant"'
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
Publikováno v:
Physical Review B 109, 235401 (2024)
In this work, we prescribe a theoretical framework aiming at predicting the position of monovacancy defects at the edges of zigzag graphene nanoribbons (ZGNRs) using Floquet-Bloch formalism, which can be experimentally observed through time- and angl
Externí odkaz:
http://arxiv.org/abs/2406.05643
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
We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the train
Externí odkaz:
http://arxiv.org/abs/2402.13450
We present a $\Delta$-machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine learned force field (MLFF) scheme based on the kernel method to capture t
Externí odkaz:
http://arxiv.org/abs/2310.06598
Autor:
Zhang, Boqin, Jing, Xin, Xu, Qimen, Kumar, Shashikant, Sharma, Abhiraj, Erlandson, Lucas, Sahoo, Sushree Jagriti, Chow, Edmond, Medford, Andrew J., Pask, John E., Suryanarayana, Phanish
SPARC is an accurate, efficient, and scalable real-space electronic structure code for performing ab initio Kohn-Sham density functional theory calculations. Version 2.0.0 of the software provides increased efficiency, and includes spin-orbit couplin
Externí odkaz:
http://arxiv.org/abs/2305.07679
M-SPARC is a Matlab code for performing ab initio Kohn-Sham Density Functional Theory simulations. Version 2.0.0 of the software further extends its capability to include relativistic effects, dispersion interactions, and advanced semilocal/nonlocal
Externí odkaz:
http://arxiv.org/abs/2212.05870
We study the bending of rectangular atomic monolayers along different directions from first principles. Specifically, choosing the phosphorene, GeS, TiS$_3$, and As$_2$S$_3$ monolayers as representative examples, we perform Kohn-Sham density function
Externí odkaz:
http://arxiv.org/abs/2208.00091
Publikováno v:
In Construction and Building Materials 9 August 2024 438