Zobrazeno 1 - 10
of 785
pro vyhledávání: '"HOUSTON, PAUL"'
Autor:
Yu, Qi, Ma, Ruitao, Qu, Chen, Conte, Riccardo, Nandi, Apurba, Pandey, Priyanka, Houston, Paul L., Zhang, Dong H., Bowman, Joel M.
Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in at
Externí odkaz:
http://arxiv.org/abs/2412.00522
This short paper reports a study of the electronic dissociation energies, De, of water clusters from direct ab initio (mostly CCSD(T)) calculations and the q-AQUA and MB-pol potentials. These clusters range in size from 6-25 monomers. These are all i
Externí odkaz:
http://arxiv.org/abs/2408.05234
Autor:
Nandi, Apurba, Pandey, Priyanka, Houston, Paul L., Qu, Chen, Yu, Qi, Conte, Riccardo, Tkatchenko, Alexandre, Bowman, Joel M.
Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently,"$\Delta$-machine learning" has been used to elevate the qu
Externí odkaz:
http://arxiv.org/abs/2407.20050
We study the fully explicit numerical approximation of a semilinear elliptic boundary value model problem, which features a monomial reaction and analytic forcing, in a bounded polygon $\Omega\subset\mathbb{R}^2$ with a finite number of straight edge
Externí odkaz:
http://arxiv.org/abs/2404.18569
Autor:
Ge, Fuchun, Wang, Ran, Qu, Chen, Zheng, Peikun, Nandi, Apurba, Conte, Riccardo, Houston, Paul L., Bowman, Joel M., Dral, Pavlo O.
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from
Externí odkaz:
http://arxiv.org/abs/2403.11216
Autor:
Pandey, Priyanka, Arandhara, Mrinal, Houston, Paul L., Qu, Chen, Conte, Riccardo, Bowman, Joel M., Ramesh, Sai G.
Here we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reporte
Externí odkaz:
http://arxiv.org/abs/2402.11158
Autor:
Houston, Paul L., Qu, Chen, Yu, Qi, Pandey, Priyanka, Conte, Riccardo, Nandi, Apurba, Bowman, Joel M.
Publikováno v:
J. Chem. Theory Comput. 2024
Assessments of machine-learned (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutationally invariant polynomial (PIP) method for ethanol, using the widely
Externí odkaz:
http://arxiv.org/abs/2401.09316
Autor:
Boyer, Kyle, Brubaker, Laura, Everly, Kyle, Herriman, RIchard, Houston, Paul, Ruckle, Sean, Scobie, Rory, Ulanday, Ian
The University of Arizona Baja Racing Team competes annually in an intense off-road racing competition. This year’s car features a distributed sensor network capable of displaying useful data to the driver, the benefits and technical aspects of whi
Externí odkaz:
http://hdl.handle.net/10150/627007
http://arizona.openrepository.com/arizona/handle/10150/627007
http://arizona.openrepository.com/arizona/handle/10150/627007
Autor:
Boyer, Kyle, Brubaker, Laura, Everly, Kyle, Herriman, RIchard, Houston, Paul, Ruckle, Sean, Scobie, Rory, Ulanday, Ian
The drivers of the University of Arizona Baja racing team must be intensely focused on tackling the jumps, boulders, mud bogs, and other challenges in the four-hour endurance race. These obstacles are just as demanding on the vehicle as the driver, s
Externí odkaz:
http://hdl.handle.net/10150/626973
http://arizona.openrepository.com/arizona/handle/10150/626973
http://arizona.openrepository.com/arizona/handle/10150/626973
In this article we consider the iterative solution of the linear system of equations arising from the discretisation of the poly-energetic linear Boltzmann transport equation using a discontinuous Galerkin finite element approximation in space, angle
Externí odkaz:
http://arxiv.org/abs/2310.14333