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A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III-V elements. The functional is machine-learned with Gaussian process re
Externí odkaz:
http://arxiv.org/abs/2407.11450
Development of new functional ceramics is important for several applications, including electrochemical batteries and fuel cells. Computational prescreening and selection of such materials can help discover novel materials but is challenging due to t
Externí odkaz:
http://arxiv.org/abs/2406.17197
Autor:
Manzhos, Sergei, Ihara, Manabu
Publikováno v:
J. Chem. Phys. 160, 021101 (2024)
Kernel methods such as kernel ridge regression and Gaussian process regressions with Matern type kernels have been increasingly used, in particular, to fit potential energy surfaces (PES) and density functionals, and for materials informatics. When t
Externí odkaz:
http://arxiv.org/abs/2311.10790
Publikováno v:
J. Chem. Phys. 159, 234115 (2023)
Machine learning of kinetic energy functionals (KEF), in particular kinetic energy density (KED) functionals, has recently attracted attention as a promising way to construct KEFs for orbital-free density functional theory (OF-DFT). Neural networks (
Externí odkaz:
http://arxiv.org/abs/2309.03482
Density-potential functional theory (DPFT) is an alternative formulation of orbital-free density functional theory that may be suitable for modeling the electronic structure of large systems. To date, DPFT has been applied mainly to quantum gases in
Externí odkaz:
http://arxiv.org/abs/2304.10059
Autor:
Manzhos, Sergei, Ihara, Manabu
Publikováno v:
Artificial Intelligence Chemistry 1, 100013 (2023)
Representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates (corresponding of different orders of coupling) are useful in quantum dynamics and other applications, especially where integratio
Externí odkaz:
http://arxiv.org/abs/2302.12013
Autor:
Manzhos, Sergei, Ihara, Manabu
Publikováno v:
J. Phys. Chem. A, 127, 7823-7835 (2023)
Feed-forward neural networks (NN) are a staple machine learning method widely used in many areas of science and technology. While even a single-hidden layer NN is a universal approximator, its expressive power is limited by the use of simple neuron a
Externí odkaz:
http://arxiv.org/abs/2301.05567
Autor:
Manzhos, Sergei, Ihara, Manabu
Publikováno v:
J. Chem. Phys. 158, 044111 (2023)
Kernel based methods including Gaussian process regression (GPR) and generally kernel ridge regression (KRR) have been finding increasing use in computational chemistry, including the fitting of potential energy surfaces and density functionals in hi
Externí odkaz:
http://arxiv.org/abs/2211.11170
We propose that the machinability of hard ceramics can be improved by reversible electrochemical doping. On the example of TiO2, we show in a combined density functional theory-molecular dynamics computational study that a small amount of intercalate
Externí odkaz:
http://arxiv.org/abs/2208.06268
Autor:
Yuriy Manzhos, Yevheniia Sokolova
Publikováno v:
Радіоелектронні і комп'ютерні системи, Vol 2024, Iss 1, Pp 127-142 (2024)
The subject: This study focuses on improving the quality of Cyber-Physical System (CPS) software by eliminating incorrect usage of units of measurement and orientation in C/C++ programs. Incorrect usage often leads to critical errors that conventiona
Externí odkaz:
https://doaj.org/article/c709c8a7187e4cd6ab459cfff98ddb79
