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Tensor-optimized antisymmetrized molecular dynamics (TOAMD) is the basis of the successive variational method for nuclear many-body problem. We apply TOAMD to finite nuclei to be described by the central interaction with strong short-range…

Nuclear Theory · Physics 2017-04-20 Takayuki Myo , Hiroshi Toki , Kiyomi Ikeda , Hisashi Horiuchi , Tadahiro Suhara

The Lorentz covariant statistical physics and thermodynamics is formulated within the preferred frame approach. The transformation laws for geometrical and mechanical quantities such as volume and pressure as well as the Lorentz-invariant…

Statistical Mechanics · Physics 2007-05-23 J. Rembielinski , K. A. Smolinski , G. Duniec

We discuss a method to calculate with quantum molecular dynamics simulations the rate of energy exchanges between electrons and ions in two-temperature plasmas, liquid metals and hot solids. Promising results from this method were recently…

Computational Physics · Physics 2020-02-05 Jacopo Simoni , Jérôme Daligault

Using very long molecular dynamics simulation runs, temperature protocols spanning up to five orders of magnitude in time-scales are performed to investigate thermally activated structural relaxation in a model amorphous solid. The…

Materials Science · Physics 2017-10-11 P. M. Derlet , R. Maass

Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can manifest under specific conditions. In this work, we develop a method in which Bayesian inference is…

Materials Science · Physics 2023-09-06 Timofei Miryashkin , Olga Klimanova , Vladimir Ladygin , Alexander Shapeev

This paper presents a study of accuracy issues in thermal modeling of high power LED modules on system level. Both physical as well as numerical accuracy issues are addressed. Incorrect physical assumptions may result in seemingly correct,…

Materials Science · Physics 2007-09-13 D. Benoy

We develop a theory of the effective disorder temperature in glass-forming materials driven away from thermodynamic equilibrium by external forces. Our basic premise is that the slow configurational degrees of freedom of such materials are…

Materials Science · Physics 2015-05-13 Eran Bouchbinder , J. S. Langer

The two-dimensional (2D) Hubbard model has long attracted interest for its rich phase diagram and its relevance to high-$T_c$ superconductivity. However, reliable finite-temperature studies remain challenging due to the exponential…

Strongly Correlated Electrons · Physics 2025-10-30 Changkai Zhang , Jan von Delft

The features for the unsteady process of thermal equilibration ("the fast motions") in a one-dimensional harmonic crystal lying in a viscous environment (e.g., a gas) are under investigation. It is assumed that initially the displacements…

Statistical Mechanics · Physics 2021-02-16 Serge N. Gavrilov , Anton M. Krivtsov

We derive the optimal estimates of the free energies of an arbitrary number of thermodynamic states from nonequilibrium work measurements; the work data are collected from forward and reverse switching processes and obey a fluctuation…

Statistical Mechanics · Physics 2009-11-11 Paul Maragakis , Martin Spichty , Martin Karplus

A new simulated tempering method, which is referred to as simulated tempering umbrella sampling, for calculating the free energy of chemical reactions is proposed. First principles molecular dynamics simulations with this simulated…

Statistical Mechanics · Physics 2012-06-05 Yoshiharu Mori , Yuko Okamoto

Many-body correlations developed in the constrained molecular dynamics approach in the frame-work of the CoMD model are analyzed in the case of a finite range effective interaction. The study is performed in the case of infinite nuclear…

Nuclear Theory · Physics 2021-12-01 Massimo Papa

We upper- and lower-bound the optimal precision with which one can estimate an unknown Hamiltonian parameter via measurements of Gibbs thermal states with a known temperature. The bounds depend on the uncertainty in the Hamiltonian term…

Douglas-Rachford splitting and its equivalent dual formulation ADMM are widely used iterative methods in composite optimization problems arising in control and machine learning applications. The performance of these algorithms depends on…

Optimization and Control · Mathematics 2019-06-28 Jacob H. Seidman , Mahyar Fazlyab , Victor M. Preciado , George J. Pappas

Fast estimation of the single-particle density matrix is key to many applications in quantum chemistry and condensed matter physics. The best numerical methods leverage the fact that the density matrix elements $f(H)_{ij}$ decay rapidly…

Statistical Mechanics · Physics 2018-04-17 Zhentao Wang , Gia-Wei Chern , Cristian D. Batista , Kipton Barros

In these lecture notes, the basic principles of stochastic thermodynamics are developed starting with a closed system in contact with a heat bath. A trajectory undergoes Markovian transitions between observable meso-states that correspond…

Statistical Mechanics · Physics 2018-06-13 Udo Seifert

The thermal and statistical properties of hadronic matter under some extreme conditions are investigated using an exactly solvable canonical ensemble model. A unified model describing both the fragmentation of nuclei and the thermal…

Nuclear Theory · Physics 2008-11-26 K. C. Chase , A. Z. Mekjian , P. Meenakshisundaram

The extended Lagrangian molecular dynamics (XLMD) method provides a useful framework for reducing the computational cost of a class of molecular dynamics simulations with constrained latent variables. The XLMD method relaxes the constraints…

Numerical Analysis · Mathematics 2020-10-16 Dong An , Lin Lin , Michael Lindsey

An often used rule that the thermal correction to the self-energy is the thermal phase-space times the forward scattering amplitude from target particles is shown to be the leading term in an exact multiple scattering expansion. Starting…

High Energy Physics - Phenomenology · Physics 2008-11-26 Sangyong Jeon , Paul J. Ellis

Analyses of accelerated (momentum-based) gradient descent usually assume bounded condition number to obtain exponential convergence rates. However, in many real problems, e.g., kernel methods or deep neural networks, the condition number,…

Machine Learning · Computer Science 2018-03-06 Chaoyue Liu , Mikhail Belkin