计算物理

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having…

Training machine learning interatomic potentials often requires optimizing a loss function composed of three variables: potential energies, forces, and stress. The contribution of each variable to the total loss is typically weighted using…

We advance the algorithm for ab initio calculations of Raman spectra for large systems via applying external electric field, and complement it by a code implementation we name RASCBEC. With the RASCBEC code, we have successfully benchmark…

Topological phonon states in crystalline materials have attracted significant research interests due to their importance for fundamental physical phenomena, yet their implication on phonon thermal transport remains largely unexplored. Here,…

Predicting and understanding the chaotic dynamics in complex systems is essential in various applications. However, conventional approaches, whether full-scale simulations or small-scale omissions, fail to offer a comprehensive solution.…

In (M Hodapp and A Shapeev 2020 Mach. Learn.: Sci. Technol. 1 045005), we have proposed an algorithm that fully automatically trains machine-learning interatomic potentials (MLIPs) during large-scale simulations, and successfully applied it…

We present two approaches to system identification, i.e. the identification of partial differential equations (PDEs) from measurement data. The first is a regression-based Variational System Identification procedure that is advantageous in…

We present a contribution to the field of system identification of partial differential equations (PDEs), with emphasis on discerning between competing mathematical models of pattern-forming physics. The motivation comes from developmental…

We propose to improve the convergence properties of the single-reference coupled cluster (CC) method through an augmented Lagrangian formalism. The conventional CC method changes a linear high-dimensional eigenvalue problem with exponential…

In this document, we examine exact and efficient numerical approaches to the MIT Bag Model, a theoretical framework used to describe the properties of bound quarks in Hadrons. We present the exact and Boundary Value Problem (BVP) numerical…

We present a mathematical and computational framework to couple the Keldysh non equilibrium quantum transport formalism with a nanoscale lattice Boltzmann method for the computational design of quantum-engineered nanofluidic devices.

Coherent X-ray Diffraction Imaging (CXDI) technique offers unique insights into the nanoscale world, enabling the reconstruction of 3D structures with a nanoscale resolution achieved through computational phase reconstruction from measured…

Multivariate functions of continuous variables arise in countless branches of science. Numerical computations with such functions typically involve a compromise between two contrary desiderata: accurate resolution of the functional…

Exploring ultrafast magnetization control in two-dimensional (2D) magnets through optically driven coherent phonons has been well-established. Yet, the microscopic interplay between spin dynamics and lattice degrees of freedom remains less…

The GW approximation is widely used for reliable and accurate modeling of single-particle excitations. It also serves as a starting point for many theoretical methods, such as its use in the Bethe-Salpeter equation (BSE) and dynamical…

Accurate electronic bandstructures of solids are indispensable for a wide variety of applications and should provide a sound prediction of phonon-induced band gap renormalization at finite temperatures. We employ our previously introduced…

In this study, we derive the heat flux formula for the Allegro model, one of machine-learning interatomic potentials using the equivariant deep neural network, to calculate lattice thermal conductivity using the homogeneous non-equilibrium…

We propose a data-driven approach for constructing machine-learning interatomic potentials (MLIPs) trained under a regularization with the aim of avoiding nonphysical heat flux. Specifically, we introduce a regularization term for the heat…

We propose an improved twist-averaging scheme for quantum Monte Carlo methods that use converged Kohn-Sham or Hartree-Fock orbitals as the reference. This twist-averaging technique is tailored to sample the Brillouin zone of magnetic…

We investigate the use of machine learning for solving analytic problems in theoretical physics. In particular, symbolic regression (SR) is making rapid progress in recent years as a tool to fit data using functions whose overall form is…