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We present an efficient numerical method for simulating the low-energy properties of disordered many-particle systems. The method which is based on the quantum-chemical configuration interaction approach consists in diagonalizing the…

Mesoscale and Nanoscale Physics · Physics 2009-10-31 Thomas Vojta , Frank Epperlein , Michael Schreiber

The study of quantum Coulomb systems at equilibrium is important for understanding properties of matter in many physical situations. Screening, recombination and van der Waals forces are basic phenomena which result from the interplay of…

Statistical Mechanics · Physics 2010-10-05 Angel Alastuey

We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schr\"odinger equation in configuration space. Traditional Configuration Interaction (CI) methods construct the wavefunction as a linear combination…

The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a…

Computational Physics · Physics 2024-11-19 Dedi Wang , Pratyush Tiwary

We develop an improved stochastic formalism for the Bethe-Salpeter equation, based on an exact separation of the effective-interaction $W$ to two parts, $W=(W-v_W)+v_W$ where the latter is formally any translationally-invariant interaction…

Chemical Physics · Physics 2023-05-03 Nadine C. Bradbury , Tucker Allen , Minh Nguyen , Khaled Ibrahim , Daniel Neuhauser

This paper provides an error estimate for the u-series method of the Coulomb interaction in molecular dynamics simulations. We show that the number of truncated Gaussians $M$ in the u-series and the base of interpolation nodes $b$ in the…

Numerical Analysis · Mathematics 2025-01-07 Jiuyang Liang , Zhenli Xu , Qi Zhou

The eigenvalue problem of quantum many-body systems is a fundamental and challenging subject in condensed matter physics, since the dimension of the Hilbert space (and hence the required computational memory and time) grows exponentially as…

Disordered Systems and Neural Networks · Physics 2021-05-12 Chen-Yu Liu , Daw-Wei Wang

We study the geometric ergodicity and the long time behavior of the Random Batch Method for interacting particle systems, which exhibits superior numerical performance in recent large-scale scientific computing experiments. We show that for…

Probability · Mathematics 2022-05-16 Shi Jin , Lei Li , Xuda Ye , Zhennan Zhou

Long-range and multi-body interactions are crucial for quantum simulation and quantum computation. Yet, their practical realization using elementary pairwise interactions remains an outstanding challenge. We propose an experimental scheme…

Quantum Physics · Physics 2026-05-19 Runmin Wu , Bing Yang , Pieter W. Claeys , Hongzheng Zhao

We present a new method, in the following called MMM2D, to accurately calculate the electrostatic energy and forces on charges being distributed in a two dimensional periodic array of finite thickness. It is not based on an Ewald summation…

Soft Condensed Matter · Physics 2009-11-07 Axel Arnold , Christian Holm

We present a real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory, leveraging the low-rank nature of the frequency-dependent density response operator. In…

Computational Physics · Physics 2025-04-03 Boqin Zhang , Shikhar Shah , John E. Pask , Edmond Chow , Phanish Suryanarayana

In recent years, reduced basis methods (RBMs) have been adapted to the many-body eigenvalue problem and they have been used, largely in nuclear physics, as fast emulators able to bypass expensive direct computations while still providing…

Superconductivity · Physics 2023-04-19 Virgil V. Baran , Denis R. Nichita

Classical Ewald methods for Coulomb and Stokes interactions rely on ``kernel-splitting," using decompositions based on Gaussians to divide the resulting potential into a near field and a far field component. Here, we show that a more…

Numerical Analysis · Mathematics 2025-09-29 Ludvig af Klinteberg , Leslie Greengard , Shidong Jiang , Anna-Karin Tornberg

We establish efficient algorithms for weakly-interacting quantum spin systems at arbitrary temperature. In particular, we obtain a fully polynomial-time approximation scheme for the partition function and an efficient approximate sampling…

Quantum Physics · Physics 2026-01-30 Ryan L. Mann , Gabriel Waite

The Poisson-Boltzmann equation (PBE) is a nonlinear elliptic PDE that arises in biomolecular modeling and is a fundamental tool for structural biology. It is used to calculate electrostatic potentials around an ensemble of fixed charges…

Numerical Analysis · Mathematics 2017-10-12 Cleophas Kweyu , Lihong Feng , Matthias Stein , Peter Benner

The starting point is the problem of finding the interaction energy of two coinciding homogeneous cubic charge distributions. The brute force method of subdividing the cube into $N^3$ sub-cubes and doing the sums results in slow convergence…

Atomic and Molecular Clusters · Physics 2007-05-23 Hanno Essen

We propose a novel method for the efficient and accurate iterative solution of frequency domain integral equations (IEs) that are used for large/multi-scale electromagnetic scattering problems. The proposed method uses a novel…

Numerical Analysis · Mathematics 2025-12-22 Enes Koç , Mert Kalfa , Secil E. Dogan , Vakur B. Ertürk

In the following paper we reconsider a recently introduced numerical scheme. The method was designed for a wide class of size structured population models as a variation of the Escalator Boxcar Train (EBT) method, which is commonly used in…

Analysis of PDEs · Mathematics 2015-05-08 Piotr Gwiazda , Piotr Orliński , Agnieszka Ulikowska

We consider the development of Monte Carlo schemes for molecules with Coulomb interactions. We generalize the classic algorithms of Bird and Nanbu-Babovsky for rarefied gas dynamics to the Coulomb case thanks to the approximation introduced…

Computational Physics · Physics 2015-03-17 Giacomo Dimarco , Russell Caflisch , Lorenzo Pareschi

Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive…

Machine Learning · Statistics 2014-10-27 Vincent Dumoulin , Ian J. Goodfellow , Aaron Courville , Yoshua Bengio