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Applied magnetic fields can couple to atomic displacements via generalized Lorentz forces, which are commonly expressed as gyromagnetic $g$ factors. We develop an efficient first-principles methodology based on density-functional…

Materials Science · Physics 2022-03-23 Asier Zabalo , Cyrus E. Dreyer , Massimiliano Stengel

Constraints can be interpreted in a broad sense as any kind of explicit restriction over the parameters. While some constraints are defined directly on the parameter space, when they are instead defined by known behaviour on the model,…

Methodology · Statistics 2015-02-27 Shirin Golchi , David A. Campbell

We use density functional theory to describe the phase behaviors of rigid molecules. The construction of kernel function G(x, P, x, P) is discussed. Excluded-volume potential is calculated for two types of molecules with C_{2v} symmetry.…

Mathematical Physics · Physics 2017-11-23 Jie Xu , Pingwen Zhang

Torsional-space Monte Carlo simulations of flexible molecules are usually based on the assumption that all values of dihedral angles have equal probability in the absence of atomic interactions. In the present paper it is shown that this…

Chemical Physics · Physics 2007-05-23 Andreas Kraemer

An algorithm to compute efficiently the first two derivatives of (very) large multideterminant wavefunctions for quantum Monte Carlo calculations is presented. The calculation of determinants and their derivatives is performed using the…

Chemical Physics · Physics 2017-04-20 Anthony Scemama , Thomas Applencourt , Emmanuel Giner , Michel Caffarel

In the simulation of biological molecules, it is customary to impose constraints on the fastest degrees of freedom to increase the time step. The evaluation of the involved constraint forces must be performed in an efficient manner, for…

Computational Physics · Physics 2019-11-01 Pablo García-Risueño

The site-dependent g factor of a single magnetic molecule, with intramolecular resolution, is demonstrated for the first time by low-temperature, high-magnetic-field scanning tunneling microscopy of dehydrogenated Mn-phthalocyanine…

Mesoscale and Nanoscale Physics · Physics 2020-04-07 Liwei Liu , Kai Yang , Yuhang Jiang , Boqun Song , Wende Xiao , Shiru Song , Shixuan Du , Min Ouyang , Werner A. Hofer , Antonio H. Castro Neto , Hong-Jun Gao

The numerical computations of many quantities of theoretical and phenomenological interest are plagued by statistical errors which increase exponentially with the distance of the sources in the relevant correlators. Notable examples are…

High Energy Physics - Lattice · Physics 2018-04-18 Leonardo Giusti , Marco Cè , Stefan Schaefer

We present a simple and general formalism to compute efficiently the derivatives of a multi-determinant Jastrow-Slater wave function, the local energy, the interatomic forces, and similar quantities needed in quantum Monte Carlo. Through a…

Materials Science · Physics 2016-06-22 Claudia Filippi , Roland Assaraf , Saverio Moroni

In many application areas, data are collected on a categorical response and high-dimensional categorical predictors, with the goals being to build a parsimonious model for classification while doing inferences on the important predictors.…

Methodology · Statistics 2013-01-22 Yun Yang , David B. Dunson

Motivated mainly by applications to partial differential equations with random coefficients, we introduce a new class of Monte Carlo estimators, called Toeplitz Monte Carlo (TMC) estimator for approximating the integral of a multivariate…

Numerical Analysis · Mathematics 2021-01-14 Josef Dick , Takashi Goda , Hiroya Murata

Techniques for simulating molecules whose conformations satisfy constraints are presented. A method for selecting appropriate moves in Monte Carlo simulations is given. The resulting moves not only obey the constraints but also maintain…

Computational Physics · Physics 2007-05-23 Charles F. F. Karney , Jason E. Ferrara

In this work, we introduce an algorithm to compute the derivatives of physical observables along the constrained subspace when flexible constraints are imposed on the system (i.e., constraints in which the hard coordinates are fixed to…

Computational Physics · Physics 2011-09-22 Pablo Echenique , Claudio N. Cavasotto , Monica De Marco , Pablo García-Risueño , J. L. Alonso

Molecular modeling at the quantum level requires choosing a parameterization of the wavefunction that both respects the required particle symmetries, and is scalable to systems of many particles. For the simulation of fermions, valid…

Machine Learning · Computer Science 2023-11-10 Jack Richter-Powell , Luca Thiede , Alán Asparu-Guzik , David Duvenaud

We present a new quantum Monte Carlo algorithm suitable for generically complex problems, such as systems coupled to external magnetic fields or anyons in two spatial dimensions. We find that the choice of gauge plays a nontrivial role, and…

Condensed Matter · Physics 2009-10-22 Lizeng Zhang , Geoff Canright , Ted Barnes

In order to find the equilibrium geometries of molecules and solids and to perform ab initio molecular dynamics, it is necessary to calculate the forces on the nuclei. We present a correlated sampling method to efficiently calculate…

Condensed Matter · Physics 2009-10-31 Claudia Filippi , C. J. Umrigar

A compression algorithm is introduced for multi-determinant wave functions which can greatly reduce the number of determinants that need to be evaluated in quantum Monte Carlo calculations. We have devised an algorithm with three levels of…

Computational Physics · Physics 2015-06-17 Gihan L. Weerasinghe , Pablo Lopez Rios , Richard J. Needs

Alignment of molecules through electric fields minimizes the averaging over orientations, e. g., in single particle imaging experiments. The response of molecules to external ac electric fields is governed by their polarizability tensor,…

Chemical Physics · Physics 2019-04-05 Muhamed Amin , Hebatallah Samy , Jochen Küpper

The prediction of the effective elastic properties of polymer bonded explosives using direct numerical simulations is computationally expensive because of the high volume fraction of particles in these particulate composites ($\sim$0.90)…

Materials Science · Physics 2012-01-13 Biswajit Banerjee , Daniel O. Adams

Bayesian models have become very popular over the last years in several fields such as signal processing, statistics, and machine learning. Bayesian inference requires the approximation of complicated integrals involving posterior…

Computation · Statistics 2021-07-20 Luca Martino , Víctor Elvira
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