Potential energy surfaces (PESs) for use in dynamics calculations of few-atom reactive systems are commonly modeled as functional forms fitting or interpolating a set of ab initio energies computed at many nuclear configurations. An automated procedure is here proposed for optimal configuration-space sampling in generating this set of energies as part of the grid-empowered molecular simulator GEMS (Laganà et al., J. Grid Comput. 2010, 8, 571-586). The scheme is based on a space-reduced formulation of the so-called bond-order variables allowing for a balanced representation of the attractive and repulsive regions of a diatom configuration space. Uniform grids based on space-reduced bond-order variables are proven to outperform those defined on the more conventional bond-length variables in converging the fitted/interpolated PES to the computed ab initio one with increasing number of grid points. Benchmarks are performed on the one- and three-dimensional prototype systems H2 and H3 using both a local-interpolation (modified Shepard) and a global-fitting (Aguado-Paniagua) scheme.
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http://dx.doi.org/10.1021/acs.jpca.5b10018 | DOI Listing |
J Chem Phys
December 2024
Dipartimento di Fisica Ettore Pancini, Università di Napoli Federico II, Monte S. Angelo, I-80126 Napoli, Italy.
Quantum Monte Carlo (QMC) methods represent a powerful family of computational techniques for tackling complex quantum many-body problems and performing calculations of stationary state properties. QMC is among the most accurate and powerful approaches to the study of electronic structure, but its application is often hindered by a steep learning curve; hence it is rarely addressed in undergraduate and postgraduate classes. This tutorial is a step toward filling this gap.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, United States.
Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schrödinger equation (SE) by integrating various neural network (NN)-based wave function ansatzes with variational Monte Carlo methods. Nevertheless, such NN-based methods are all based on the Born-Oppenheimer approximation (BOA) and require computationally expensive training for each nuclear configuration. In this work, we propose a novel NN architecture, SchrödingerNet, to solve the full electronic-nuclear SE by defining a loss function designed to equalize local energies across the system.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States.
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 low-dimensional collective variable (CV) space and then partitioning it into bins. The efficacy of WE simulations heavily depends on the selection of CVs and binning schemes.
View Article and Find Full Text PDFPhys Rev E
October 2024
Peter Grünberg Institut (PGI-14), Forschungszentrum Jülich GmbH, Jülich, Germany.
Physics-based Ising machines (IM) have been developed as dedicated processors for solving hard combinatorial optimization problems with higher speed and better energy efficiency. Generally, such systems employ local search heuristics to traverse energy landscapes in searching for optimal solutions. Here, we quantify and address some of the major challenges met by IMs by extending energy-landscape geometry visualization tools known as disconnectivity graphs.
View Article and Find Full Text PDFJ Phys Chem A
November 2024
Pritzker School of Molecular Engineering, The University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United States.
The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources.
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