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A one-electron Schroedinger equation based on special one-electron potentials for atoms is shown to exist that produces orbitals for an arbitrary molecule that are sufficiently accurate to be used without modification to construct single-…

Chemical Physics · Physics 2022-05-16 Jerry L. Whitten

Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO$_2$ without the use of \textit{ab…

Materials Science · Physics 2024-07-29 Karim Zongo , Hao Sun , Claudiane Ouellet-Plamondon , Laurent Karim Béland

Lattice models parameterized using first-principles calculations constitute an effective framework to simulate the thermodynamic behavior of physical systems. The cluster expansion method is a flexible lattice-based method used extensively…

Materials Science · Physics 2023-01-09 Luis Barroso-Luque , Gerbrand Ceder

We provide a methodology for generating interatomic potentials for use in classical molecular dynamics simulations of atomistic phenomena occurring at energy scales ranging from lattice vibrations to crystal defects to high energy…

Materials Science · Physics 2009-12-03 Pratyush Tiwary , Axel van de Walle , Niels Grønbech-Jensen

Many standard structural quantities, such as order parameters and correlation functions, exist for common condensed matter systems, such as spherical and rod-like particles. However, these structural quantities are often insufficient for…

Soft Condensed Matter · Physics 2012-01-18 Aaron S. Keys , Christopher R. Iacovella , Sharon C. Glotzer

Efficient and accurate interatomic potential functions are critical to computational study of materials while searching for structures with desired properties. Traditionally, potential functions or energy landscapes are designed by experts…

Materials Science · Physics 2022-04-05 Andrew Eldridge , Alejandro Rodriguez , Ming Hu , Jianjun Hu

The complex atomic structures and defects of metal-oxide surfaces are vital for a variety of applications in material science and chemistry. While scanning probe microscopy allows accessing atomic-scale structures in real space, elemental…

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTP) are polynomial-like functions of…

A reduced-order model based on Proper Orthogonal Decomposition (POD) is proposed for the bidomain equations of cardiac electrophysiology. Its accuracy is assessed through electrocardiograms in various configurations, including myocardium…

Numerical Analysis · Mathematics 2012-07-23 Muriel Boulakia , Elisa Schenone , Jean-Frédéric Gerbeau

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we…

An effective potential function is critical for protein structure prediction and folding simulation. Simplified protein models such as those requiring only $C_\alpha$ or backbone atoms are attractive because they enable efficient search of…

Biomolecules · Quantitative Biology 2007-05-23 Jinfeng Zhang , Rong Chen , Jie Liang

Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the…

Materials Science · Physics 2025-12-09 Ikuma Kohata , Kaoru Hisama , Keigo Otsuka , Shigeo Maruyama

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are…

Existing feature selection methods fail to properly account for interactions between features when evaluating feature subsets. In this paper, we attempt to remedy this issue by using orthogonal variance decomposition to evaluate features.…

Machine Learning · Computer Science 2019-10-23 Firuz Kamalov

An energy functional for orbital based $O(N)$ calculations is proposed, which depends on a number of non orthogonal, localized orbitals larger than the number of occupied states in the system, and on a parameter, the electronic chemical…

mtrl-th · Physics 2016-09-07 Jeongnim Kim , Francesco Mauri , Giulia Galli

We present a simple, yet general, end-to-end deep neural network representation of the potential energy surface for atomic and molecular systems. This methodology, which we call Deep Potential, is "first-principle" based, in the sense that…

Computational Physics · Physics 2020-07-20 Jiequn Han , Linfeng Zhang , Roberto Car , Weinan E

A parameter method is introduced in order to estimate the relationship among the various variables of a system in equilibrium, where the potential energy functions are incompletely known or the quantum mechanical calculations very…

General Physics · Physics 2011-05-12 Walton R. Gutierrez

We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic…

Computational Physics · Physics 2022-04-25 Claudio Zeni , Kevin Rossi , Aldo Glielmo , Stefano De Gironcoli

The concept of basic chemical models is introduced, which is new from the standpoint of the physics of nonideal atomic plasma. This concept is based on the requirement of full conformity of the expression for free energy in the chemical…

Plasma Physics · Physics 2015-06-26 A. L. Khomkin , I. A. Mulenko , A. S. Shumikhin

For a given many-electron molecule, it is possible to define a corresponding one-electron Schr\"odinger equation, using potentials derived from simple atomic densities, whose solution predicts fairly accurate molecular orbitals for single-…

Chemical Physics · Physics 2022-05-16 Jerry L. Whitten