Ab Initio Methods

Ohno, Kaoru; Esfarjani, Keivan

doi:10.1007/978-3-662-56542-1_2

“…where ε 2B ref (k) is the 2B QM reference energy for the k th configuration in the 2B training set, with V elec (k), V pol (k), and V 2B disp (i) describing permanent electrostatics, polarization, and dispersion energy, respectively (Eqs. [3][4][5][6][7][8].…”

Section: E Parameterization and Trainingmentioning

confidence: 99%

“…Molecular-level computer simulations, such as molecular dynamics (MD) and Monte Carlo (MC) simulations, 1,2 have become an indispensable tool in molecular sciences, providing fundamental insights into structural, thermodynamic, and dynamical properties of molecular systems, from materials to biomolecules, which are difficult (if not impossible) to obtain by other means. [3][4][5][6][7][8] However, the level of realism and the predictive power of any MD and MC simulation depends sensitively on the accuracy of the potential energy function (PEF) used to represent the multidimensional potential energy surface (PES) of the molecular system in question. In the early days of computer simulations, due to limited computational resources, the only effectively suitable PEFs were empirically parameterized force fields (FFs) that adopted relatively simple expressions to describe intramolecular distortions and purely pairwise additive functions to describe intermolecular interactions.…”

Section: Introductionmentioning

confidence: 99%

MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions

Bull-Vulpe

¹

,

Riera

²

,

Goetz

³

et al. 2021

Preprint

0

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Many-body potential energy functions (MB-PEFs), which integrate data-driven representations of many-body short-range quantum mechanical interactions with physics-based representations of many-body polarization and long-range interactions, have recently been shown to provide high accuracy in the description of molecular interactions, from the gas to the condensed phase. Here, we present MB-Fit, a software infrastructure for the automated development of MB-PEFs for generic molecules within the TTM-nrg ("Thole-type model energy") and MB-nrg ("many-body energy") theoretical frameworks. Besides providing all the necessary computational tools for generating TTM-nrg and MB-nrg PEFs, MB-Fit provides a seamless interface with the MBX software, a many-body energy/force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, we believe that MB-Fit will enable routine, predictive computer simulations of generic (small) molecules in the gas, liquid, and solid phases, including, but not limited to, the modeling of isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams.

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“…Fortunately, recent advances in computing have made the investigation of the mechanics of fluids and materials on nano-scales feasible with the use of molecular models. Molecular models can be simulated using two techniques: molecular dynamics (102) and Monte Carlo (103) . The computational expense limits the use of these methods to a number of atoms relatively small compared to macro-scale problems.…”

Section: Multi-physics Modellingmentioning

confidence: 99%

Computational aerodynamics: Advances and challenges

Drikakis

¹

,

Kwak

²

,

Kiris

³

2016

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Computational aerodynamics, which complement more expensive empirical approaches, are critical for developing aerospace vehicles. During the past three decades, computational aerodynamics capability has improved remarkably, following advances in computer hardware and algorithm development. However, most of the fundamental computational capability realised in recent applications is derived from earlier advances, where specific gaps in solution procedures have been addressed only incrementally. The present article presents our view of the state of the art in computational aerodynamics and assessment of the issues that drive future aerodynamics and aerospace vehicle development. Requisite capabilities for perceived future needs are discussed, and associated grand challenge problems are presented.

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“…Molecular-level computer simulations, such as molecular dynamics (MD) and Monte Carlo (MC) simulations, 1,2 have become an indispensable tool in molecular sciences, providing fundamental insights into structural, thermodynamic, and dynamical properties of molecular systems, from materials to biomolecules, which are difficult (if not impossible) to obtain by other means. [3][4][5][6][7][8] However, the level of realism and the predictive power of any MD and MC simulation depends sensitively on the accuracy of the potential energy function (PEF) used to represent the multidimensional potential energy surface (PES) of the molecular system in question. In the early days of computer simulations, due to limited computational resources, the only effectively suitable PEFs were empirically parameterized force fields (FFs) that adopted relatively simple expressions to describe intramolecular distortions and purely pairwise additive functions to describe intermolecular interactions.…”

Section: Introductionmentioning

confidence: 99%

MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions

Bull-Vulpe

¹

,

Riera

²

,

Goetz

³

et al. 2021

Preprint

View full text Add to dashboard Cite

Many-body potential energy functions (MB-PEFs), which integrate data-driven representations of many-body short-range quantum mechanical interactions with physics-based representations of many-body polarization and long-range interactions, have recently been shown to provide high accuracy in the description of molecular interactions, from the gas to the condensed phase. Here, we present MB-Fit, a software infrastructure for the automated development of MB-PEFs for generic molecules within the TTM-nrg ("Thole-type model energy") and MB-nrg ("many-body energy") theoretical frameworks. Besides providing all the necessary computational tools for generating TTM-nrg and MB-nrg PEFs, MB-Fit provides a seamless interface with the MBX software, a many-body energy/force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, we believe that MB-Fit will enable routine, predictive computer simulations of generic (small) molecules in the gas, liquid, and solid phases, including, but not limited to, the modeling of isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams.

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Ab Initio Methods

Cited by 11 publications

References 622 publications

MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions

MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions

Computational aerodynamics: Advances and challenges

MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions

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