Ab initio interatomic potentials and the thermodynamic properties of fluids.

J Chem Phys

Centre for Molecular Simulation, Swinburne University of Technology, P.O. Box 218, Hawthorn, Victoria 3122, Australia.

Published: July 2017

Monte Carlo simulations with accurate ab initio interatomic potentials are used to investigate the key thermodynamic properties of argon and krypton in both vapor and liquid phases. Data are reported for the isochoric and isobaric heat capacities, the Joule-Thomson coefficient, and the speed of sound calculated using various two-body interatomic potentials and different combinations of two-body plus three-body terms. The results are compared to either experimental or reference data at state points between the triple and critical points. Using accurate two-body ab initio potentials, combined with three-body interaction terms such as the Axilrod-Teller-Muto and Marcelli-Wang-Sadus potentials, yields systematic improvements to the accuracy of thermodynamic predictions. The effect of three-body interactions is to lower the isochoric and isobaric heat capacities and increase both the Joule-Thomson coefficient and speed of sound. The Marcelli-Wang-Sadus potential is a computationally inexpensive way to utilize accurate two-body ab initio potentials for the prediction of thermodynamic properties. In particular, it provides a very effective way of extending two-body ab initio potentials to liquid phase properties.

Download full-text PDF

Source
http://dx.doi.org/10.1063/1.4991012DOI Listing

Publication Analysis

Top Keywords

interatomic potentials
12
thermodynamic properties
12
two-body initio
12
initio potentials
12
initio interatomic
8
isochoric isobaric
8
isobaric heat
8
heat capacities
8
joule-thomson coefficient
8
coefficient speed
8

Similar Publications

Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time.

View Article and Find Full Text PDF

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.

J Chem Inf Model

January 2025

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves.

View Article and Find Full Text PDF

Two-dimensional (2D) nanomaterials are at the forefront of potential technological advancements. Carbon-based materials have been extensively studied since synthesizing graphene, which revealed properties of great interest for novel applications across diverse scientific and technological domains. New carbon allotropes continue to be explored theoretically, with several successful synthesis processes for carbon-based materials recently achieved.

View Article and Find Full Text PDF

The successful design and deployment of next-generation nuclear technologies heavily rely on thermodynamic data for relevant molten salt systems. However, the lack of accurate force fields and efficient methods has limited the quality of thermodynamic predictions from atomistic simulations. Here we propose an efficient free energy framework for computing chemical potentials, which is the central free energy quantity behind many thermodynamic properties.

View Article and Find Full Text PDF

Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter (e.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!