Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS

Thermophysical Properties and Phase Behavior of CO2 with Impurities: Insight from Molecular Simulations

Experimentally determining thermophysical properties for various compositions commonly found in CO2 transportation systems is extremely challenging. To overcome this challenge, we performed Monte Carlo (MC) and Molecular Dynamics (MD) simulations of CO2 rich mixtures to compute thermophysical properties such as densities, thermal expansion coefficients, isothermal compressibilities, heat capacities, Joule-Thomson coefficients, speed of sound, and viscosities at temperatures of (235-313) K and pressures of (20-200) bar. We computed thermophysical properties of pure CO2 and CO2 rich mixtures with N2, Ar, H2, and CH4 as impurities of (1-10) mol % and showed good agreement with available Equations of State (EoS). We showed that impurities decrease the values of thermal expansion coefficients, isothermal compressibilities, heat capacities, and Joule-Thomson coefficients in the gas phase, while these values increase in the liquid and supercritical phases. In contrast, impurities increase the value of speed of sound in the gas phase and decrease it in the liquid and supercritical phases. We present an extensive data set of thermophysical properties for CO2 rich mixtures with various impurities, which will help to design the safe and efficient operation of CO2 transportation systems.

Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations

A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs and triplets between processes is general to all potentials. Results are presented for a simulation box of argon, including full box and atom displacement calculations, which are relevant to Monte Carlo simulation. Data on speed-up are presented for up to 120 processes across four nodes. A 4-fold speed-up is observed over five processes, extending to 20-fold over 40 processes and 30-fold over 120 processes.

Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions

Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction data, using the machine learning technique, Gaussian Processes (GP). The GP approach needs no bespoke modification when the number or type of molecules is changed. Our method produces highly accurate interpolation from significantly fewer training points than typical approaches, meaning ab initio calculations can be performed at higher accuracy. As an exemplar we compute the PES for all three-body cross interactions for CO₂-Ar mixtures. From these we calculate the CO₂-Ar virial coefficients up to 5th order. The resulting virial equation of state (EoS) is convergent for densities up to the critical density. Where convergent, the EoS makes accurate first-principles predictions for a range of thermophysical properties for CO₂-Ar mixtures, including the compressibility factor, speed of sound and Joule-Thomson coefficient. Our method has great potential to make wide-ranging first-principles predictions for mixtures of comparably sized molecules. Such predictions can replace the need for expensive, laborious and repetitive experiments and inform the continuum models required for applications.

Gaussian process models of potential energy surfaces with boundary optimization

A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at a long range, and the crossover distance between this model and the Gaussian process is learnt from the training data. The results are presented for different implementations of this procedure, known as boundary optimization, across the following dimer systems: CO-Ne, HF-Ne, HF-Na⁺, CO₂-Ne, and (CO₂)₂. The technique reduces the number of training points, at fixed accuracy, by up to ∼49%, compared to our previous work based on a sequential learning technique. The approach is readily transferable to other statistical methods of prediction or modeling problems.

A full-dimensional ab initio intermolecular potential energy surface and rovibrational spectra for OC-HF and OC-DF

We present a full-dimensional ab initio intermolecular potential energy surface (IPES) for the OC-HF van der Waals complex. 3167 ab initio points were computed at the frozen-core (FC) explicitly correlated coupled cluster [FC-CCSD(T)-F12b] level, with the augmented correlation-consistent polarized valence quadruple-zeta basis set plus bond functions. Basis set superposition error correction was also considered by the full counterpoise procedure. Gaussian process regression (GPR) was used to map out the potential energy surface, while a multipole expansion method was employed to smooth the ab initio noise of intermolecular potential in the long range. The global minimum of -1248.364 cm^-1 was located at the linear configuration with the C atom pointing toward the H atom of the HF molecule. In addition, a local minimum of -602.026 cm^-1 was found at another linear configuration with the O atom pointing toward the H atom of the HF molecule. The eigenstates were calculated on the vibrational averaged four-dimensional IPESs with the mixed radial discrete variable representation/angular finite basis representation method and Lanczos propagation algorithm. The dissociation energy D₀ was calculated to be 701.827 cm^-1, well reproducing the experimental value of 732 ± 2 cm^-1. The dipole moment surfaces were also fitted by GPR from 3132 ab initio points calculated using the coupled cluster method [CCSD(T)] with AVTZ basis set plus bond functions. The frequencies and relative line intensities of rovibrational transitions in the HF (DF) and CO stretching bands were further calculated and compared well with the experimental results. These results indicate the high fidelity of the new IPES.

Gaussian Process Regression for Materials and Molecules

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.