arXiv subjects

Andrew Horsfield

Publications and source records attributed to Andrew Horsfield.

4 recordsLinked to original sources

arXiv2026

Kinetic Cellular Model of Corrosion

Aqueous corrosion of metals involves multiple interconnected processes. Thus, computer simulation of corrosion as a whole needs to be able to describe the individual processes and how they influence each other. Atomistic simulations are designed to obtain detailed information for small regions of space over short times. Thus there are limits to the understanding that can be obtained by atomistic simulations alone. Here is presented a method that uses generalised rate equations to extend the length and time scales that can be accessed. It is shown to reduce to either the phase field or cellular automata methods in certain limits. The generalised kinetic equations can reproduce the behaviour described by both the Nernst-Planck and Butler-Volmer equations, which are frequently used to describe corrosion. In addition, the method can describe local rearrangements of atoms such as chemical reactions. Example results are shown for illustrative 1D and 2D problems, with good agreement being found with other methods.

cond-mat.mtrl-sci↗

arXiv2026

A Combined Tight Binding with Machine Learning Potential Model for Magnesium Compounds

We present a model for magnesium-based systems that combines density functional tight binding (DFTB) with MACE, a machine learning interatomic potential (DFTB+MACE). In this model, the conventional repulsive potential, pair potential, is replaced by a many-body MACE potential. The MACE component of the model is trained on the difference between density functional theory (DFT) energies and forces and the corresponding DFTB values, but neglecting the pair potential contribution. Using this model we performed structural relaxation of MgO-CO2 adsorption systems, molecular dynamics calculations of water clusters and phonon spectrum calculations of stable fcc-MgO and metastable bcc-MgO structures. We compare the performance of our model with a pure MACE model and with DFT. We demonstrate that the DFTB+MACE model achieves improved accuracy relative to DFTB with a pair potential, in many cases with only a moderate increase in computational cost. In addition, it can provide electronic structures that most of the machine learning potentials cannot. The training dataset, originally developed for MACE, may not fully represent all regions of the potential surface we may encounter during simulations. Expanding the dataset for a wider potential surface is expected to further enhance predictive accuracy of DFTB+MACE model. Overall, the resulting DFTB+MACE framework enables simulations at length and time scales beyond the reach of first-principles methods while retaining an explicit description of electronic structures, making it particularly attractive for studying charge-transfer in materials.

cond-mat.mtrl-sci↗

arXiv2023

Accelerating GW calculations through machine learned dielectric matrices

The GW approach produces highly accurate quasiparticle energies, but its application to large systems is computationally challenging, which can be largely attributed to the difficulty in computing the inverse dielectric matrix. To address this challenge, we develop a machine learning approach to efficiently predict density-density response functions (DDRF) in materials. For this, an atomic decomposition of the DDRF is introduced as well as the neighbourhood density-matrix descriptor both of which transform in the same way under rotations. The resulting DDRFs are then used to evaluate quasiparticle energies via the GW approach. This technique is called the ML-GW approach. To assess the accuracy of this method, we apply it to hydrogenated silicon clusters and find that it reliably reproduces HOMO-LUMO gaps and quasiparticle energy levels. The accuracy of the predictions deteriorates when the approach is applied to larger clusters than those included in the training set. These advances pave the way towards GW calculations of complex systems, such as disordered materials, liquids, interfaces and nanoparticles.

cond-mat.mtrl-sci↗

arXiv2021

Predicting polarizabilities of silicon clusters using local chemical environments

Calculating polarizabilities of large clusters with first-principles techniques is challenging because of the unfavorable scaling of computational cost with cluster size. To address this challenge, we demonstrate that polarizabilities of large hydrogenated silicon clusters containing thousands of atoms can be efficiently calculated with machine learning methods. Specifically, we construct machine learning models based on the smooth overlap of atomic positions (SOAP) descriptor and train the models using a database of calculated random-phase approximation polarizabilities for clusters containing up to 110 silicon atoms. We first demonstrate the ability of the machine learning models to fit the data and then assess their ability to predict cluster polarizabilities using k-fold cross validation. Finally, we study the machine learning predictions for clusters that are too large for explicit first-principles calculations and find that they accurately describe the dependence of the polarizabilities on the ratio of hydrogen to silicon atoms and also predict a bulk limit that is in good agreement with previous studies.

cond-mat.mtrl-sci↗