arXiv subjects

Mgcini Keith Phuthi

Publications and source records attributed to Mgcini Keith Phuthi.

3 recordsLinked to original sources

arXiv2025

Stability and Structure of Binary Metal Hydrides under Pressure, Electrochemical Potential and Combined Pressure-Electrochemistry

Metal hydrides can be tuned to have a diverse range of properties and find applications in hydrogen storage and superconductivity. Finding methods to control the synthesis of hydrides can open up new pathways to unlock novel hydride compounds with desired properties. We introduced the idea of utilizing electrochemistry as an additional tuning knob and in this work, we study the synthesis of binary metal hydrides using high pressure, electrochemistry and combined pressure-electrochemistry. Using density functional theory calculations, we predict the phase diagrams of selected transition metal hydrides under combined pressure and electrochemical conditions and demonstrate that the approach agrees well with experimental observations for most phases. We use the phase diagrams to determine trends in the stability of binary metal hydrides of scandium, yttrium and lanthanum as well as discuss the hydrogen-metal charge transfer at different pressures. Furthermore, we predict a diverse range of vanadium and chromium hydrides that could potentially be synthesized using pressure electrochemistry. These predictions highlight the value of exploring pressure-electrochemistry as a pathway to novel hydride synthesis.

cond-mat.mtrl-sci↗

arXiv2024

Vibrational Entropy and Free Energy of Solid Lithium using Covariance of Atomic Displacements Enabled by Machine Learning

Vibrational properties of solids are key to determining stability, response and functionality. However, they are challenging to computationally predict at Ab-Initio accuracy, even for elemental systems. Ab-Initio methods for modeling atomic interactions are limited in the system sizes and simulation times that can be achieved. Due to these limitations, Machine Learning Interatomic Potentials (MLIPs) are gaining popularity and success as a faster, more scalable approach for modeling atomic interactions, potentially at Ab-Initio accuracy. Even with faster potentials, methodologies for predicting entropy, free energy and vibrational properties vary in accuracy, cost and difficulty to implement. Using the Covariance of Atomic Displacements (CAD) to predict entropy, free energy and finite-temperature phonon dispersions is a promising approach but thorough benchmarking has been hampered by the cost of Ab-Initio methods for sampling. In this work, we use a MLIP and the CAD to characterize the convergence of the predicted properties and determine optimal sampling strategies. We focus on solid lithium at zero pressure, showing that the MLIP-CAD approach reproduces experimental entropy, phonon dispersions and the martensitic transition while also comparing to more established methods.

cond-mat.mtrl-sci↗

arXiv2023

Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials

The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties and ab-initio calculations are too costly. In this work, we train Machine Learning Interaction Potentials (MLIPs) on Density Functional Theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab-initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants and various surface properties inaccessible using DFT. We establish that there exists a Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets.

cond-mat.mtrl-sci↗