Authors: Bhullar, Mangladeep
Source: University Library (University of Saskatchewan) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Study of Potential Energy Surfaces of Group 14 Elements Scientific and technological advancements in numerous areas of physical sciences and engineering rely on the ability to understand and manipulate the properties of matter at the atomic and molecular scales. Density Functional Theory (DFT) excels in approximating the fundamental equations, the Schrödinger equation that describes the quantum behaviour of atoms and molecules. Although DFT is considered the core of continually growing scientific literature, it is prohibitively expensive for simulating large systems requiring long time scales and is generally limited to moderate-sized systems (a few hundred atoms). While it is essential to follow the principles from the theory, it is important to recognise that the development and modification of simulation algorithms is an art. The development of new and innovative methods to solve the unique challenges of complex systems is essential to understand the behaviour of complex systems where conventional methods are insufficient. Therefore, advancements in computational techniques can be achieved through the amalgamation of classical mechanics, statistical mechanics, and quantum mechanics to offer heuristic descriptions, provide additional theoretical analysis, and introduce necessary approximations to develop practical workable approaches for more complex problems. Machine learning has become pervasive in materials modelling, facilitating large-scale atomistic simulations with quantum-mechanical accuracy and even accelerating the exploration of the potential energy surface. This work overcomes the challenges posed by DFT and employs a state- of-the-art integration of machine learning and ab initio methods to accelerate the investigation of the potential energy surfaces of group 14 elements, both dynamically and statically. The first section of this work presents an exploration of the potential energy surface of Silicon dynamically. This project investigates the reconstructive phase transition in large-scale Silicon systems using a highly efficient computational framework, with computational cost proportional to system size. The study employs a well-trained machine learning potential to simulate phase transitions in bulk Si. Using an integrated approach of machine learning driven metadynamics simulation method, the phase transition and the consequent development of grains and dislocation defects within the system were examined. In addition, the new method is used to explore the phase transitions of bulk silicon under pressure conditions of 10GPa and 25GPa. This approach reveals the transition paths and formation of polycrystalline silicon systems under specific stress conditions, highlighting the potency of data-driven metadynamics simulations in gaining insights into complex material behaviours in large-scale systems. The second section of this work focuses on the exploration of the potential energy surface of Germanium statically. In this study, we conducted an extensive computational search to find germanium allotropes with a direct band gap and low energy using a sensible random structure search approach informed by data-derived interatomic potentials. Among the predicted allotropes, we identified a hexagonal 8H structure with the lowest energy compared to all known germanium allotropes and only 5 meV/atom above the ground state cubic diamond structure. Compared to the cubic diamond phase, which has complete cubicity, the 8H phase consists of 3/4 cubicity and 1/4 hexagonality. This structural motif, coupled with band structure back folding in the hexagonal Brillouin zone, results in a direct band gap of 0.25 eV. Future research could explore alloying 8H germanium with silicon to optimise the bandgap energy and optical transitions, potentially achieving lifetimes comparable to those of group III-V semiconductors like GaAs, thereby improving the viability of Ge polytypic structures for photovoltaic applications. The final section of this work focuses on the static exploration of the potential energy surface of silicon. High-order hexagonal silicon polytypes (up to 24H) are investigated as possible candidates for bandgap engineering. These polytypic structures are systematically constructed and also identified using the Ab Initio Random Structure Search (AIRSS) approach accelerated using Ephemeral Data-Derived Potentials (EDDP). All hexagonal Si polytypes are mechanically and dynamically stable, featuring indirect band gaps ranging from 0.97 – 1.12 eV, with elastic constants satisfying Born stability criteria. The study further focuses on the indirect-to-direct bandgap transition of the high-order polytypes. The 16H polytypic structure is chosen for further detailed investigation under 1– 8% biaxial strain, unveiling a strain-induced indirect-to-direct bandgap transition occurring at 5% biaxial strain. The electronic properties of strained 16H structures demonstrate the capabilities and emphasizes the potential of strained silicon polytypes for photovoltaic technologies. Heuristic ; Development (topology) ; Work (physics) ; Energy (signal processing) ; Core (optical fiber) ; Density functional theory ; Quantum ; Complex system Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications