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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"machine learning interatomic potentials"”.
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Designing efficient, interpretable, and generalizable machine learning interatomic potentials
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Machine Learning Interatomic Potentials to Predict Bond Dissociation Energies
… to open-shell structures. Recently, Machine Learning Interatomic Potentials (MLIPs) have emerged as versatile surrogate models capable of accurately reproducing ab initio potential energy surfaces. However, most of their applications have been targeted at near-equilibrium closed-shell …
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Investigations into liquid state physics in energy applications with machine-learning interatomic potentials: An active learning framework utilizing subascent
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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Developing machine learning interatomic potentials to probe the plasma-materials interactions of tungsten-based high-entropy alloys as plasma-facing components
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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Predicting Material Properties with Machine Learned Interatomic Potentials
Machine learning interatomic potentials (ML-IPs) have emerged as a promising approach for bridging the gap between quantum electronic structure calculations (QM) and large scale classical molecular modeling simulations and have shifted the development of these many-body force fields to become …
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A scalable machine learning approach to thermal and non-thermal order-disorder phase transitions with ab initio accuracy
… methodological frameworks based on high-fidelity machine learning interatomic potentials. These tools are utilized to investigate three distinct out-of- equilibrium regimes: • Ultrafast non-thermal melting in silicon: a novel framework based on constrained density functional perturbation theory …
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Towards routine modelling of condensed phases with the accuracy of quantum Diffusion Monte Carlo
… Next, we explore development and application of machine learning interatomic potentials, enabling efficient and accurate modelling of thermodynamic properties at finite temperatures. This includes: (i) resolving the long-debated phase behaviour of one-dimensional nano-confined water; and (ii) …
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Atomistic Insights into Alloy Solidification using Machine-Learning Potentials
… insights, but is hindered by the need for interatomic models that are both accurate and computationally efficient across relevant timescales and length scales. To overcome these challenges, this thesis develops and applies machine-learning interatomic potentials (MLPs) that capture the …
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Machine Learning Potentials for Perovskite Science - Applications and Development
… is rapidly changing, however, with the advent of machine learning interatomic potentials (MLIPs). MLIPs can learn to reproduce the behaviour of ab initio calculations but at a fraction of the computational cost. MLIPs are already transforming science by enabling large, accurate simulations of …
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Battery interfaces in the face of machine learning and artificial intelligence
… that model the interfaces explicitly, to new machine learning techniques that we developed for interface generation. We studied the phase transformation of the MgxMn2O4 cathode, which was found to self-arrest even in nanoparticles, resulting in phase heterogeneity and detrimental strain fields …