Global ETD Search
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 8 of 8 for “"Machine Learning Force Fields"”.
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Machine Learning Force Fields for Molecular Chemistry
… time scales. The traditional alternative is force fields that enable fast and accurate simulations by bypassing the treatment of the electrons and describing the system solely in terms of the atomic positions. The emergence of machine learning tools has opened up the opportunity for the …
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Machine learning force fields for elemental sulphur
… task, out of the reach of any current force fields (which are too inaccurate) or quantum mechanical methods (which are too slow and expensive). However, following the footsteps of similar work done on silicon, phosphorus and carbon, surrogate machine learning models mimicking quantum …
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Machine Learning Force Fields for Modelling Reactions at Complex Interfaces
… accuracy and computational cost. Recently, machine learning (ML) architectures have been trained on highly accurate quantum mechanical calculations to sidestep this constraint~\cite{behlerFirstPrinciplesNeural2017, bartokGaussianApproximationPotentials2010}. So-called machine learning force …
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Modeling larger length and time scales in machine learning force fields
… is determined by the accuracy of the interatomic forces. These forces can be obtained from fast but approximate empirical force fields, or slow but accurate quantum mechanical methods. Machine learning force fields have emerged as a promising alternative, bridging this gap by approximating …
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Semi-empirical and machine learned interatomic interaction potentials for zirconium: training, validation and application
… interaction potentials--also known as force fields--is the main factor determining the physical soundness of molecular dynamics simulations and kinetic Monte Carlo simulations. Zirconium (Zr) is widely utilized in structural components of CANDU nuclear reactors. In this thesis, …
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Learning to Model Atoms Across Scales
… exhaustive search methods. This thesis presents machine learning methods for modeling atoms for tasks across different scales. First, we propose machine learning force fields that can decompose molecular interactions into fast and slow components, and then accelerate molecular simulations through …
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Towards Machine Learning Foundation Models for Materials Chemistry
This thesis demonstrates how recent advances in machine learning (ML) for materials can accelerate our search for new stable inorganic crystals. We show how best to measure and compare the utility of different models, what range of applications a foundational ML force field can be expected to …
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Machine Learning Interatomic Potentials to Predict Bond Dissociation Energies
Empirical force fields are valuable tools in computational chemistry, however, they suffer from limitations in terms of accuracy, transferability and their lack of applicability to open-shell structures. Recently, Machine Learning Interatomic Potentials (MLIPs) have emerged as versatile surrogate …