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 potential"”.
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Characterizing water-metal interfaces and machine learning potential energy surfaces
… (ANNs), and demonstrate how they can be used to machine learn electronic structure calculations. As a proof of principle, we show the success of an ANN potential energy surfaces for a dimer molecule with a Lennard-Jones potential.
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Designing a machine learning potential for molecular simulation of liquid alkanes
… This dissertation details the creation of a potential, a model for how the atoms and molecules in the simulation interact, based on a systematic approximation of the quantum mechanical potential energy surface using machine learning. This approximation has the advantage of producing forces …
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Global Structure Search of Nickel Clusters Using Basin Hopping and Gaussian Approximation Potential
… remains challenging due to the complex potential energy landscape associated with metallic bonding. Accurately predicting the configurations of Ni clusters is essential for understanding their fundamental properties and advancing the development of various nanoscale applications. This …
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Multilevel frameworks for studying protein energy landscapes
… landscapes described by the AMBER and UNRES potentials are also analysed. In parallel, the UNRES coarse-grained potential was integrated into the Cambridge energy landscape software, enabling efficient modelling of large biomolecular structures (Chapter 4). This method preserves accuracy …
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Machine learning force fields for elemental sulphur
… on silicon, phosphorus and carbon, surrogate machine learning models mimicking quantum methods at a fraction of the cost could achieve this feat. In this work we propose several such models, prompted by the continuous evolution of the field, and benchmark them on a series of static and dynamic …
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Optimisation of Gaussian process regressions of molecular potential energy surfaces
Machine learning methods applied to multi-dimensional surface learning pose some fundamental questions on the importance of the mathematical expression of the feature dimensions of the input space. Moreover, for Gaussian processes which are particularly popular in regression problems, the choice of …
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Nuclear Wavefunctions of Dispersion Bound Systems: Endohedral Eigenstates of Endofullerenes
… has to be solved in order to generate the Potential Energy Surface (PES). However, as these endofullerenes are bound through non-covalent interactions, they pose challenges to the electronic structure techniques with respect to achieving spectroscopic accuracy. By restricting the fullerene …