Physics Commons^™

The Structural Information Filtered Features Potential For Machine Learning Calculations Of Energies And Forces Of Atomic Systems., Jorge Arturo Hernandez Zeledon

Graduate Theses, Dissertations, and Problem Reports

In the last ten years, machine learning potentials have been successfully applied to the study of crystals, and molecules. However, more complex materials like clusters, macro-molecules, and glasses are out reach of current methods. The input of any machine learning system is a tensor of features (the most universal type are rank 1 tensors or vectors of features), the quality of any machine learning system is directly related to how well the feature space describes the original physical system. So far, the feature engineering process for machine learning potentials can not describe complex material. The current methods are highly inefficient …

Depth Dependent Atomic Valence Determination In La0.7sr0.3mno3 Thin Films Using Synchrotron Techniques, Robbyn B. Trappen

Graduate Theses, Dissertations, and Problem Reports

The valence of atoms often has a strong effect on the properties of materials, such as magnetism, conductivity, and superconductivity. The atomic valence is often perturbed at the surface and/or interface and this deviation may play a strong role in many physical phenomena such as interfacial coupling and dead layers, both magnetic and electric. In this dissertation, I present a non-destructive approach of combining two X-ray absorption detection modes, electron yield and fluorescence, with very different probing depths in conjunction with theory to map out the layer-by-layer valence of a thin film.

The weighted average Mn atomic valence as measured …

Application Of Global Search Methods To Materials Prediction And Design, Adam J. Payne

Graduate Theses, Dissertations, and Problem Reports

Due to increased availability and power of computational resources over the past few decades, prediction and design of novel materials using computational methods has become feasible. Simulation of material systems has become vital to the further realization of novel material systems. In order to ascertain physical properties, accurate determination and identification of stable crystalline structures is necessary. Additionally, further identification of novel properties, such as magnetic moments or orbital occupation, is necessary to further realize this goal. Global search methods provide a path to accurate prediction of these properties. In this dissertation, the Firefly algorithm and minima hopping methods are …

Novel Computational Methods For Catalytic Applications, Gihan Uthpala Panapitiya

Graduate Theses, Dissertations, and Problem Reports

Thiolate protected nanoclusters gold nanoparticles are gaining interest of many researchers due to their promising applications in a variety of fields the development of synthesizing techniques capable of producing atomically precise nanoclusters with high purity. Au₂₅(SR)₁₈ is one of the widely studied nanoclsuters due its remarkable stability. In this first part of this study, we explore the structural, electronic and catalytic properties of bimetallic Au_25−xAg_x(SR)₁₈ (for x = 6, 7, 8). Due to the combinatorial enormity of the total number of possible alloyed isomers, we choose a randomly selected subset corresponding to …