The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

The self-assembly of strongly-coupled nanocrystal superlattices, as a convenient bottom-up synthesis technique featuring a wide parameter space, is at the forefront of next-generation material design. To realize the full potential of such tunable, functional materials, a more complete understanding of the self-assembly process and the artificial crystals it produces is required. In this work, we discuss the results of a hard coherent X-ray scattering experiment at the Linac Coherent Light Source, observing superlattices long after their initial nucleation. The resulting scattering intensity correlation functions have dispersion suggestive of a disordered crystalline structure and indicate the occurrence of rapid, strain-relieving events therein. We also present real space reconstructions of individual superlattices obtained via coherent diffractive imaging. Through this analysis we thus obtain high-resolution structural and dynamical information of self-assembled superlattices in their native liquid environment.