The ability to control structure and composition at the nanoscale has introduced exciting scientific and technological opportunities. Advances in the creation of nanomaterials such as single-layer materials and nanocrystals have led to improved understanding of basic structure-property relationships that, in turn, have enabled impressive progress in a broad range of nanotechnologies with applications for energy storage, catalysis and electronic devices. Yet, significant knowledge gaps persist in what single-layer materials could be synthesized and in our understanding of the nature of the surfaces of nanocrystals, particularly in the complex environment of solvents and ligands. The discovery of potentially stable novel single-layer materials and the prediction of nanocrystal surface structures are arguably among the most critical aspects of nanoscale materials. This research will provide the computational tools for the detailed prediction of the structure of two-dimensional materials and nanostructure surfaces in complex environments. This will impact the development and the design of novel nanomaterials with properties optimized for applications ranging from catalyst for chemical reactions, to energy conversion materials, to low-power and high-speed electronic devices.
Progress in the field requires better computational methods for structure prediction. This project will (i) transform the Genetic Algorithm for Structure Prediction (GASP) software package developed by the PI into a sustainable scientific tool, (ii) extend its functionality to 2D materials and materials interfaces, and (iii) increase its performance by coupling to surrogate energy models that are optimized on the fly. These complementary goals will be achieved through expansion of the developer and user base, transition to portable software interfaces and data structures, and the addition of modular algorithms for functionality and performance enhancements. To enhance the functionality, the GASP algorithms will be extended to two two-dimensional materials and materials surfaces with adsorbates and ligands. To enhance the performance of the genetic algorithm, the optimization approach will be coupled to surrogate energy models such as machine-learning techniques and empirical energy models that are optimized on the fly. The publication of user tutorials, and documentation on the data structures and software interfaces will enhance the GASP codes overall utility, increase the user and developer base, and enable further extension to other data-mining and structure prediction approaches. The students involved in this project will receive extensive training and experience in algorithm development, scientific computation, and structure/property determination of complex nanomaterials. As part of the education and outreach component of the project, the PI will develop a course module on Materials Structure Predictions and widely distribute it. A weeklong workshop for students and postdocs in the third year of the project on Materials Discovery and Design will broaden the research?s impact beyond the creation of new software and the discovery of novel single-layer materials and nanocrystal surface and ligand configurations.
