Abstract

Recent advances in computational resources and the development of high-throughput frameworks enable the efficient sampling of complicated multivariate functions such as energy and electronic property landscapes of inorganic, organic, biomolecular, and hybrid materials, and functional nanostructures. Combined with the recent focus on data science and the materials genome initiative, this leads to a rapidly growing need for numerical methods and a fundamental mathematical understanding of efficient sampling approaches, optimization techniques, hierarchical surrogate models and coarse graining techniques, and methods for uncertainty quantification. The goal of this long program is to bring together the scientific communities from pure and applied mathematics with computer science, materials science, chemistry, physics, and biomolecular science to advance the understanding of simulation, stochastic sampling and optimization methods for multidimensional energy landscapes.