Dr. Patrick Rinke, Aalto University, Finland (CMU MSE Seminar)
Data generation in materials science is often limited by the time it takes to perform experiments or simulations. To facilitate the exploration and characterization of complex materials, we have developed the Bayesian Optimization Structure Search (BOSS) code. BOSS uses an active learning technique that strategically samples the parameter space of material-science tasks be it experimental or computational. BOSS proposes new data acquisition points for maximum knowledge gain, balancing exploitation with exploration. I will demonstrate BOSS' smart and efficient data strategy for two examples: 1) sustainable biomaterials and 2) hybrid organic-inorganic electronic materials. For 1), we extract lignin from wood samples with hydrothermal treatment. Lignin is further processed by chemical modification into sustainable composite materials (e.g. carbon fibers, thermoplastics and three-dimensional printed objects) Lignin extraction and processing is coupled to BOSS to visualize process-structure-property correlations and to efficiently optimize extraction and modification conditions. For 2), we couple BOSS to density-functional theory (DFT) calculations to study the adsorption of a camphor molecule on a copper surface. We identify 8 unique stable adsorbates. By matching the stable structures to atomic force microscopy (AFM) images, we conclude that the experiments feature 3 different structures of chemisorbed camphor molecules. This is the first time that the atomic structure of bulky 3D adsorbates has been decoded.