UChicago PME Materials Special Seminar - Brett Savoie

Where is the Prediction Frontier in Materials Chemistry?

The prediction frontier is defined by the set of problems for which the governing functional relationships still evade us. For most of the past century this frontier has moved at a hard-earned but glacial pace, with big breakthroughs occurring slowly through improvements in quantum chemistry, force-field improvements, sampling techniques in statistical mechanics, and the scaleup in computational resources. But in the past few years, the prediction frontier has moved too quickly to easily define. Protein structures? We can often predict those now. Sequence to function mappings of polymers? We can often predict those, too. The distribution of molecules with a specified range of properties? Prediction methods are daily improving. In this presentation, I’ll discuss three prediction problems that are at the frontier of being predictable under useful circumstances. In all three cases, I will argue that we had no realistic pathway to prediction prior to recent developments in machine learning. The common thread for all three is that the maturation of machine learning approaches—combined with the decades of physics-based methods preceding it—puts the field within reach of “solving” each of these problems under useful constraints.