Information extraction, analysis and feedback for directing matter by design

Recent technical advances in the area of nanoscale imaging, spectroscopy and scattering/diffraction have provided tremendous capabilities for investigating materials structural, dynamical and functional characteristics. At the same time, advances in computational algorithms, including deep learning approaches, and computer capacities that are orders of magnitude larger and faster, have enabled extreme-scale simulations and deep data analytics of materials properties and processes starting with nothing but the identity of the atomic species and the basic principles of quantum and statistical mechanics and thermodynamics. This powerful confluence of capabilities/advances and the information bound in large volumes of high-quality data, offers new opportunities for advancing materials and chemical sciences. In this talk I will discuss how we are probing in-situ, chemical reactions and materials transformations, including hierarchical assembly, as a modality for direct feedback to an experiment in order to precisely impart directed energy (electrons, ions, photons, thermal) that manipulates a material at the nanoscale. This approach is enabled via the dual capability of high-resolution imaging and focused energy in-situ, with data rates, quality and volumes that allow for a deep learning framework to efficiently identify structures and dynamics across broad length and time scales. We have found that the approach can provide efficient mapping of solid-state reactions and transformations and overall allows a major step toward directing matter by design.

This research was conducted at the Center for Nanophase Materials Sciences, which is a US Department of Energy Office of Science User Facility.