Mark Hbertsen (Brookhaven National Laboratory)

Abstract

At Brookhaven National Laboratory, we develop unique instruments to interrogate the properties of materials under the conditions in which they operate, e.g., battery electrode materials during lithium insertion or heterogenous catalyst particles under reaction conditions. These techniques offer game-changing potential, both to unravel fundamental physical processes and to enable accelerated design of materials to target function. Achieving these goals requires tackling significant data challenges according to the three “V’s”: Volume, velocity and variety. After a brief introduction to operando experimentation, I will discuss examples drawn from pilot projects in which we explore the use of different data analysis approaches, including machine learning and artificial neural network models, in application to X-ray absorption spectroscopy [1-4]. X-ray Absorption Near Edge Structure (XANES) is well-adapted for in situ and operando experiments.  It is both atomically specific and it encodes local structure of the surrounding atoms.  Our research both utilizes data analytics techniques and it combines theoretical tools to compute spectra from materials with machine learning approaches to solve the inverse problem of structure inference.

Work performed in part at the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704.

     [1] J. Timoshenko, et al., J. Phys. Chem. Lett. 8, 5091 (2017).

     [2] M. R. Carbone, et al., Phys. Rev. Mater. 3, 033604 (2019).

     [3] D. Yan, et al., Nano Lett. 19, 3457, (2019).

     [4] M. R. Carbone, et al., Phys. Rev. Lett. 124, 156401 (2020).

About ALBA II Colloquium

The series ALBA II Colloquium, addressed to the scientific user's community of synchrotron radiation, is aimed at inspiring and promoting fruitful ideas and information exchange about the future development of ALBA II facility.