ChBE Seminar Series - 3:30 p.m. EDT Wednesday February 22 - Tyler Martin

Wed Feb 22 3:30 pm to 4:30 pm
College of Computing 016

Tyler Martin, Materials Science and Engineering Division, NIST

"Enhancing Neutron and X-ray Scattering of Soft Materials Using Theory, Simulation, and Machine Learning"

Coffee and snacks will precede the seminar at 3 p.m. in the Ford ES&T atrium (first floor)



Societal need and regulations are driving the reformulation of products so that they reduce the pace of climate change and cause less harm to humanity and the environment. Complicating this task is the fact that consumer and industrial formulations often consist of dozens to hundreds of components with wildly varying and sometimes conflicting purposes and design requirements. With this large number of carefully balanced components, small perturbations to a formulation, for example replacing a petroleum-derived fragrance with a bio-derived one, can cause large changes in macroscopic properties and functionality. Combining this property instability with a combinatorically large phase space creates a clear need for new methods which increase the knowledge gained from each characterization while reducing the time and number of measurements needed to achieve a goal. In this talk, I will outline NIST-led efforts to develop new theoretical, simulation, and machine-learning based approaches for improving neutron and x-ray scattering measurements, workhorse tools for formulation development. I will highlight our development of polymer liquid state theory models and the Autonomous Formulation Lab (AFL), our open-source, machine learning driven platform for formulation optimization and discovery.


Dr. Tyler Martin is a staff member in the Materials Science and Engineering Division at NIST and a neutron beamline scientist for the nSoft consortium at the NIST Center for Neutron Research. Working closely with nSoft stakeholders, he leverages machine learning, molecular simulation, and liquid state theories to enhance neutron and x-ray scattering measurements of soft materials. Tyler co-leads the Autonomous Formulation Lab program, which combines machine learning with automated measurement with the goal of accelerating formulation discovery and optimization. Tyler’s Ph.D. at the University of Colorado focused on using simulation and theory to develop design rules for tailoring polymer nanocomposite morphology.


College of Computing 016