Richard Alma Messerly earned his Bachelor (2012) and Doctorate (2017) degrees in Chemical Engineering from Brigham Young University in Provo, Utah, USA. His PhD research focused on uncertainty quantification in molecular simulations by employing Bayesian statistical techniques to develop classical force fields for long-chain hydrocarbons. During his PhD, Richard also investigated the thermophysical properties of select chemicals as a research assistant for the Design Institute for Physical Properties (DIPPR).
After completing his Ph.D., Richard was selected through a competitive evaluation process as a Postdoctoral Associate by the National Research Council (NRC). For his two-year NRC associateship, Richard joined the Thermodynamics Research Center (TRC) at the National Institute of Standards and Technology (NIST). His research at TRC/NIST focused on developing accelerated methods to train force fields using multistate reweighting techniques. Subsequently, Richard completed a one-year postdoc at the National Renewable Energy Laboratory (NREL), where he studied the ignition sensitivity of various biofuel candidates.
In 2021, Richard became a Staff Scientist in the Theoretical Division at Los Alamos National Laboratory (LANL) after fulfilling a one-and-a-half-year postdoc position. At LANL, Richard established his expertise in machine learning by training neural network interatomic potentials for diverse systems, including high explosives, polymers, nuclear fuels, biofuels, materials, and aqueous salts.
Currently, as a Computational Scientist at Oak Ridge National Laboratory (ORNL), Richard is leveraging his skills in artificial intelligence, molecular simulations, and high-performance computing to contribute to the prestigious group at the Oak Ridge Leadership Computing Facility (OLCF) within the National Center for Computational Sciences (NCCS). With access to the most powerful supercomputer in the world, Richard is excited to continue exploring the Frontier of computational science, both figuratively and literally.
