Bio
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.
Professional Experience
Staff Scientist, Los Alamos National Laboratory, Los Alamos, NM June 2021 - October 2024
Postdoctoral Researcher, Los Alamos National Laboratory, Los Alamos, NM Nov. 2019- June 2021
Postdoc Research Scientist, National Renewable Energy Laboratory, Golden, CO Feb. 2019- Nov. 2019
Postdoc Associate, National Institute of Standards and Technology, Boulder, CO Feb. 2017- 2019
Research Assistant, Design Institute for Physical Properties, Provo, UT Jan. 2012-Feb. 2014
Awards
3rd Place Award – 10th Industrial Fluid Properties Simulation Challenge Nov. 2018
NRC Associateship - Received 91/100 scoring from National Research Council (NRC) selection committee
Dean’s List Student – achieved a 4.0 semester GPA as undergraduate Apr. 2009 & Jun. 2010
Eagle Scout Award – erected a flag pole in front of a religious center Sept. 11th, 2002
Education
Ph.D. Chemical Engineering, Brigham Young University, Provo, UT Apr. 2017
B.S. Chemical Engineering, Brigham Young University, Provo, UT Dec. 2012
Other Publications
Allen, A. E. A.; Lubbers, N.; Matin, S.; Smith, J.; Messerly, R.; Tretiak, S.; Barros, K. Learning together: Towards
foundation models for machine learning interatomic potentials with meta-learning. npj Computational Materials. 10,
154, 2024.
Zhang, S.; Makos M. Z.; Jadrich, R. B.; Kraka, E.; Barros, K.; Nebgen, B. T.; Tretiak, S.; Isayev, O.; Lubbers, N.;
Messerly, R. A.; Smith, J. S. Exploring the frontiers of condensed-phase chemistry with a general reactive
machine learning potential. Nature Chemistry. 16, 727-734, 2024.
Stippell, E.; Alzate-Vargas, L.; Subedi, K. N.; Tutchton, R. M.; Cooper, M. W. D.; Tretiak, S.; Gibson, T.;
Messerly, R. A. Building a DFT+U Machine Learning Interatomic Potential for Uranium Dioxide. Artificial
Intelligence Chemistry. 2 (1), 2024.
Fedik, N.; Nebgen, B.; Lubbers, N.; Barros, K.; Kulichenko, M.; Li, Y. W.; Zubatyuk, R.; Messerly, R.; Isayev,
O.; Tretiak, S. Synergy of semiempirical models and machine learning in computational chemistry. The Journal of
Chemical Physics. 159 (11), 2023.
Freixas, V. M.; Malone, W.; Li, X.; Song, H.; Negrin-Yuvero, H.; Pérez-Castillo, R.; White, A.; Gibson, T. R.;
Makhov, D. V.; Shalashilin, D. V.; Zhang, Y.; Fedik, N.; Kulichenko, M.; Messerly, R.; Mohanam, L. N.;
Sharifzadeh, S.; Bastida, A.; Mukamel, S.; Fernandez-Alberti, S.; Tretiak, S. NEXMD v2.0 software package for
nonadiabatic excited state molecular dynamics simulations. Journal of Chemical Theory and Computation. 19 (16), 5356-
5368, 2023.
Kulichenko, M.; Barros, K.; Lubbers, N.; Li, Y. W.; Messerly, R.; Tretiak, S.; Smith, J. S.; Nebgen, B.
Uncertainty-driven dynamics for active learning of interatomic potentials. Nature Computational Science. 3 (3), 230-
239, 2023.
Fedik, N.; Zubatyuk, R.; Kulichenko, M.; Lubbers, N.; Smith, J. S.; Nebgen, B.; Messerly, R.; Li, Y. W.;
Boldyrev, A. I.; Barros, K.; Isayev, O.; Tretiak, S. Extending machine learning beyond interatomic potentials for
predicting molecular properties. Nature Reviews Chemistry. 6 (9), 653-672, 2022.
Messerly, R. A.; Yoon, T. J.; Jadrich, R. B.; Currier, R. P.; Maerzke, K. A. Elucidating the temperature and
density dependence of silver chloride hydration numbers in high-temperature water vapor: A first-principles
molecular simulation study. Chemical Geology. 594, 120766, 2022.
Messerly, R. A.; Gifford, B. J.; Holland, T. M. Kinetic isotope effects for dissociative recombination of tritiated
ketenyl ion (3HCCO+): A surface-hopping ab initio molecular dynamics study. Computational and Theoretical
Chemistry. 1210, 113634, 2022.
Madin, O. C.; Boothroy, S.; Messerly, R. A.; Fass, J.; Chodera, J. D.; Shirts, M. R. Bayesian-inference-drive model
parameterization and model selection for 2CLJQ fluid models. Journal of Chemical Information and Modeling. 62 (4),
874-889, 2022.
Sifain, A. E.; Lystrom, L.; Messerly, R. A.; Smith, J. S.; Nebgen, B.; Barros, K.; Tretiak, S.; Lubbers, N.; Gifford,
B. J. Predicting phosphorescence energies and inferring wavefunction localization with machine learning. Chemical
Science, 12 (30), 10207-10217, 2021.
Messerly, R. A.; Luecke, J. H.; St. John, P.; Etz, B. D.; Kim, Y.; Zigler, B.; McCormick, R.; Kim, S.
Understanding how chemical structure affects ignition-delay-time 𝜙-sensitivity. Combustion & Flame, 225, 377-387,
2021.
Messerly, R. A.; Rahimi, M.; St. John, P.; Luecke, J.; Park, J.; Huq, N. A.; Foust, T. D.; Lu, T.; Zigler, B.;
McCormick, R.; Kim, S. Towards quantitative prediction of ignition-delay-time sensitivity on the fuel-to-air
equivalence-ratio. Combustion & Flame, 214, 103-115, 2020.
Messerly, R. A.; Gokul, N; Schultz, A. J.; Kofke, D. A.; Harvey, A. H. Molecular calculation of the critical
parameters of classical helium. Journal of Chemical & Engineering Data, 65, 3, 1028-1037, 2020.
Etz, B. D.; Fioroni G. M.; Messerly, R. A.; Rahimi, M. J.; St. John, P. C.; Robichaud, D. J.; Christensen E. D.;
Beekley, B. P.; McEnally, C. S.; Pfefferle, L. D.; Xuan, Y.; Vyas, S.; Paton, R. S.; McCormick, R. L.; Kim, S.
Elucidating the chemical pathways responsible for the sooting tendency of 1 and 2-phenylethanol. Proceedings of the
Combustion Institute, 38, 1327-1334, 2020.
Kwon, H.; Etz, B. D.; Montgomery, M. J.; Messerly, R. A.; Shabnam, S.; Vyas, S.; van Duin, A. C.; McEnally, C.
S.; Pfefferle, L. D.; Kim, S.; Xuan, Y.. Reactive molecular dynamics simulations and quantum chemistry
calculations to investigate soot-relevant reaction pathways for hexylamine isomers. Journal of Physical Chemistry A,
124(21), 4290–4304, 2020.
Kim, Y.; Etz, B. D.; Fioroni, G. M.; Hays, C. K.; St. John, P.; Messerly, R. A.; Vyas, S.; Beekley, B. P.; Guo, F.;
McEnally, C. S.; Pfefferle, L. D.; McCormick, R. L.; Kim, S. Investigation of structural effects of aromatic
compounds on sooting tendency with mechanistic insight into ethylphenol isomers. Proceedings of the Combustion
Institute, 38, 1143-1151, 2020.
Messerly, R. A.; Soroush Barhaghi, M.; Potoff, J. J.; Shirts, M. R. Histogram-free reweighting with grand
canonical Monte Carlo: Post-simulation optimization of non-bonded potentials for phase equilibria. Journal of
Chemical & Engineering Data, 64, 9, 3701-3717, 2019.
Messerly, R. A.; Anderson, M. C.; Razavi, S. M.; Elliott, J. R. Mie 16-6 force field predicts viscosity with faster-
than-exponential pressure dependence for 2,2,4-trimethylhexane. Fluid Phase Equilibria, 495, 76-85, 2019.
Messerly, R. A.; Anderson, M. C.; Razavi, S. M.; Elliott, J. R. Improvements and limitations of Mie λ-6 potential
for prediction of saturated and compressed liquid viscosity. Fluid Phase Equilibria, 483, 101-115, 2019.
Bell, I. H.; Messerly, R. A.; Thol, M.; Costigliola, L; Dyre, J. Modified residual entropy scaling of the transport
properties of the Lennard-Jones fluid. Journal of Physical Chemistry B, 123(29), 6345-6363, 2019.
Razavi, S. M.; Messerly, R. A.; Elliott, J. R. Coexistence calculation using the isothermal-isochoric integration
method. Fluid Phase Equilibria, 501, 2019.
Maginn, E. J.; Messerly, R. A.; Carlson, D. J.; Roe, D. R.; Elliott, J. R. Best Practices for Computing Transport
Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics. Living Journal of Computational
Molecular Science, 1(1), 2019.
Messerly, R. A.; Shirts, M. R.; Kazakov, A. F. Uncertainty quantification confirms unreliable extrapolation
toward high pressures for united-atom Mie λ-6 force field. The Journal of Chemical Physics, 149(11), 114109, 2018.
Messerly, R. A.; Razavi, S. M.; Shirts, M. R. Configuration-sampling-based surrogate models for rapid
parameterization of non-bonded interactions. Journal of Chemical Theory and Computation, 14 (6), 3144-3162, 2018.
Messerly, R. A.; Knotts IV, T. A.; Wilding, W. V. Uncertainty quantification and propagation of errors of the
Lennard-Jones 12-6 parameters for n-alkanes. The Journal of Chemical Physics, 146, 194110(1-16), 2017.
Messerly, R. A.; Knotts IV, T. A.; Giles, N. F.; Wilding, W. V. Developing an internally consistent set of
theoretically based prediction models for the critical constants and normal boiling points of large n-alkanes. Fluid
Phase Equilibria, 449, 104-116, 2017.
Messerly, R. A. First principles prediction of the copolymerization process of 1,3-butadiene and vinyl chloride.
Journal of Theoretical & Computational Science, 3:142, 1-4, 2016.
Messerly, R. A.; Knotts IV, T. A.; Rowley, R. L.; Wilding, W. V. Improved estimates of the critical point
constants for large n-alkanes using Gibbs ensemble Monte Carlo simulations. Journal of Chemical & Engineering
Data, 61(10), 3640-3649, 2016.
Messerly, R. A.; Knotts IV, T. A.; Rowley, R. L.; Wilding, W. V. An improved approach for predicting the
critical constants of large molecules with Gibbs ensemble Monte Carlo simulation. Fluid Phase Equilibria, 425, 432-
442, 2016.
Hogge, J. W.; Messerly, R. A.; Giles, N. F.; Knotts IV, T. A.; Rowley, R. L.; Wilding, W. V. Improving
thermodynamic consistency among vapor pressure, heat of vaporization, and liquid and ideal gas isobaric heat
capacities through multi-property optimization. Fluid Phase Equilibria, 418, 37-43, 2016.
Messerly, R. A.; Rowley, R. L.; Knotts IV, T. A.; Wilding, W. V. An improved statistical analysis for predicting
the critical temperature and critical density with Gibbs ensemble Monte Carlo simulation. The Journal of Chemical
Physics, 143(10), 104101(1-8), 2015.
Bell, J. C.; Messerly, R. A.; Gee, R.; Harrison, A.; Rowley, R. L.; Wilding, W. V. Ternary liquid-liquid equilibrium
of biodiesel compounds for systems consisting of a methyl ester + glycerin + water. Journal of Chemical &
Engineering Data, 58(4), 1001-1004, 2013.