Rodrigo Freitas

  • AMAX Assistant Professor of Materials Science and Engineering
  • B.Sc., University of Campinas, Brazil
  • Ph.D., University of California Berkeley

Computational Materials Science; Phase Transformations; Thermodynamics

Rodrigo Freitas


Rodrigo Freitas received B.Sc. and M.Sc. degrees in Physics from the University of Campinas in Brazil, and M.Sc. and Ph.D. degrees in Materials Science and Engineering from the University of California Berkeley. During his Ph.D. he was also a Livermore Graduate Scholar in the Materials Science Division of the Lawrence Livermore National Laboratory. As a graduate student, Dr. Freitas investigated the thermodynamics, kinetics, and mechanics of extended defects in metals (such as grain boundaries and dislocations) using atomistic simulation methods, i.e., methods in which the behavior of each atom is explicitly considered. When a postdoctoral researcher at Stanford University, he worked to leverage Machine Learning tools to perform physics-based modeling of materials kinetics.

In January 2021, Dr. Freitas joined DMSE as Assistant Professor, with a research focus on elucidating the fundamental mechanisms of microstructural evolution for systems of relevance in materials science broadly construed. His research group employs a range of computational techniques with the goal of bridging the gap between the all-atom information gathered from simulations and the mesoscale description of microstructural elements employed in materials science.



V. Dufour-Décieux, Freitas, R., and Reed, E. J., “Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations”, The Journal of Physical Chemistry A. American Chemical Society (ACS), 2021.
Y. Zhu et al., “Spectrum of Exfoliable 1D van der Waals Molecular Wires and Their Electronic Properties”, ACS Nano. American Chemical Society (ACS), 2021.


L. A. Zepeda-Ruiz et al., “Atomistic insights into metal hardening”, Nature Materials. Nature Publishing Group, pp. 1-6, 2020.
R. Freitas and Reed, E. J., “Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning”, Nature Communications, vol. 11. Nature Research, p. 3260, 2020.


E. Chen, Yang, Q., D'ecieux, V., Sing-Long, C. A., Freitas, R., and Reed, E. J., “Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations”, The Journal of Physical Chemistry A, vol. 123. American Chemical Society, pp. 1874-1881, 2019.


R. Freitas, “Free energy of grain boundary phases: Atomistic calculations for Σ 5 ( 310 ) [ 001 ] grain boundary in Cu”, Physical Review Materials, vol. 2. p. 093603, 2018.
R. Freitas, Asta, M., and Bulatov, V., “Quantum effects on dislocation motion from ring-polymer molecular dynamics”, npj Computational Materials, vol. 4. p. 55, 2018.
P. A. F. P. Moreira, Veiga, R. G. de A., Ribeiro, I. de A., Freitas, R., Helfferich, J., and de Koning, M., “Anomalous diffusion of water molecules at grain boundaries in ice I h”, Physical Chemistry Chemical Physics, vol. 20. pp. 13944-13951, 2018.


R. Freitas, Frolov, T., and Asta, M., “Capillary fluctuations of surface steps: An atomistic simulation study for the model Cu(111) system”, Physical Review E, vol. 96. p. 043308, 2017.
P. Saidi, Freitas, R., Frolov, T., Asta, M., and Hoyt, J. J., “Free energy of steps at faceted (1 1 1) solid-liquid interfaces in the Si-Al system calculated using capillary fluctuation method”, Computational Materials Science, vol. 134. pp. 184-189, 2017.
R. Freitas, Frolov, T., and Asta, M., “Step free energies at faceted solid surfaces: Theory and atomistic calculations for steps on the Cu(111) surface”, Physical Review B, vol. 95. p. 155444, 2017.


R. P. Leite, Freitas, R., Azevedo, R., and de Koning, M., “The Uhlenbeck-Ford model: Exact virial coefficients and application as a reference system in fluid-phase free-energy calculations”, The Journal of Chemical Physics, vol. 145. p. 194101, 2016.
R. Freitas, “Nonequilibrium free-energy calculation of solids using LAMMPS”, Computational Materials Science, vol. 112. pp. 333-341, 2016.