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Preprints
Stable machine learning potentials for liquid metals via dataset engineering
Alex Tai, Jason Ogbebor, Rodrigo Freitas
Machine learning potentials for modeling alloys across compositions
Killian Sheriff, Daniel Xiao, Yifan Cao, Lewis R. Owen, Rodrigo Freitas
On the nature of chemical short-range order evolution
Guilherme C. Stumpf*, Yifan Cao*, Vinícius P. Bacurau, Daniel Miracle,
Witor Wolf, Edgar D. Zanotto, Rodrigo Freitas, Francisco G. Coury
2026
Spectral analysis of light interstitial segregation energies in Ni: The role of local Cr coordination for boron and carbon
Tyler D. Doležal, Rodrigo Freitas, and Ju Li
Scripta Materialia
2025
Spectral sampling of boron diffusion in Ni alloys: Cr and Mo effects on bulk and grain boundary transport
Tyler D. Doležal, Rodrigo Freitas, and Ju Li
Acta Materialia
Dislocation-mediated short-range order evolution during thermomechanical processing
Mahmudul Islam, Killian Sheriff, Rodrigo Freitas
Acta Materialia
Nonequilibrium chemical short-range order in metallic alloys
Mahmudul Islam*, Killian Sheriff*, Yifan Cao*, and Rodrigo Freitas
Nature Communications
See also: MIT News
Atomistic mechanisms of oxidation and chlorine corrosion in Ni-based superalloys: The role of boron and light interstitial segregation
Tyler D. Doležal, Rodrigo Freitas, and Ju Li
Acta Materialia
Capturing short-range order in high-entropy alloys with machine learning potentials
Yifan Cao, Killian Sheriff, and Rodrigo Freitas
npj Computational Materials
Segregation and ordering of light interstitials (B, C, H, and N) in Cr-Ni Alloys: Implications for grain boundary stability in superalloy design
Tyler D. Doležal, Rodrigo Freitas, and Ju Li
Acta Materialia
Atomistic simulations of short-range ordering with light interstitials in Inconel superalloys
Tyler D. Doležal, Emre Tekoglu, Jong-Soo Bae, Gi-Dong Sim, Rodrigo Freitas, and Ju Li
Computational Materials Science
Roadmap on machine learning-based interatomic potentials
Section: “Capturing chemical complexity in high-entropy materials”
Killian Sheriff*, Yifan Cao*, and Rodrigo Freitas
Modelling and Simulation in Materials Science and Engineering
2024
Simultaneous discovery of reaction coordinates and committor functions using equivariant graph neural networks
Killian Sheriff, Rodrigo Freitas, Amalie Trewartha, Steven Torrisi
NeurIPS 2024 – AI for Accelerated Materials Discovery (AI4Mat) Workshop
Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks
Killian Sheriff, Yifan Cao, and Rodrigo Freitas
npj Computational Materials
See highlight in the Nobel Prize in Physics 2024 collection.
Comprehensive analysis of ordering in CoCrNi and CrNi₂ alloys
Vinícius Bacurau, Pedro Moreira, Gustavo Bertoli, Angelo Andreolli, Eric Mazzer, Flavio Assis, Piter Gargarella, Santiago Figueroa, Michael Widom, Michael Kaufman, Andrea Fantin, Yifan Cao, Rodrigo Freitas, Daniel Miracle, Francisco Coury
Nature Communications
Quantifying chemical short-range order in metallic alloys
Killian Sheriff*, Yifan Cao*, Tess Smidt, Rodrigo Freitas
Proceedings of the National Academy of Sciences
See also: MIT News, DMSE News, ACCESS News, and Kudos
Na vs Li metal anodes for batteries: unraveling thermodynamic and electronic origins of voids and developing descriptors for artificial surface coatings
Victor Venturi, Rodrigo Freitas, Iwnetim I. Abate
Journal of Materials Chemistry A
2022
Temperature-extrapolatable kinetic model for extension of molecular dynamics of complex chemistry to microsecond timescales: application to hydrocarbon pyrolysis
Vincent Dufour-Decieux, Brandi Ransom, Rodrigo Freitas, Jose Blanchet, and Evan J. Reed
Journal of Chemical Theory and Computation
Machine-learning potentials for crystal defects
Rodrigo Freitas and Yifan Cao
MRS Communications
Dual phase patterning during a congruent grain boundary phase transition in elemental copper
Lena Langenohl*, Tobias Brink*, Rodrigo Freitas, Timofey Frolov, Gerhard Dehm, and Christian H. Liebscher
Nature Communications
Data-centric framework for crystal structure identification in atomistic simulations using machine learning
Heejung W. Chung*, Rodrigo Freitas*, Gowoon Cheon, and Evan J. Reed
Physical Review Materials
Editors’ Suggestion
2021
Spectrum of exfoliable 1D van der Waals molecular wires and their electronic properties
Yanbing Zhu, Daniel A. Rehn, Evan A. Antoniuk, Gowoon Cheon, Rodrigo Freitas, Aditi Krishnapriyan, and Evan J. Reed
ACS Nano
Atomic-level features for kinetic Monte Carlo models of complex chemistry from molecular dynamics simulations
Vincent Dufour-Decieux, Rodrigo Freitas, and Evan Reed
The Journal of Physical Chemistry A
2020
Atomistic insights into metal hardening
Luis A. Zepeda-Ruiz, Alexander Stukowski, Tomas Oppelstrup, Nicolas Bertin, Nathan Barton, Rodrigo Freitas, and Vasily V. Bulatov
Nature Materials
Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning
Rodrigo Freitas and Evan J. Reed
Nature Communications
2019
Transferable kinetic Monte Carlo models with thousands of reactions learned from molecular dynamics simulations
Enze Chen, Qian Yang, Vincent D. Decieux, Carlos A. Sing-Long, Rodrigo Freitas, and Evan J. Reed
The Journal of Physical Chemistry A
2018
Quantum effects on dislocation motion from ring-polymer molecular dynamics
Rodrigo Freitas, Mark Asta, and Vasily Bulatov
npj Computational Materials
Free energy of grain boundary phases: Atomistic calculations for Σ5(310)[001] grain boundary in Cu
Rodrigo Freitas, Robert E. Rudd, Mark Asta, and Timofey Frolov
Physical Review Materials
Anomalous diffusion of water molecules at grain boundaries in ice Ih
Pedro A. Moreira, Roberto G. Veiga, Ingrid A. Ribeiro, Rodrigo Freitas, Julian Helfferich, and Maurice de
Physical Chemistry Chemical Physics
2017
Capillary fluctuations of surface steps: An atomistic simulation study for the model Cu (111) system
Rodrigo Freitas, Timofey Frolov, and Mark Asta
Physical Review E
Free energy of steps at faceted (111) solid-liquid interfaces in the Si-Al system calculated using capillary fluctuation method
Peyman Saidi, Rodrigo Freitas, Timofey Frolov, Mark Asta, and Jeff Hoyt
Computational Materials Science
Step free energies at faceted solid surfaces: Theory and atomistic calculations for steps on the Cu (111) surface
Rodrigo Freitas, Timofey Frolov, and Mark Asta
Physical Review B
2016
The Uhlenbeck-Ford model: Exact virial coefficients and application as a reference system in fluid-phase free-energy calculations
Rodolfo Leite, Rodrigo Freitas, Rodolfo Azevedo, and Maurice de Koning
The Journal of Chemical Physics
Nonequilibrium free-energy calculation of solids using LAMMPS
Rodrigo Freitas, Mark Asta, and Maurice de Koning
Computational Materials Science
Editor’s Choice