Applications/Gallery

If you use FLARE in your research, please let us know. We will list the applications of FLARE here.

• Jonathan Vandermause, Steven B. Torrisi, Simon Batzner, Yu Xie, Lixin Sun, Alexie M. Kolpak, and Boris Kozinsky. On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events. npj Computational Materials 6.1 (2020): 1-11. (arXiv) (published version)

Phase transition of AgI

• Jin Soo Lim, Jonathan Vandermause, Matthijs A. Van Spronsen, Albert Musaelian, Yu Xie, Lixin Sun, Christopher R. O’Connor, Tobias Egle, Nicola Molinari, Jacob Florian, Kaining Duanmu, Robert J. Madix, Philippe Sautet, Cynthia M. Friend, and Boris Kozinsky. Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics. Journal of the American Chemical Society (2020). (ChemRxiv) (published version)

Evolution of Pd/Ag bimetallic surface

• Yu Xie, Jonathan Vandermause, Lixin Sun, Andrea Cepellotti, and Boris Kozinsky. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene. npj Computational Materials 7, no. 1 (2021): 1-10. (arXiv) (published version)

Phase transition of stanene

• Jonathan Vandermause, Yu Xie, Jin Soo Lim, Cameron J. Owen, and Boris Kozinsky. Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics. arXiv preprint arXiv:2106.01949 (2021). (arXiv)

Chemical reaction of H2 and Pt surface

• Claudio Zeni, Kevin Rossi, Theodore Pavloudis, Joseph Kioseoglou, Stefano de Gironcoli, Richard Palmer, and Francesca Baletto. Data-driven simulation and characterization of gold nanoparticles melting. Nat Commun 12, 6056 (2021). (arXiv) (published version)

Melting of Au nanoparticle

• Kai Xu, Lei Yan, and Bingran You. Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111). ChemRxiv preprint. (ChemRxiv)

• Anders Johansson, Yu Xie, Cameron J. Owen, Jin Soo Lim, Lixin Sun, Jonathan Vandermause, Boris Kozinsky. Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning (arXiv)

Strong scaling benchmarks

Chemical reaction

• Harry H. Halim and Yoshitada Morikawa. Elucidation of Cu–Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics. ACS Phys. Chem Au (2022). (published version)

Cu-Zn surface alloying

• Yu Xie, Jonathan Vandermause, Senja Ramakers, Nakib H. Protik, Anders Johansson, Boris Kozinsky. Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC. arXiv:2203.03824. (arXiv)

SiC phase transition

• Zhou, Chen, Hio Tong Ngan, Jin Soo Lim, Zubin Darbari, Adrian Lewandowski, Dario J. Stacchiola, Boris Kozinsky, Philippe Sautet, and Jorge Anibal Boscoboinik. Dynamical Study of Adsorbate-Induced Restructuring Kinetics in Bimetallic Catalysts Using the PdAu (111) Model System. Journal of the American Chemical Society 144, no. 33 (2022): 15132-15142.

• Hong, Sung Jun, Hoje Chun, Jehyun Lee, Byung-Hyun Kim, Min Ho Seo, Joonhee Kang, and Byungchan Han. First-principles-based machine-learning molecular dynamics for crystalline polymers with van der Waals interactions. The Journal of Physical Chemistry Letters 12, no. 25 (2021): 6000-6006.

• Duschatko, Blake R., Jonathan Vandermause, Nicola Molinari, and Boris Kozinsky. Uncertainty Driven Active Learning of Coarse Grained Free Energy Models. arXiv preprint arXiv:2210.16364 (2022).

• Cameron J Owen, Steven B Torrisi, Yu Xie, Simon Batzner, Jennifer Coulter, Albert Musaelian, Lixin Sun, Boris Kozinsky. Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set. arXiv preprint arXiv:2302.12993 (2023).

• Mike Pols, Victor Brouwers, Sofía Calero, Shuxia Tao. How fast do defects migrate in halide perovskites: insights from on-the-fly machine-learned force fields. Chemical Communications 59, no. 31 (2023): 4660-4663.