am a postdoctoral researcher in the Quantum Simulations Group, part of the Materials Science Division at LLNL. My current research focus on the application of machine learning to molecular simulations, with topics related to the mechanism of chemical reactions, vibrational spectroscopy and code development for automated simulations. Recent advances in machine learning applied to physical sciences expanded the time and length scales of first principles simulations, allowing atomistic simulations with quantum mechanical accuracy but at orders of magnitude lower computational cost. I take advantage of the computational efficiency of machine learning to derive free energy surfaces of chemical reactions from first principles. I have studied reactions relevant for climate security, such as the direct air capture of CO2 by amines, and energy security (proton transfer on metal oxide surfaces). The application of machine learning to computer simulations also provides an efficient way to derive vibrational spectra (IR, Raman and SFG) from first principles, bringing atomistic insights into macroscopic experimental observables. The calculation of the IR spectra, for instance, aided interpretation on the structure of water confined in carbon nanotubes and water adsorbed in TiO2 surfaces.

Please find a complete list of my publications on my Google Scholar page.

PhD in Chemistry, Princeton University, Princeton, New Jersey

MS in Chemistry, Princeton University, Princeton, New Jersey

BS in Chemistry, University of Sao Paulo, Sao Paulo, Brazil

• Calegari Andrade, M. F., Pham, T. A. Probing Confinement Effects on the Infrared Spectra of Water with Deep Potential Molecular Dynamics Simulations. J. Phys. Chem. Lett. 14, 5560-5566 (2023) (cover).
• Li, S., Calegari Andrade, M. F., Varni, A. J., Russell-Parks, G. A., Braunecker, W. A., Hunter-Sellars, E., Marple, M. A. T., & Pang, S. H. (2023). Enhanced hydrogen bonding via epoxide-functionalization restricts mobility in poly(ethylenimine) for CO2 capture. Chemical Communications (2023) (cover).
• Wen, B., Calegari Andrade, M. F., Liu, L., Selloni, A.. Water dissociation at the water–rutile TiO₂(110) interface from ab initio-based deep neural network simulations. Proc. Natl. Acad. Sci. U. S. A. 120, e2212250120 (2023).
• Calegari Andrade, M. F., Ko, H.-Y., Zhang, L., Car, R. & Selloni, A. Free Energy of Proton Transfer at the Water - TiO₂ Interface from Ab Initio Deep Potential Molecular Dynamics. Chem. Sci. 11, 2335 (2020) (cover).
• Calegari Andrade, M. F., Ko, H.-Y., Car, R. & Selloni, A. Structure, Polarization, and Sum Frequency Generation Spectrum of Interfacial Water on Anatase TiO₂. J. Phys. Chem. Lett. 9, 6716–6721 (2018).

• 2021: ACS Chemical Computing Group Excellence Award
• 2019: Margaret and Herman Sokol Fellowship in Chemistry - Excellence in Teaching Award - Princeton University
• 2015-2017: Science without Borders Fellowship
• 2015: Lavoisier Award - Academic Excellence - University of São Paulo