Dr Daniel Montemayor was born in San Antonio, Texas. He received his PhD in Chemistry from Boston University, Massachusetts, where he studied Quantum Liouville and Linearized Path Integral treatments of vibronic excitations in small molecules in the pure dephasing limit. He developed semiclassical density propagation schemes to treat electronic relaxations in the pre-dissociation process of I2. He employed Diatomics-in-Molecules method to model valance bond potential energy surfaces of dihalogen in rare gas environments. During his dissertation, he received the GRS Excellence in Teaching Award.
He then worked as a Postdoctoral Research Fellow at the Atlantic Centre for Atomistic and Molecular Modeling, University of Dublin, where he studied excitonic energy transfer in biological systems using Linearized Path Integral Density matrix propagation schemes. During his stay there, he also developed the NonAdMD quantum-classical simulation software for study of non-adiabatic transfer processes in condensed phase systems. He also investigated coherent energy transfer processes subject to inhomogeneous dissipative system-bath interactions. He further applied inhomogeneous pigment-protein interaction methodology to model excitonic energy transfer processes in light harvesting complexes.
Dr Montemayor worked as a senior postdoctoral research associate at Queens College where he conducted simulations of exciton dynamics and spectroscopy of the photosynthetic light harvessting complexes of purple bacteria. He has palyed a major role in obtaining two grants to use the supercomputing facilities of the D. E. Shaw company's specialized Anton computer, and the systems at the Center for Functional Nano-materials, Brookhaven National Laboratory. He has also worked on machine learning models for odor perception using deep belief networks. He has also been developing the Fortified software that uses machine learning and quantum mechanical toolset to study chemical systems.
PhD in Chemistry, Boston University
Dr Montemayor is currently interested in using AI Machine learning techniques to further his research into mammalian olfaction and perception. He is also interested in coherent energy transfer processes of photosynthetic light harvesting complexes. He is actively continuing to developing the Fortified and NonAdMD software package.
© 2023 The Jang Group, Chemistry Department, Queens College, CUNY.