Alán Aspuru-Guzik
University of Toronto
664 Papers
4.7K Citations
Alán Aspuru-Guzik is an academic researcher from University of Toronto. The author has contributed to research in topics: Quantum computer & Quantum. The author has an hindex of 97, co-authored 628 publications. Previous affiliations of Alán Aspuru-Guzik include D-Wave Systems & National Autonomous University of Mexico.
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Papers
Exciton-Phonon Information Flow in the Energy Transfer Process of Photosynthetic Complexes
TL;DR: A recently developed measure for non-Markovianity is utilized to elucidate the exciton-phonon dynamics in terms of the information flow between electronic and vibrational degrees of freedom and it is found that for a model dimer system and for the Fenna-Matthews-Olson complex the non- MarkovianITY is significant under physiological conditions.
Autonomous Molecular Design: Then and Now.
TL;DR: An overview of the developments in chemistry automation and the applications of machine learning techniques in the chemical and pharmaceutical industries with a focus on the novel capabilities that deep learning brings in is provided.
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Quantum Algorithm for Obtaining the Energy Spectrum of Molecular Systems
TL;DR: A quantum algorithm to obtain the energy spectrum of molecular systems based on the multiconfigurational self-consistent field (MCSCF) wave function is presented and it is shown that a small increase of the MCSCF space can dramatically increase the success probability of the quantum algorithm, even in regions of the potential energy surface that are far from the equilibrium geometry.
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•Posted Content
Separation of Electromagnetic and Chemical Contributions to Surface-Enhanced Raman Spectra on Nanoengineered Plasmonic Substrates
TL;DR: In this article, the authors explored the origin of the Raman spectra modification of benzenethiol adsorbed on nanostructured gold surfaces and found that the effect of chemical binding is mostly due to changes in the electronic structure of the molecule rather than to the fixed orientation of molecules relative to the substrate.
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Machine learning exciton dynamics
TL;DR: In this article, a machine learning technique, multi-layer perceptrons, was proposed to reduce the time required to compute excited state energies of bacteriochlorophylls in the FMO complex.