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
Computational Complexity in Electronic Structure
TL;DR: In this paper, the fundamentals of computational complexity are reviewed and motivated from the vantage point of chemistry, and recent results from the computational complexity literature regarding common model chemistries including Hartree-Fock and density functional theory are discussed.
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Assigning confidence to molecular property prediction.
AkshatKumar Nigam,Robert Pollice,Matthew F. D. Hurley,Riley J. Hickman,Matteo Aldeghi,Naruki Yoshikawa,Seyone Chithrananda,Vincent A. Voelz,Alán Aspuru-Guzik +8 more
TL;DR: Assessing uncertainty in property prediction models is essential whenever closed-loop drug design campaigns relying on high-throughput virtual screening are deployed, and considering sources of uncertainty leads to better-informed validations, more reliable predictions and more realistic expectations of the entire workflow.
63
Memory-Assisted Exciton Diffusion in the Chlorosome Light-Harvesting Antenna of Green Sulfur Bacteria.
TL;DR: In this paper, the authors explore the microscopic origin of the fast excitation energy transfer in the chlorosome using the recently resolved structure and atomistic-detail simulations and show that the exciton delocalizes over the entire aggregate in about 200 fs.
•Posted Content
Local protein solvation drives direct down-conversion in phycobiliprotein PC645 via incoherent vibronic transport
Samuel M. Blau,Doran I. G. Bennett,Doran I. G. Bennett,Christoph Kreisbeck,Gregory D. Scholes,Gregory D. Scholes,Alán Aspuru-Guzik,Alán Aspuru-Guzik +7 more
TL;DR: In this article, an incoherent vibronic transport mechanism is proposed to enable direct down-conversion from the highest energy states to the lowest energy pigments in light-harvesting complexes, and the authors quantify the solvation dynamics of individual pigments using ab initio QM/MM nuclear dynamics.
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How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry
Florian Häse,Ignacio Fernández Galván,Alán Aspuru-Guzik,Alán Aspuru-Guzik,Roland Lindh,Morgane Vacher +5 more
TL;DR: In this article, the authors used machine learning models to predict the timescale of the decomposition of 1,2-dioxetane in a molecular dynamics simulation, and demonstrated that the models accurately reproduce the dissociation time of the compound.