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
Optical Spectra of p-Doped PEDOT Nano-Aggregates Provide Insight into the Material Disorder
TL;DR: In this paper, the analysis of the optical spectra of poly(3,4-ethylenedioxythiophene) domains reveals the nature and magnitude of the structural disorder in the material.
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Coherent exciton dynamics in supramolecular light-harvesting nanotubes revealed by ultrafast quantum process tomography
Joel Yuen-Zhou,Dylan H. Arias,Dorthe M. Eisele,Colby P. Steiner,Jacob J. Krich,Moungi G. Bawendi,Keith A. Nelson,Alán Aspuru-Guzik,Alán Aspuru-Guzik +8 more
TL;DR: This work proposes a novel strategy, quantum process tomography (QPT), for ultrafast spectroscopy and applies it to reconstruct the evolving quantum state of excitons in double-walled supramolecular light-harvesting nanotubes at room temperature from eight narrowband transient grating experiments, constituting the first experimental QPT in a "warm" and complex system.
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The decade of artificial intelligence in chemistry and materials
TL;DR: In 2018, Digital Discovery celebrated its first anniversary as discussed by the authors and introduced the Digital Discovery Challenge, a competition for the first-ever digital discovery challenge, which was won by Discovery's winner.
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From absorption spectra to charge transfer in PEDOT nanoaggregates with machine learning
Loïc M. Roch,Semion K. Saikin,Florian Häse,Pascal Friederich,Randall H. Goldsmith,Salvador León,Alán Aspuru-Guzik +6 more
TL;DR: It is shown that ML models can be trained to be transferable across a broad range of spectral resolutions, and that the electronic couplings can be predicted from the simulated spectra with an 88 % accuracy when ML models are used as classifiers.
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Characterization and quantification of the role of coherence in ultrafast quantum biological experiments using quantum master equations, atomistic simulations, and quantum process tomography
TL;DR: In this article, the role of quantum coherence, dephasing, relaxation and other elementary processes in energy transfer efficiency in photosynthetic complexes, based on the information obtained from the atomistic simulations, or, using QPT, directly from the experiment.
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