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
Hydrogen-bonded diketopyrrolopyrrole (DPP) pigments as organic semiconductors.
Eric Daniel Głowacki,Halime Coskun,Martin A. Blood-Forsythe,Uwe Monkowius,Lucia Leonat,Marek Grzybowski,Daniel T. Gryko,Matthew S. White,Alán Aspuru-Guzik,Niyazi Serdar Sariciftci +9 more
TL;DR: In this article, the performance of three archetypical H-bonded DPP pigments, which show ambipolar carrier mobilities in the range 0.01-0.06 cm 2 /V s in organic field-effect transis-tors, were investigated.
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On the chemical bonding effects in the Raman response: Benzenethiol adsorbed on silver clusters
TL;DR: In this paper, the effects of chemical bonding on Raman scattering from benzenethiol chemisorbed on silver clusters using time-dependent density functional theory (TDDFT) are computed using a formalism that employs analytical derivatives of frequency-dependent electronic polarizabilities.
Organic molecules with inverted gaps between first excited singlet and triplet states and appreciable fluorescence rates
Robert Pollice,Pascal Friederich,Pascal Friederich,Cyrille Lavigne,Gabriel dos Passos Gomes,Alán Aspuru-Guzik +5 more
- 05 May 2021
TL;DR: In this paper, the authors describe the first molecules with negative singlet-triplet gaps and considerable fluorescence rates and show that they are more common than hypothesized previously, based on computational studies.
Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
TL;DR: Inverse design allows the generation of molecules with desirable physical quantities using property optimization as discussed by the authors, but the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming.
Designing and understanding light-harvesting devices with machine learning
TL;DR: This work presents opportunities to gain detailed scientific insights into the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-Harvesting devices.