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.
Chat about Author
Papers
Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project
Johannes Hachmann,Johannes Hachmann,Roberto Olivares-Amaya,Roberto Olivares-Amaya,Adrian Jinich,Anthony L. Appleton,Martin A. Blood-Forsythe,Laszlo Ryan Seress,Carolina Román-Salgado,Kai Trepte,Sule Atahan-Evrenk,Süleyman Er,Supriya Shrestha,Rajib Mondal,Anatoliy N. Sokolov,Zhenan Bao,Alán Aspuru-Guzik +16 more
TL;DR: The virtual high-throughput screening framework of the Harvard Clean Energy Project allows for the computational assessment of candidate structures for organic electronic materials, in particular photovoltaics, at an unprecedented scale as discussed by the authors.
Role of quantum coherence in chromophoric energy transport
TL;DR: In this article, the role of quantum coherence and the environment in the dynamics of excitation energy transfer is not fully understood, and the concept of dynamical contributions of various physical processes to the energy transfer efficiency is introduced.
273
•Posted Content
Self-driving laboratory for accelerated discovery of thin-film materials
Benjamin P. MacLeod,Fraser G. L. Parlane,Thomas D. Morrissey,Florian Häse,Loïc M. Roch,Kevan E. Dettelbach,Raphaell Moreira,Lars P. E. Yunker,Michael B. Rooney,Joseph R. Deeth,Veronica Lai,Gordon J. Ng,Henry Situ,Ray H. Zhang,Michael S. Elliott,Ted H. Haley,David J. Dvorak,Alán Aspuru-Guzik,Jason E. Hein,Curtis P. Berlinguette +19 more
TL;DR: A modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions is reported here.
255
Neural networks for the prediction organic chemistry reactions
TL;DR: In this article, the authors explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method, and combine this predictor with SMARTS transformations to build a system which, given a set of reagents and re-actants, predicts the likely products.
250
Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
TL;DR: The employment of multilayer perceptrons, a type of artificial neural network, is proposed as part of a computational funneling procedure for high‐throughput organic materials design and it is demonstrated that a great reduction in the fraction of the screening library that is actually calculated is demonstrated.
249