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
•Posted Content
Convolutional Networks on Graphs for Learning Molecular Fingerprints
David Duvenaud,Dougal Maclaurin,Jorge Aguilera-Iparraguirre,Rafael Gómez-Bombarelli,Timothy D. Hirzel,Alán Aspuru-Guzik,Ryan P. Adams +6 more
TL;DR: A convolutional neural network that operates directly on graphs that allows end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape is introduced.
396
From computational discovery to experimental characterization of a high hole mobility organic crystal
Anatoliy N. Sokolov,Sule Atahan-Evrenk,Rajib Mondal,Hylke B. Akkerman,Roel S. Sánchez-Carrera,Sergio Granados-Focil,Joshua Schrier,Stefan C. B. Mannsfeld,Arjan P. Zoombelt,Zhenan Bao,Alán Aspuru-Guzik +10 more
TL;DR: It is shown that in silico screening of novel derivatives of the dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene semiconductor with high hole mobility and air stability can lead to the discovery of a new high-performance semiconductor.
Reinforced Adversarial Neural Computer for de Novo Molecular Design
Evgeny Putin,Arip Asadulaev,Yan A. Ivanenkov,Yan A. Ivanenkov,Vladimir A. Aladinskiy,Benjamin Sanchez-Lengeling,Alán Aspuru-Guzik,Alán Aspuru-Guzik,Alex Zhavoronkov +8 more
TL;DR: An original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on the generative adversarial network (GAN) paradigm and reinforcement learning (RL).
360
Machine-learned potentials for next-generation matter simulations.
TL;DR: A review of machine-learned potentials can be found in this paper, where the authors summarize the basic principles of the underlying machine learning methods, the data acquisition process and active learning procedures.
348
The 2019 materials by design roadmap
Kirstin Alberi,Marco Buongiorno Nardelli,Andriy Zakutayev,Lubos Mitas,Stefano Curtarolo,Stefano Curtarolo,Anubhav Jain,Marco Fornari,Nicola Marzari,Ichiro Takeuchi,Martin L. Green,Mercouri G. Kanatzidis,Michael F. Toney,Sergiy Butenko,Bryce Meredig,Stephan Lany,Ursula R. Kattner,Albert V. Davydov,Eric S. Toberer,Vladan Stevanović,Aron Walsh,Aron Walsh,Nam-Gyu Park,Alán Aspuru-Guzik,Daniel P. Tabor,Jenny Nelson,James Edward Murphy,Anant Achyut Setlur,John M. Gregoire,Hong Li,Ruijuan Xiao,Alfred Ludwig,Lane W. Martin,Lane W. Martin,Andrew M. Rappe,Su-Huai Wei,John D. Perkins +36 more
TL;DR: In this paper, the authors present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed.