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
Inverse Design of Solid-State Materials via a Continuous Representation
Juhwan Noh,Jae-Hoon Kim,Helge S. Stein,Benjamin Sanchez-Lengeling,John M. Gregoire,Alán Aspuru-Guzik,Alán Aspuru-Guzik,Yousung Jung +7 more
- 06 Nov 2019
TL;DR: This work presents a framework for learning a continuous representation of materials and building a model for new discovery using latent space representation, and suggests computational efficiency of generative models that can explore chemical compositional space effectively by learning the distributions of known materials for crystal structure prediction.
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What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery
Edward O. Pyzer-Knapp,Changwon Suh,Rafael Gómez-Bombarelli,Jorge Aguilera-Iparraguirre,Alán Aspuru-Guzik +4 more
TL;DR: A philosophy for defining what constitutes a virtual high-throughput screen is discussed, and the choices that influence decisions at each stage of the computational funnel are investigated, including an in-depth discussion of the generation of molecular libraries.
Phoenics: A Bayesian Optimizer for Chemistry.
TL;DR: Phoenics is a probabilistic global optimization algorithm identifying the set of conditions of an experimental or computational procedure which satisfies desired targets, and is recommended for rapid optimization of unknown expensive-to-evaluate objective functions, such as experimentation or long-lasting computations.
323
From transistor to trapped-ion computers for quantum chemistry
Man-Hong Yung,Jorge Casanova,Antonio Mezzacapo,Jarrod R. McClean,Lucas Lamata,Alán Aspuru-Guzik,Enrique Solano +6 more
TL;DR: This work presents an efficient toolkit that exploits both the internal and motional degrees of freedom of trapped ions for solving problems in quantum chemistry, including molecular electronic structure, molecular dynamics, and vibronic coupling.
Data-Driven Strategies for Accelerated Materials Design.
Robert Pollice,Gabriel dos Passos Gomes,Matteo Aldeghi,Riley J. Hickman,Mario Krenn,Cyrille Lavigne,Michael Lindner-D’Addario,AkshatKumar Nigam,Cher Tian Ser,Zhenpeng Yao,Alán Aspuru-Guzik +10 more
TL;DR: The most recent contributions of this group in this thriving field of machine learning for material science are reviewed, focusing on small molecules as organic electronic materials and crystalline materials and the data-driven approaches they employed to speed up discovery and derive material design strategies.
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