Alán Aspuru-Guzik’s research lies at the interface of computer science with chemistry and physics. He works in the integration of robotics, machine learning and high-throughput quantum chemistry for the development of materials acceleration platforms. These “self-driving laboratories” promise to accelerate the rate of scientific discovery, with applications to clean energy and optoelectronic materials. Alán also develops quantum computer algorithms for quantum machine learning and has pioneered quantum algorithms for the simulation of matter. He is jointly appointed as a Professor of Chemistry and Computer Science at the University of Toronto, as well as cross-appointed in the departments of Materials Science and Chemical Engineering and Applied Science. Previously, he was a full professor at Harvard University. Alán is also a co-founder of Zapata Computing and Kebotix, two early-stage ventures in quantum computing and self-driving laboratories respectively.
Professor, Department of Chemistry, University of Toronto
Professor, Department of Computer Science, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Director, Acceleration Consortium
Research Interests
- Machine Learning
- Robotics & Self-Driving Labs
- Quantum Computing
- Theoretical & Quantum Chemistry
Highlights
- Co-Director and Lebovic Fellow, CIFAR Accelerated Decarbonization Program
- Chemical Engineering Medal, ETH Zurich
- Canadian Society for Chemistry (CSC) John C. Polanyi Award
- The Plenary Robert J. Le Roy Lecturer. University of Waterloo
- Davidson Lecturer. University of North Texas. Denton, TX.
- Holds the Canada 150 Research Chair in Theoretical and Quantum Chemistry
- Google Focus Research Award in Quantum Computing
- CIFAR Senior Fellow, Biologically-Inspired Solar Energy Program
- MIT Technology Review 35 under 35
- Alfred P. Sloan Fellow
- Elected Fellow of the American Association for the Advancement of Science
- Fellow of the American Physical Society
Publications
Noisy intermediate-scale quantum algorithms
2022
Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package
2021
Machine-learned potentials for next-generation matter simulations
2021
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
2021
Nanoparticle synthesis assisted by machine learning
2021
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
2021
Data-science driven autonomous process optimization
2021
Neural message passing on high order paths
2021
Olympus: a benchmarking framework for noisy optimization and experiment planning
2021
Quantum computing at the frontiers of biological sciences
2021
Meta-variational quantum eigensolver: Learning energy profiles of parameterized hamiltonians for quantum simulation
2021
Organic molecules with inverted gaps between first excited singlet and triplet states and appreciable fluorescence rates
2021
Assigning confidence to molecular property prediction
2021
Machine learning directed drug formulation development
2021
Conceptual understanding through efficient automated design of quantum optical experiments
2021
Mutual information-assisted adaptive variational quantum eigensolver
2021
Scientific intuition inspired by machine learning-generated hypotheses
2021
A comprehensive discovery platform for organophosphorus ligands for catalysis
2022
Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations
2021
Quantum computer-aided design: digital quantum simulation of quantum processors
2021
Self‐Driving Platform for Metal Nanoparticle Synthesis: Combining Microfluidics and Machine Learning
2021
Quantum computer-aided design of quantum optics hardware
2021
Natural evolutionary strategies for variational quantum computation
2021
Frank-van der Merwe growth in bilayer graphene
2021
An artificial spiking quantum neuron
2021
Experimental high-dimensional greenberger-horne-zeilinger entanglement with superconducting transmon qutrits
2022
Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approximation
2022
A molecular computing approach to solving optimization problems via programmable microdroplet arrays
2021
MPGVAE: improved generation of small organic molecules using message passing neural nets
2021
Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions
2021
Analog quantum simulation of non-Condon effects in molecular spectroscopy
2021
Routescore: Punching the ticket to more efficient materials development
2022
Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables
2021
Correction to “Generative Adversarial Networks for Crystal Structure Prediction”
2022
Updated Calibrated Model for the Prediction of Molecular Frontier Orbital Energies and Its Application to Boron Subphthalocyanines
2022
A Materials Acceleration Platform for Organic Laser Discovery
2022
Toward Quantum Computing with Molecular Electronics
2022
Funsies: A minimalist, distributed and dynamic workflow engine
2021
Fast Reverse Intersystem Crossing Over 107 s-1 in Organic Emitters with Inverted Singlet-Triplet Gap via Intramolecular Through Space Charge Transfer
2021
Correction to “Extending the Lifetime of Organic Flow Batteries via Redox State Management”
2021