Alán Aspuru-Guzik
Orcid: 0000-0002-8277-4434
According to our database1,
Alán Aspuru-Guzik
authored at least 105 papers
between 2005 and 2024.
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Bibliography
2024
Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.
J. Cheminformatics, December, 2024
Nat., 2024
Range-separated density functional theory using multiresolution analysis and quantum computing.
J. Comput. Chem., 2024
How to do impactful research in artificial intelligence for chemistry and materials science.
CoRR, 2024
A theory of understanding for artificial intelligence: composability, catalysts, and learning.
CoRR, 2024
CoRR, 2024
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data.
CoRR, 2024
Chemically Motivated Simulation Problems are Efficiently Solvable by a Quantum Computer.
CoRR, 2024
ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization.
CoRR, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Proceedings of the Forty-first International Conference on Machine Learning, 2024
2023
J. Chem. Inf. Model., July, 2023
Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.
J. Chem. Inf. Model., June, 2023
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks.
Nat. Comput. Sci., 2023
Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance.
CoRR, 2023
CoRR, 2023
Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files.
CoRR, 2023
Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting.
CoRR, 2023
CoRR, 2023
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the IEEE International Conference on Robotics and Automation, 2023
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
2022
Quantum, September, 2022
Learning interpretable representations of entanglement in quantum optics experiments using deep generative models.
Nat. Mach. Intell., 2022
Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning.
Mach. Learn. Sci. Technol., 2022
Updated Calibrated Model for the Prediction of Molecular Frontier Orbital Energies and Its Application to Boron Subphthalocyanines.
J. Chem. Inf. Model., 2022
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS.
CoRR, 2022
Waveflow: Enforcing boundary conditions in smooth normalizing flows with application to fermionic wave functions.
CoRR, 2022
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design.
CoRR, 2022
Bayesian optimization with known experimental and design constraints for chemistry applications.
CoRR, 2022
AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor.
CoRR, 2022
2021
Mach. Learn. Sci. Technol., September, 2021
Inverse design of nanoporous crystalline reticular materials with deep generative models.
Nat. Mach. Intell., 2021
Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations.
Mach. Learn. Sci. Technol., 2021
The influence of sorbitol doping on aggregation and electronic properties of PEDOT: PSS: a theoretical study.
Mach. Learn. Sci. Technol., 2021
Mach. Learn. Sci. Technol., 2021
MPGVAE: improved generation of small organic molecules using message passing neural nets.
Mach. Learn. Sci. Technol., 2021
Mach. Learn. Sci. Technol., 2021
J. Open Source Softw., 2021
CoRR, 2021
Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset.
CoRR, 2021
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design.
CoRR, 2021
Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction.
CoRR, 2021
Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation.
CoRR, 2021
Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021
2020
Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization.
Quantum, 2020
Mach. Learn. Sci. Technol., 2020
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation.
Mach. Learn. Sci. Technol., 2020
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning.
CoRR, 2020
Experimental demonstration of a quantum generative adversarial network for continuous distributions.
CoRR, 2020
Gryffin: An algorithm for Bayesian optimization for categorical variables informed by physical intuition with applications to chemistry.
CoRR, 2020
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space.
Proceedings of the 8th International Conference on Learning Representations, 2020
2019
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules.
CoRR, 2019
CoRR, 2019
SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry.
CoRR, 2019
2018
PLoS Comput. Biol., 2018
J. Chem. Inf. Model., 2018
CoRR, 2018
2017
Quantum Inf. Process., 2017
MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.
J. Chem. Inf. Model., 2017
Quantum Neuron: an elementary building block for machine learning on quantum computers.
CoRR, 2017
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models.
CoRR, 2017
Proceedings of the Quantum Technology and Optimization Problems, 2017
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space.
Proceedings of the 34th International Conference on Machine Learning, 2017
2016
Automatic chemical design using a data-driven continuous representation of molecules.
CoRR, 2016
2015
Faster than Classical Quantum Algorithm for dense Formulas of Exact Satisfiability and Occupation Problems.
CoRR, 2015
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015
2014
2013
2012
2011
Quantum Inf. Process., 2011
2010
Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units.
Comput. Sci. Eng., 2010
2005
J. Comput. Chem., 2005
J. Comput. Chem., 2005