Search Results for author: Riley J. Hickman

Found 7 papers, 6 papers with code

Bayesian optimization with known experimental and design constraints for chemistry applications

1 code implementation29 Mar 2022 Riley J. Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru-Guzik

The tools developed constitute a simple, yet versatile strategy to enable model-based optimization with known experimental constraints, contributing to its applicability as a core component of autonomous platforms for scientific discovery.

Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation

1 code implementation5 Mar 2021 Riley J. Hickman, Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik

We recommend using Gemini for regression tasks with sparse data and in an autonomous workflow setting where its predictions of expensive to evaluate objectives can be used to construct a more informative acquisition function, thus reducing the number of expensive evaluations an optimizer needs to achieve desired target values.


Golem: An algorithm for robust experiment and process optimization

1 code implementation5 Mar 2021 Matteo Aldeghi, Florian Häse, Riley J. Hickman, Isaac Tamblyn, Alán Aspuru-Guzik

Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently.

Assigning Confidence to Molecular Property Prediction

1 code implementation23 Feb 2021 AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley, Riley J. Hickman, Matteo Aldeghi, Naruki Yoshikawa, Seyone Chithrananda, Vincent A. Voelz, Alán Aspuru-Guzik

Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.

Molecular Docking Molecular Property Prediction

Olympus: a benchmarking framework for noisy optimization and experiment planning

1 code implementation8 Oct 2020 Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, Alán Aspuru-Guzik

Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials.

Probabilistic Deep Learning

Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge

no code implementations26 Mar 2020 Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Alán Aspuru-Guzik

Leveraging domain knowledge in the form of physicochemical descriptors, Gryffin can significantly accelerate the search for promising molecules and materials.

Density Estimation

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