Search Results for author: Loïc M. Roch

Found 5 papers, 4 papers with code

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.


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

Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems

1 code implementation8 Sep 2019 Stefan Langner, Florian Häse, José Darío Perea, Tobias Stubhan, Jens Hauch, Loïc M. Roch, Thomas Heumueller, Alán Aspuru-Guzik, Christoph J. Brabec

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends which represents a clear trend towards multi-component active layer blends.

Applied Physics

PHOENICS: A universal deep Bayesian optimizer

1 code implementation4 Jan 2018 Florian Häse, Loïc M. Roch, Christoph Kreisbeck, Alán Aspuru-Guzik

In this work we introduce PHOENICS, a probabilistic global optimization algorithm combining ideas from Bayesian optimization with concepts from Bayesian kernel density estimation.

Density Estimation Gaussian Processes

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