Search Results for author: Florian Häse

Found 11 papers, 8 papers with code

On scientific understanding with artificial intelligence

no code implementations4 Apr 2022 Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru-Guzik

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.

Philosophy

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.

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.

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.

regression

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

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation

2 code implementations31 May 2019 Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.

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

Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties

no code implementations20 Jul 2017 Florian Häse, Christoph Kreisbeck, Alán Aspuru-Guzik

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.

BIG-bench Machine Learning

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