8 citations · 13 across the 3 of their papers we have counts for
5 papers
Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation
Riley J. Hickman, Florian Häse, Loïc M. Roch +1
Bayesian optimization has emerged as a powerful strategy to accelerate scientific discovery by means of autonomous experimentation. However, expensive measurements are required to…
Olympus: a benchmarking framework for noisy optimization and experiment planning
Florian Häse, Matteo Aldeghi, Riley J. Hickman +5
Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in…
Automated discovery of superconducting circuits and its application to 4-local coupler design
Tim Menke, Florian Häse, Simon Gustavsson +3
Superconducting circuits have emerged as a promising platform to build quantum processors. The challenge of designing a circuit is to compromise between realizing a set of performa…
From absorption spectra to charge transfer in PEDOT nanoaggregates with machine learning
Loïc M. Roch, Semion K. Saikin, Florian Häse +4
Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes…
PHOENICS: A universal deep Bayesian optimizer
Florian Häse, Loïc M. Roch, Christoph Kreisbeck +1
In this work we introduce PHOENICS, a probabilistic global optimization algorithm combining ideas from Bayesian optimization with concepts from Bayesian kernel density estimation.…