8 citations · 13 across the 4 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…
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…
Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems
Stefan Langner, Florian Häse, José Darío Perea +6
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…
Self-driving laboratory for accelerated discovery of thin-film materials
Benjamin P. MacLeod, Fraser G. L. Parlane, Thomas D. Morrissey +17
Discovering and optimizing commercially viable materials for clean energy applications typically takes over a decade. Self-driving laboratories that iteratively design, execute, an…
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.…