169 citations · 298 across the 7 of their papers we have counts for
8 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…
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Florian Häse, Matteo Aldeghi, Riley J. Hickman +2
Designing functional molecules and advanced materials requires complex design choices: tuning continuous process parameters such as temperatures or flow rates, while simultaneously…
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…