4 citations · 5 across the 2 of their papers we have counts for
4 papers
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Gary Tom, Riley J. Hickman, Aniket Zinzuwadia +3
Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties. When the datasets are smaller (< 2000 molecules), it is not cle…
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…
Assigning Confidence to Molecular Property Prediction
AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley +6
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recentl…
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…