Search Results for author:
Found 7 papers, 6 papers with code
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties.
Bayesian optimization with known experimental and design constraints for chemistry applications
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.
Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation
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.
Golem: An algorithm for robust experiment and process optimization
Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently.
Assigning Confidence to Molecular Property Prediction
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.
Olympus: a benchmarking framework for noisy optimization and experiment planning
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.
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Leveraging domain knowledge in the form of physicochemical descriptors, Gryffin can significantly accelerate the search for promising molecules and materials.
