Search Results for author:
Found 11 papers, 8 papers with code
On scientific understanding with artificial intelligence
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.
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.
Golem: An algorithm for robust experiment and process optimization
Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently.
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.
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.
Automated discovery of superconducting circuits and its application to 4-local coupler design
Superconducting circuits have emerged as a promising platform to build quantum processors.
Quantum Physics
Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems
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 multi-component active layer blends.
Applied Physics
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.
PHOENICS: A universal deep Bayesian optimizer
In this work we introduce PHOENICS, a probabilistic global optimization algorithm combining ideas from Bayesian optimization with concepts from Bayesian kernel density estimation.
Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties
Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.
