Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …
BoTorch: A framework for efficient Monte-Carlo Bayesian optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …
applications, including automatic machine learning, engineering, physics, and experimental …
Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
Protein design with guided discrete diffusion
A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …
model for conditional sampling. The generative model samples plausible sequences while …
A multi-objective active learning platform and web app for reaction optimization
We report the development of an open-source experimental design via Bayesian
optimization platform for multi-objective reaction optimization. Using high-throughput …
optimization platform for multi-objective reaction optimization. Using high-throughput …
Unexpected improvements to expected improvement for bayesian optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian
optimization and has found countless successful applications, but its performance is often …
optimization and has found countless successful applications, but its performance is often …
A self-driving laboratory advances the Pareto front for material properties
Useful materials must satisfy multiple objectives, where the optimization of one objective is
often at the expense of another. The Pareto front reports the optimal trade-offs between …
often at the expense of another. The Pareto front reports the optimal trade-offs between …
Automated self-optimization, intensification, and scale-up of photocatalysis in flow
The optimization, intensification, and scale-up of photochemical processes constitute a
particular challenge in a manufacturing environment geared primarily toward thermal …
particular challenge in a manufacturing environment geared primarily toward thermal …
Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement
Optimizing multiple competing black-box objectives is a challenging problem in many fields,
including science, engineering, and machine learning. Multi-objective Bayesian optimization …
including science, engineering, and machine learning. Multi-objective Bayesian optimization …