From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Optimizing multiple competing black-box objectives is a challenging problem in many fields,
including science, engineering, and machine learning. Multi-objective Bayesian optimization …

The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …

In recent years, the development of new algorithms for multiobjective optimization has
considerably grown. A large number of performance indicators has been introduced to …

Many real world scientific and industrial applications require optimizing multiple competing
black-box objectives. When the objectives are expensive-to-evaluate, multi-objective …

Automated development of chemical processes requires access to sophisticated algorithms
for multi-objective optimization, since single-objective optimization fails to identify the trade …

Expensive multi-objective optimization problems can be found in many real-world
applications, where their objective function evaluations involve expensive computations or …