Pareto optimal

Computer security

Operating system

Set (abstract data type)

Data mining

Computer science

Mathematical optimization

Relation (database)

Pareto principle

Mathematics

Leverage (statistics)

Artificial intelligence

Programming language

Active learning (machine learning)

Space (punctuation)

Machine learning

Multi-objective optimization

Key (lock)

Nature Communications

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

Bias free multiobjective active learning for materials design and discovery