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Bias Free Multiobjective Active Learning for Materials Design and Discovery

preprint
submitted on 06.11.2020, 12:28 and posted on 09.11.2020, 09:29 by Kevin Maik Jablonka, Giriprasad Melpatti Jothiappan, Shefang Wang, Berend Smit, Brian Yoo
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 introduce 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 reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.
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.

Funding

NCCR MARVEL: Materials’ Revolution: Computational Design and Discovery of Novel Materials (phase II)

Swiss National Science Foundation

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The Materials Genome in Action

European Research Council

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Swiss National Science Foundation (200021_172759)

BASF Explore Together internship program

History

Email Address of Submitting Author

kevin.jablonka@epfl.ch

Institution

EPFL

Country

Switzerland

ORCID For Submitting Author

0000-0003-4894-4660

Declaration of Conflict of Interest

No conflict of interest

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