Bias free multiobjective active learning for materials design and discovery
Creators
- 1. Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland
- 2. BASF Corporation, 540 White Plains Road, Tarrytown, New York, 10591, USA
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Description
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.
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References
Software (Script that can be used to reproduce the main results.) K. M. Jablonka, M. J. Giriprasad, S. Wang, B. Smit, and B. Yoo, dispersant_screening_PAL (2020).
Software (General-purpose implementation of the active learning algorithm.) K. M. Jablonka, M. J. Giriprasad, S. Wang, B. Smit, and B. Yoo, PyPAL (2020).
Preprint (Preprint where the data is discussed.) K. M. Jablonka, M. J. Giriprasad, S. Wang, B. Smit, and B. Yoo, Chemrxiv (2020)., doi: 10.26434/chemrxiv.13200197.v1
Journal reference K. M. Jablonka, G. M. Jothiappan, S. Wang, B. Smit, B. Yoo, Nature Communications 12, 1-10 (2021), doi: 10.1038/s41467-021-22437-0