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Bias-Free Multiobjective Active Learning for Materials Design and Discovery

Chemistry

Bias-Free Multiobjective Active Learning for Materials Design and Discovery

K. M. Jablonka, G. M. Jothiappan, et al.

Explore a groundbreaking bias-free multiobjective active learning algorithm developed by Kevin Maik Jablonka, Giriprasad Melpatti Jothiappan, Shefang Wang, Berend Smit, and Brian Yoo. This innovative approach streamlines materials discovery, especially for designing polymers, by efficiently identifying Pareto-optimal materials and minimizing evaluations through advanced simulations and machine learning.

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Playback language: English
Abstract
This work introduces a bias-free multiobjective active learning algorithm for efficient materials discovery, particularly in scenarios with multiple, competing objectives. The algorithm leverages the Pareto dominance relation to identify Pareto-optimal materials with high accuracy, significantly reducing the number of materials requiring evaluation. The approach is applied to *de novo* polymer design for dispersant applications, demonstrating its effectiveness in navigating a vast search space using molecular simulations and machine learning.
Publisher
Nature Communications
Published On
Apr 19, 2021
Authors
Kevin Maik Jablonka, Giriprasad Melpatti Jothiappan, Shefang Wang, Berend Smit, Brian Yoo
Tags
active learning
materials discovery
multiobjective optimization
Pareto dominance
polymer design
machine learning
molecular simulations

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