期刊
NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-22437-0
关键词
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资金
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [666983]
- NCCR-MARVEL - Swiss National Science Foundation
- Swiss National Science Foundation [200021_172759]
- Swiss National Science Foundation (SNF) [200021_172759] Funding Source: Swiss National Science Foundation (SNF)
This study utilizes an active learning algorithm and Pareto dominance relation to compute a set of Pareto optimal materials for multi-objective material design. By conducting molecular simulations, the number of materials that need to be evaluated is drastically reduced, enhancing design efficiency.
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.
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