About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
ORIGINALLY MON PM: Accelerating the Growth of Metal-Organic Framework Thin Films Guided by Pool-based Active Learning |
Author(s) |
Roberto Javier Herrera del Valle, Luke Huelsenbeck, Sangeun Jung, Gaurav Giri, Prasanna Balachandran |
On-Site Speaker (Planned) |
Roberto Javier Herrera del Valle |
Abstract Scope |
In this work, we develop a novel pool-based active learning (PAL) approach to rapidly guide the experimental growth of metal-organic framework (MOF) thin films with full coverage. The PAL approach is based on the idea that a supervised machine learning algorithm can achieve improved performance with fewer training data, provided the learning task allows the algorithm to autonomously choose data points from the vast unexplored parameter space. We implemented a PAL strategy that recommends data points based on two key measures: Diversity and Representativeness. While uncertainty-based sampling served as a measure of representative criterion, we used the maximin space-filling design to sample diverse growth parameters. The iterative PAL approach was able to efficiently guide the thin film growth towards promising regions of the vast processing space where full film coverage can be experimentally confirmed. The outcome of this work has major implications in using data-driven methods for high-throughput synthesis. |
Proceedings Inclusion? |
Undecided |