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Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery
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DOI：10.1021/acscatal.2c02291.png)
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摘要
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The development of machine-learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While they are effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst data sets has offered the opportunity to build a universal machine-learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO2 reduction, NH3 production, etc.) without the additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 Data set (OC20) has begun just that, pushing the heterogeneous catalysis and machine-learning communities toward building more accurate and robust models. In this Perspective, we discuss some of the challenges and findings of recent developments on OC20. We examine the performance of current models across different materials and adsorbates to identify notably under-performing subsets. We then discuss some of the modeling efforts surrounding energy conservation, approaches to finding and evaluating the local minima, and augmentation of off-equilibrium data. To complement the community's ongoing developments, we end with an outlook to some of the important challenges that have yet to be thoroughly explored for large-scale catalyst discovery.
Keyword:
machine-learning potentials
graph neural networks
catalyst discovery
high-throughput screening
renewable energy
期刊
IF:
13.1
论文数:
1.6W
被引数:
15.0W
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