Feb. 8, 2024, 5:42 a.m. | Nung Siong Lai Yi Shen Tew Xialin Zhong Jun Yin Jiali Li Binhang Yan Xiaonan Wang

cs.LG updates on arXiv.org arxiv.org

In the pursuit of novel catalyst development to address pressing environmental concerns and energy demand, conventional design and optimization methods often fall short due to the complexity and vastness of the catalyst parameter space. The advent of Machine Learning (ML) has ushered in a new era in the field of catalyst optimization, offering potential solutions to the shortcomings of traditional techniques. However, existing methods fail to effectively harness the wealth of information contained within the burgeoning body of scientific literature …

artificial artificial intelligence catalyst complexity concerns cs.lg demand design development energy environmental intelligence machine machine learning novel optimization physics.chem-ph space workflow

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