如何让AI在化学与材料科学中产生更大影响
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
- 从实际问题出发，结合化学领域需求设计机器学习方法
- 强调跨学科协作，提升研究的实际应用价值
- 适合希望深耕交叉领域的AI研究者参考
机器学习正广泛渗透到科学各领域，化学与材料科学也不例外。尽管已取得显著影响，但其潜力尚未完全释放。本文首先概述了机器学习在化学多样性问题中的当前应用，接着讨论了机器学习研究者在该领域的问题认知与解决思路，最后提出如何通过方法设计、数据构建和合作模式优化来最大化研究成果的影响力。
原文摘要 · Abstract (English)
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.
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