从知识树到知识森林：利用机器学习收获化学认知

doi:10.1016/j.actphy.2025.100233

物理化学学报

从知识树到知识森林：利用机器学习收获化学认知

赵东波¹, 赵一霖², 荣春英³, 张贺^4,5, 刘畅^4,5, 刘述斌^6,7

1 云南大学生物医药研究院, 云南昆明 650500;
2 Department of Chemistry and Chemical Biology, McMaster University, Hamilton Ontario L8S 4M1, Canada.;
3 湖南师范大学化学化工学院, 湖南长沙 410081;
4 中关村人工智能研究院, 北京 100194;
5 北京中关村学院, 北京 100194;
6 Research Computing Center, University of North Carolina, Chapel Hill, NC 27599-3420, USA.;
7 Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599-3290, USA.

收稿日期:2025-10-16 修回日期:2025-12-13 录用日期:2025-12-18
通讯作者: 荣春英, 刘畅, 刘述斌 E-mail:rongchunying@aliyun.com;liuchang@bjzgca.edu.cn;shubin@email.unc.edu
基金资助:
本研究得到国家自然科学基金(2220307, 22373034)的资助。

From knowledge tree to knowledge forest: Harnessing chemical understanding with machine learning

Dongbo Zhao¹, Yilin Zhao², Chunying Rong³, He Zhang^4,5, Chang Liu^4,5, Shubin Liu^6,7

1 Institute of Biomedical Research, Yunnan University, Kunming 650500, Yunnan Province, China;
2 Department of Chemistry and Chemical Biology, McMaster University, Hamilton Ontario L8S 4M1, Canada.;
3 College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha 410081, Hunan Province, China;
4 Zhongguancun Institute of Artificial Intelligence, Beijing 100094, China;
5 Zhongguancun Academy, Beijing 100094, China;
6 Research Computing Center, University of North Carolina, Chapel Hill, NC 27599-3420, USA.;
7 Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599-3290, USA.

Received:2025-10-16 Revised:2025-12-13 Accepted:2025-12-18
Contact: Chunying Rong, Chang Liu, Shubin Liu E-mail:rongchunying@aliyun.com;liuchang@bjzgca.edu.cn;shubin@email.unc.edu

摘要/Abstract

摘要： 2024年诺贝尔物理学奖与化学奖在同一周授予了机器学习(ML)领域的突破性成果，标志着“ AI科学元年”的到来，突显了AI在自然科学中的变革性作用。然而，数据并不等同于理解，这一点对化学学科尤为关键。化学自诞生以来，始终依赖于像化学键、芳香性、和反应活性等概念作为理解与解释分子世界的基石。基于我们近期的两篇观点文章(ACS Phys. Chem. Au 2024, 4, 135-142; J.Chem. Theory Comput. 2025, 21, 10068-10079)，本文探讨了机器学习如何成为化学理解的引擎。我们提出了化学知识的五重体，即本体论、认识论、化学理论、化学概念与化学理解，并借助“知识树”与“知识森林”的隐喻，阐明多样性的认识论方法如何相互作用递归性地丰富彼此。通过对芳香性、催化、无轨道密度泛函理论、以及蛋白质折叠四个例子的剖析，我们揭示，当机器学习的特征被解释为化学概念之根时便能结出化学理解之果。通过对比多尺度模拟与层级模拟的差别，我们进一步提出，机器学习能够促进跨层级的涌现并对概念驱动进行整合。耕耘和培育一个多元且层级化的知识生态，有望引领理论化学迈向新的突破，不是通过暴力计算来解决狄拉克难题，而是通过一片片“概念之林”将海量数据转化为持久的化学认知。

关键词: 机器学习(ML), 人工智能(AI), 化学概念, 化学理解, 本体论, 认知论, 知识树, 知识森林

Abstract: The 2024 Physics and Chemistry Nobel Prizes to machine learning (ML) breakthroughs marked “Year 1 of AI for Science,” underscoring their transformative role in physical sciences. Yet data are not the same as understanding—a distinction central to chemistry, which has long relied on concepts such as bond, aromaticity, and reactivity as scaffolds for understanding and explanation. Building on our recent perspectives (ACS Phys. Chem. Au 2024, 4, 135-142; J. Chem. Theory Comput. 2025, 21, 10068-10079), this article explores how ML can become engines of chemical understanding. We introduce a quintet of chemical knowledge— ontology, epistemology, theory, concept, and understanding—and develop the metaphors of the Knowledge Tree and Knowledge Forest to show how diverse epistemologies interact and recursively enrich one another. Case studies on aromaticity, catalysis, orbital-free density functional theory, and protein folding illustrate how ML features, when interpreted as conceptual roots, yield fruits of understanding. Contrasting multiscale modeling with hierarchical modeling, we argue that ML enables emergent, concept-driven integration across levels. Cultivating this plural and hierarchical ecosystem may guide theoretical chemistry toward its next breakthroughs, resolving Dirac’s dilemma not by brute force but by forests of concepts that transform data into enduring understanding.

Key words: Machine learning (ML), Artificial intelligence (AI), Chemical concepts, Chemical understanding, Ontology, Epistemology, Knowledge tree, Knowledge forest

赵东波, 赵一霖, 荣春英, 张贺, 刘畅, 刘述斌. 从知识树到知识森林：利用机器学习收获化学认知[J]. 物理化学学报 2026, (), 100233. doi: 10.1016/j.actphy.2025.100233

Dongbo Zhao, Yilin Zhao, Chunying Rong, He Zhang, Chang Liu, Shubin Liu. From knowledge tree to knowledge forest: Harnessing chemical understanding with machine learning[J]. Acta Phys. -Chim. Sin. 2026, (), 100233. doi: 10.1016/j.actphy.2025.100233

链接本文: https://www.whxb.pku.edu.cn/CN/10.1016/j.actphy.2025.100233

https://www.whxb.pku.edu.cn/CN/Y2026/V/I/100233

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