Automating Reasoning in Chemical Science and Engineering
Tyler R. Josephson · ChemRxiv · 2025
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and engineering: inductive, deductive, abductive, probabilistic, causal, analogical, and informal reasoning. For each, we describe approaches and tools for implementing in computational settings. We find the traditional categories of logic (induction, deduction, abduction) to be useful for interpreting a wide range of research activities in the computational sciences: curve fitting and supervised learning are induction, predicting from a curve or a supervised model is deduction, first-principles simulations are deduction, molecular structure elucidation is abduction, and inverse design is isomorphic to abduction. Experimental design incorporates themes of all three, but also stands apart because it modifies the data. We also survey the ``reasoning'' capabilities of large language models, and illustrate some challenges and opportunities in building systems that learn to reason. Central concepts that emerge include the dual nature of reasoning as propositional and step-by-step (the "what" and the "how"), the importance of appreciating syntax and semantics, and the centrality of abstraction in formal and informal contexts. Throughout, we highlight untapped opportunities, including bug-free scientific computing software, predictive modeling approaches to generalize outside training data, inverse design, and automated hypothesis generation and evaluation.