The Role of Knowledge Representation & Reasoning in Deciphering Chemical Complexity

Abstract

Modern chemistry is pushing the limits of traditional Artificial Intelligence (AI) models, placing unprecedented demands on data availability to address humanity's most pressing challenges. One particular concern is AI's dependence on large, curated data and its tendency to deviate from or misrepresent fundamental chemistry principles. Nonetheless, this concern is often overshadowed by the urgent demand for emergent solutions to real-world problems. This perspective describes the incorporation of a domain-specific knowledge representation & reasoning (KR&R) framework with machine learning (ML) for predictive chemistry. KR&R is presented as a framework to represent chemical knowledge, making a formal connection between inductive hypothesis generation and deductive reasoning. By integrating scientific rules into data-driven processes, upholding a “chemist in the loop” approach, KR&R ensures that ML models are understandable and consistent with existing chemical theory. These concepts are illustrated by case studies where KR&R improves the interpretability of ML predictive models targeting thermodynamic properties (ΔG_sol, Δ_vapH_m°), reaction yields, and catalytic performance. These examples also show KR&R's importance in managing the complexity of modern computational chemistry, establishing it as a key component of explainable AI in the field.