A human-AI co-thinking framework that enforces network invariance to extract testable hypotheses from complex chemical graphs and guided the prospective synthesis of a copper-iron oxide catalyst demonstrating a threefold increase in acetate selectivity over matched Cu-rich baselines.
Abstract
Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening. In complex reactions such as electrochemical carbon dioxide reduction, product selectivity is governed by dynamic interfacial, electrolyte, and potential factors as well as kinetic pathway competition. Conventional descriptor-based machine learning and computational potentials struggle to resolve these mechanistic branch points, primarily relying on static ground-state descriptors or bulk structural correlations rather than end-to-end topological pathway analysis. Here, we show that frontier language models, when strictly constrained to reason over explicit reaction networks, can discover novel catalysts by identifying the physical levers that govern pathway competition. We developed a human-AI co-thinking framework that enforces network invariance to extract testable hypotheses from complex chemical graphs. Applied to CO2 electroreduction, the framework identified ketene desorption and hydroxide capture as the acetate-forming pathway, and predicted a distinct adsorbed CO and CH2 coupling route to ketene. By isolating actionable control levers, specifically local alkalinity, controlled iron incorporation, and restricted interfacial proton-donor accessibility, the framework guided the prospective synthesis of a copper-iron oxide catalyst demonstrating a threefold increase in acetate selectivity over matched Cu-rich baselines. This mechanism-guided reasoning architecture shifts the computational paradigm from retrospective statistical prediction to forward-looking hypothesis generation, providing a broadly applicable blueprint for mechanism-guided materials discovery.
The optimization of chemical reactions, both catalytic and noncatalytic, to simultaneously achieve high selectivity, activity, and stability remains a central challenge in homogeneous catalysis and synthetic organic chemistry. Traditional discovery pipelines rely heavily on labor-intensive experimental screening, while current automated computational workflows are often limited by incomplete end-to-end automation, restricted accessibility beyond expert computational chemists, integration of quantum-mechanical surrogate methods, and reliance on single or approximate performance descriptors. A general-purpose, turnkey software platform that extends accessibility beyond command-line execution and removes the need for programming expertise can empower nonexpert scientists to design molecular catalysts and optimize chemical reactions. Here, we present RxnEnumProfiler, a highly automated, GUI-enabled software platform for mechanism-guided molecular catalyst design and reactivity optimization that integrates quantum mechanical methods, semiempirical tight-binding approaches, and reactive machine-learning interatomic potentials into a unified end-to-end workflow for simultaneous multimetric catalyst scoring and parallel exploration of competing reaction pathways. This platform enables rapid, high-throughput evaluation of arbitrary objective performance descriptors and subsequent ranking of candidate compounds via reaction network enumeration and profiling, substantially expanding the accessible chemical design space and accelerating the discovery of high-performing molecular catalysts and reactions. The generality of this approach is demonstrated across three representative homogeneous catalytic systems─organocatalyzed asymmetric hydrogenation, Pd-catalyzed C-O cross-coupling, and ansa-metallocene-catalyzed isotactic propylene polymerization─as well as a noncatalytic transformation relevant to epoxy curing chemistry.
Pavel A. Dub, Thomas F. Hughes, T. Mustard· Journal of Chemical Informat...· 0 citations
It is shown that explainable artificial intelligence (XAI) provides a transformative approach by connecting model predictions to interpreting chemical descriptors, structural motifs, and reaction features, and it is reshaping data-driven catalysis from opaque prediction toward mechanism-informed catalyst design.
Zixuan Geng, Di Wu, Xu Zhao et al.· Academia Catalysis· 0 citations
ChemFusion is presented, a hybrid neural network that fuses conventional electronic features with explicit 3D atomic coordinates and reveals that the architecture autonomously learns to identify and penalize restrictive steric hindrances, demonstrating that spatially aware networks can navigate complex reaction sterics that standard statistical models typically miss.
A mechanism-driven approach to alleviate data dependence and develop a multisource transfer learning (MS-TL) framework that leverages the knowledge embedded in abundant adsorption data sets while accurately capturing local structural dependence, enabling a deep fusion of multidimensional thermodynamic knowledge while preserving local structural information.
Wangqiang Lin, Huiyan Zhang, Jinxin Sun et al.· Journal of the American Chem...· 0 citations
This perspective focuses on three opportunities where LLMs can significantly contribute to catalysis: (1) text to properties; (2) text to structure; and (3) text to mechanistic models.
Shane S Michtavy, Sinhara M. H. D. Perera, Marc D. Porosoff· The journal of physical chem...· 0 citations
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 (Δ
G
sol
, Δ
vap
H
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.
José Ferraz-Caetano, Filipe Teixeira, M. N. D. S. Cordeiro· WIREs Computational Molecula...· 0 citations
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