CatDiT is presented, a unified framework for inverse catalyst design that generates valid and novel structures ranging from intermetallic alloys to oxide surfaces and establishes CatDiT as a practical and scalable approach for property-directed catalyst inverse design and targeted catalyst generation.
Abstract
The vast chemical design space and complex, interdependent design variables make catalyst discovery for targeted properties highly labor- and resource-intensive. Although generative models have emerged as a promising solution, existing approaches are generally limited to single-property conditioning or narrow chemical spaces. Here, we present Catalyst Diffusion Transformer (CatDiT), a unified framework for inverse catalyst design that generates valid and novel structures ranging from intermetallic alloys to oxide surfaces. By learning compressed latent representations, CatDiT enables efficient training and rapid sampling while supporting simultaneous conditioning on adsorbate type, binding energy, and catalyst class. The model provides reliable control of discrete properties and directional control of continuous properties, enriching candidate pools for reaction-specific catalyst discovery. As a representative application, multi-conditional generation for the nitrogen reduction reaction (NRR) yields 28 density functional theory (DFT)-relaxed alloy candidates that satisfy the target activity window and lie above the pure-metal *N-*H scaling line, corresponding to a ~1.5-fold enrichment over the source distribution. These results establish CatDiT as a practical and scalable approach for property-directed catalyst inverse design and targeted catalyst generation.
In complex heterogeneous systems, data-driven catalyst discovery is severely hindered by the scarcity of kinetic data and the breakdown of traditional linear scaling relationships caused by the diverse local coordination environments. Herein, we formulate 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. Taking methane C-H activation as a representative case, this framework extracts key thermodynamic descriptors corresponding to the initial, transition, and final states as source domains, enabling a deep fusion of multidimensional thermodynamic knowledge while preserving local structural information. Using this framework, we achieved universal predictions of barriers across various facets and compositions in complex alloys. Subsequent data-driven analysis recovers the classical Sabatier principle beyond the limits of linear scaling, revealing a multidimensional volcano-shaped trend that delineates the optimal catalytic window. Furthermore, we propose a temperature-barrier composite kinetic descriptor that quantitatively bridges microscopic theoretical calculations with macroscopic experimental methane oxidation rates, establishing a new data-driven paradigm for rational catalyst design under realistic operating conditions.
Wangqiang Lin, Huiyan Zhang, Jinxin Sun et al.· Journal of the American Chem...· 0 citations
The discovery of highly active polyethylene (PE) catalysts demands a systematic understanding of structure-condition-activity relationships in a vast chemical space. In this Letter, we present a data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation. From a curated data set of 507 catalysts (bis(phenoxyimine) and bis(imino)pyridine ligands, seven metals), a gradient boosting regression (GBR) model achieves a test R2 of 0.91, outperforming convolutional and graph neural networks. SHAP analysis identifies topological (Chi2v), electronic (EState_VSA), and hydrophobic (SlogP_VSA) descriptors as governing activity and reveals a classical volcano-type temperature dependence, fundamentally governed by the Sabatier principle. A virtual library of 665 685 structures, constructed via combinatorial fragment assembly, extends the known chemical space substantially. High-throughput screening, coupled with SCscore filtering, yields 1090 synthetically accessible candidates with predicted activities exceeding 2 × 107 g mol-1 h-1. Substructure analysis uncovers metal-dependent design rules, in which early transition metals favor electron-deficient aromatics while late metals profit from moderately sized alkyls. This work establishes a practical route from experimental data to actionable catalyst designs.
Xuefeng Li, Haoke Qiu, Hanwen Pei et al.· Journal of Physical Chemistr...· 0 citations
Selective oxidation of methane to methanol under mild conditions remains a "holy grail" in chemical manufacturing, constrained not only by the inertness of the C-H bond but also by the difficulty of controlling highly reactive intermediates once methane is activated. Plasma activation provides a nonequilibrium reaction environment in which methyl radicals are generated independently of the catalytic surface, effectively bypassing surface-mediated C-H activation. However, this shifts the bottleneck to subsequent interfacial chemistry, where radical interception, product selectivity, and catalyst stability impose competing constraints. Here, we introduce a plasma-electrochemical reaction framework coupled with a generative catalyst discovery strategy to navigate this multiobjective landscape. By integrating graph-neural-network-accelerated evaluation with generative exploration across a multimetallic alloy space spanning ∼1029 possible configurations, the framework identifies transferable catalytic motifs rather than individual compositions. This process reveals an emergent catalytic architecture consisting of an Ag-rich matrix with isolated Pd/Pt sites, which provide localized reactivity for radical interception and C-O bond formation while maintaining weak oxygen affinity that suppresses overoxidation. Guided by this motif-level principle, we identify experimentally realizable Ag3Pd and Ag3Pt catalysts. In a hybrid plasma-electrocatalytic system, Ag3Pd achieves a methanol faradaic efficiency of up to 80.1% at ∼1.7 mA cm-2 with a productivity of 64.5 μmol·cm-2·h-1, outperforming previously reported plasma-assisted and electrochemical methane conversion systems. More broadly, this work demonstrates how generative algorithms can uncover emergent catalytic architectures across vast chemical spaces.
Chengyi Zhang, Qiang Song, Wanping Xu et al.· Journal of the American Chem...· 0 citations
Catalysis is fundamental to chemical manufacturing, energy conversion, and environmental remediation, yet the rational design of high-performance catalysts remains constrained by the complexity of catalysts, reaction environments, and multi-step reaction networks. However, while machine learning and deep learning have accelerated catalyst discovery and performance prediction, they often obscure the underlying physicochemical mechanisms, limiting mechanistic insights and practical reliability. Here, we show that explainable artificial intelligence (XAI) provides a transformative approach by connecting model predictions to interpre1 chemical descriptors, structural motifs, and reaction features. This review summarizes recent advances in XAI for catalysis, covering key methodologies, evaluation criteria, and representative applications across heterogeneous, homogeneous, and enzymatic catalysis. Particular attention is given to the identification of electronic, structural, and intrinsic atomic descriptors, as well as to the interpretation of active sites, dynamic speciation, reaction pathways, thermodynamics, and kinetics. By transforming black-box predictions into interpretable chemical insights, XAI 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
Electrocatalysis plays a pivotal role in sustainable energy conversion technologies; however, the rational design of high-performance electrocatalysts remains challenging because of complex reaction mechanisms, multiscale kinetics, and the vast chemical space of candidate materials. This review highlights the synergistic integration of density functional theory (DFT), machine learning (ML), and microkinetic modeling (MKM) as a unified framework for accelerating electrocatalyst discovery. We first discuss the role of DFT in elucidating electronic structures, adsorption energetics, reaction mechanisms, and descriptor development. We then examine recent advances in ML for high-throughput catalyst screening, descriptor engineering, feature selection, property prediction, uncertainty quantification, and autonomous discovery workflows. The role of MKM in bridging atomistic energetics with experimentally relevant quantities, including reaction rates, turnover frequencies, selectivity, and surface coverages, is subsequently discussed. Representative applications of integrated DFT-ML-MKM frameworks for the rational design of single-atom, dual-atom, and multifunctional electrocatalysts for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR) are highlighted. Finally, current challenges-including data quality, descriptor selection, model transferability, interpretability, realistic electrochemical modeling, and multiscale integration-are critically assessed. Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.
Swetarekha Ram, Shalini Tomar, S. Bhattacharjee· Chemical Communications· 0 citations
Ru‐based solid‐solution alloys have emerged as attractive substitutes for Pt‐based electrocatalysts in the alkaline hydrogen evolution reaction (HER). However, the vast compositional space of these alloys hinders rapid catalyst discovery. Existing machine‐learning design strategies are largely based on idealized simulation data, limiting their experimental relevance and screening efficiency. Herein, we develop an interpretable machine‐learning framework informed by experiment to facilitate the rapid screening of Ru‐based HER electrocatalysts. Built on an experimental dataset, the framework integrates electronic descriptors, interpretability analysis, a Bayesian‐optimized surrogate model and uncertainty‐aware ranking to efficiently identify promising compositions. Interpretability analysis identifies V, Co, and Ru as the key elements governing catalytic performance, whereas the surrogate model maps HER activity across the ternary alloy space to define a confined high‐performance composition window. Guided by this framework, the optimized V
17
Co
31
Ru
52
catalyst exhibits an overpotential of 61.7 mV at a current density of 100 mA·cm
−2
in 1 M KOH, while maintaining stable operation for 260 h under the same current density. This work provides a rapid, interpretable and experimentally relevant strategy for discovering high‐performance alkaline HER electrocatalysts.
Jing Yang, Jun Yu, Jianming Cai et al.· Materials Genome Engineering...· 0 citations