Explore the vast range of titles available.

The electrochemical nitrate reduction reaction (NO 3 RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d -states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO 3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO 3 RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V RHE and a yield rate of 6.25 mol h −1 g −1 at −0.6 V RHE . This study provides machine-learned design rules besides the d -band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations. Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia.

Building upon the d -band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d -states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials. Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d -band model to capture the essential physics of adsorbate–substrate interactions.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt 3 Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt 3 Ir, and Pt 3 Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d -band center metric of binding sites. Electrochemical conversion of ammonia to nitrogen has important energy and environmental applications but is hindered by lack of efficient electrocatalysts. Here the authors use quantum chemistry and machine learning to gain insights into the reaction mechanism and accelerate the design of highly active Ir-free trimetallic catalysts.

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d -band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties. Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d -band theory of chemisorption for reactivity prediction of transition-metal surfaces.

As a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high‐dimensional data. While the intricacy of cutting‐edge ML models, such as deep learning, makes them powerful, it also renders decision‐making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on 111‐oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory‐infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys (∼17,000 $\\sim 17,000$candidates) that were generated from ∼1500 $\\sim 1500$thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from sp $sp$ ‐band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites. Unlocking the secrets of catalysis with AI: Leveraging the power of explainable artificial intelligence (XAI), this study rapidly identifies novel Pt monolayer core‐shell alloys for enhancing oxygen reduction reaction (ORR) efficiency. By exploring thousands of alloy combinations and employing SHAP analysis, we reveal the critical role of adsorbate resonance energies in chemical bonding, offering new pathways for designing superior catalytic materials.

Heterogeneous catalysis underpins a wide variety of industrial processes including energy conversion, chemical manufacturing and environmental remediation. Significant advances in computational modelling towards understanding the nature of active sites and elementary reaction steps have occurred over the past few decades. The complexity gap between theory and experiment, however, remains overwhelming largely due to the limiting length and timescales of ab initio simulations, which severely impede the discovery of high-performance catalytic materials. This Review summarizes recent developments and applications of machine learning to narrow and, optimistically, bridge the gap created by the dynamic, mechanistic and chemostructural complexities inherent to the reactive interfaces of practical relevance. We foresee the prospects and challenges of machine learning for the automated design of sustainable catalytic technologies within a data-centric ecosystem that coevolves with computational and data sciences. Computational chemistry has the potential to aid in the design of heterogeneous catalysts; however, there is currently a large gap between the complexity of real systems and what can be readily computed at scale. This Review discusses the ways in which machine learning can assist in closing this gap to facilitate rapid advances in catalyst discovery.

Structurally ordered intermetallic nanocrystals (NCs) and single-atom catalysts (SACs) are two emerging catalytic motifs for sustainable chemical production and energy conversion. However, both have synthetic limitations which can lead to the aggregation of NCs or metal atoms. Single-atom alloys (SAAs), which contain isolated metal atoms in a host metal, can overcome the aggregation concern because of the thermodynamic stabilization of single atoms on host metal surfaces. Here we report a direct solution-phase synthesis of Cu/CuAu core/shell NCs with tunable SAA layers. This synthesis can be extended to other Cu/CuM (M = Pt, Pd) systems, in which M atoms are isolated in the copper host. Using this method, the density of SAAs on a copper surface can be controlled, resulting in both low and high densities of single atoms. Alloying gold into the copper matrix introduced ligand effects that optimized the chemisorption of *NO3 and *N. As a result, the densely packed Cu/CuAu material demonstrated a high selectivity toward NH3 from the electrocatalytic nitrate reduction reaction with an 85.5% Faradaic efficiency while maintaining a high yield rate of 8.47 mol h−1 g−1. This work advances the design of atomically precise catalytic sites by creating core/shell NCs with SAA atomic layers, opening an avenue for broad catalytic applications.Well-defined single-atom alloy (SAA) nanocrystals possess isolated atom centres and tunable electronic properties but are challenging to synthesize. Here, a direct solution-phase synthesis of Cu/CuAu core/shell nanocubes with tunable SAA layers is reported. The Cu/CuAu nanomaterial is highly active for the electrocatalytic conversion of nitrate into ammonia.

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Here, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established \\(d\\)-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance, while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.