Extendable Machine Learning Model for the Stability of Single Atom Alloys

Single-Atom Heterogeneous Catalysts: Human- and AI-Driven Platform for Augmented Designs, Analytics and Reality-Enabled Manufacturing

Heterogeneous catalysts with targeted functionality can be designed with atomic precision, but it is challenging to retain the structure and performance upon the scaled-up manufacturing. Particularly challenging is to ensure the "atomic economy", where every catalytic site is most gainfully utilized. Given the emerging synergistic integration of human- and artificial intelligence (AI)-driven augmented designs (AD), augmented analytics (AA), and augmented reality manufacturing (AM) platforms, this minireview focuses on single-atom heterogeneous catalysts (SAHCs) and examines the current status, challenges, and future perspectives of translating atomic-level structural precision and data-driven discovery to next-generation industrial manufacturing. We critically examine the atomistic insights into structure-driven SAHCs functionality and discuss the opportunities and challenges on the way towards the synergistic human-AI collaborative data-driven platform capable of monitoring, analyzing, manufacturing, and retaining the atomic-scale structure and functions. Enhanced by the atomic-level AD, AA, and AM, evolving from the current high-throughput capabilities and digital materials manufacturing acceleration, this synergistic human-AI platform is promising to enable atom-efficient and atomically precise heterogeneous catalyst production.

Design of Single-Atom Catalysts and Tracking Their Fate Using Operando and Advanced X-ray Spectroscopic Tools

The potential of operando X-ray techniques for following the structure, fate, and active site of single-atom catalysts (SACs) is highlighted with emphasis on a synergetic approach of both topics. X-ray absorption spectroscopy (XAS) and related X-ray techniques have become fascinating tools to characterize solids and they can be applied to almost all the transition metals deriving information about the symmetry, oxidation state, local coordination, and many more structural and electronic properties. SACs, a newly coined concept, recently gained much attention in the field of heterogeneous catalysis. In this way, one can achieve a minimum use of the metal, theoretically highest efficiency, and the design of only one active site-so-called single site catalysts. While single sites are not easy to characterize especially under operating conditions, XAS as local probe together with complementary methods (infrared spectroscopy, electron microscopy) is ideal in this research area to prove the structure of these sites and the dynamic changes during reaction. In this review, starting from their fundamentals, various techniques related to conventional XAS and X-ray photon in/out techniques applied to single sites are discussed with detailed mechanistic and in situ/operando studies. We systematically summarize the design strategies of SACs and outline their exploration with XAS supported by density functional theory (DFT) calculations and recent machine learning tools.

Towards the rational design of Pt-based alloy catalysts for the low-temperature water-gas shift reaction: from extended surfaces to single atom alloys

The rational design of Pt-based catalysts for the low-temperature water-gas-shift (LT-WGS) reaction is an active research field because of its important role played in the fuel cell-based hydrogen economy, especially in mobile applications. Previous theoretical analyses have suggested that Pt alloys, leading to a weaker CO binding affinity than the Pt metal, could help alleviate CO poisoning and thus should be promising catalysts of the LT-WGS reaction. However, experimental research along this line was rather ineffective in the past decade. In the present work, we employed the state-of-the-art kinetic Monte Carlo (KMC) simulations to examine the influences of the electronic effect by introducing sub-surface alloys and/or core–shell structures, and the synergetic effect by introducing single atom alloys on the catalytic performance of Pt-alloy catalysts. Our KMC simulations have highlighted the importance of the OH binding affinity on the catalyst surfaces to reduce the barrier of water dissociation as the rate determining step, instead of the CO binding affinity as has been emphasized before in conventional mean-field kinetic models. Along this new direction of catalyst design, we found that Pt–Ru synergetic effects can significantly increase the activity of the Pt metal, leading to Ru_1–3@Pt alloys with a tetrahedron site of one surface-three subsurface Ru atoms on the Pt host, showing a turnover frequency of about five orders of magnitude higher than the Pt metal.

KMC simulations show that decreasing the barrier of H₂O decomposition is more beneficial than decreasing the CO binding affinity in LT-WGS, while the latter was overemphasized by MF-MKM. Here Ru_1–3@Pt alloy is proposed as a promising catalyst.

Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning

Single atom alloys (SAAs) show great promise as catalysts for a wide variety of reactions due to their tunable properties, which can enhance the catalytic activity and selectivity. To design SAAs, it is imperative for the heterometal dopant to be stable on the surface as an active catalytic site. One main approach to probe SAA stability is to calculate surface segregation energy. Density functional theory (DFT) can be applied to investigate the surface segregation energy in SAAs. However, DFT is computationally expensive and time-consuming; hence, there is a need for accelerated frameworks to screen metal segregation for new SAA catalysts across combinations of metal hosts and dopants. To this end, we developed a model that predicts surface segregation energy using machine learning for a series of SAA periodic slabs. The model leverages elemental descriptors and features inspired by the previously developed bond-centric model. The initial model accurately captures surface segregation energy across a diverse series of FCC-based SAAs with various surface facets and metal-host pairs. Following our machine learning methodology, we expanded our analysis to develop a new model for SAAs formed from FCC hosts with FCC, BCC, and HCP dopants. Our final, five-feature model utilizes second-order polynomial kernel ridge regression. The model is able to predict segregation energies with a high degree of accuracy, which is due to its physically motivated features. We then expanded our data set to test the accuracy of the five features used. We find that the retrained model can accurately capture E _seg trends across different metal hosts and facets, confirming the significance of the features used in our final model. Finally, we apply our pretrained model to a series of Ir- and Pd-based SAA cuboctahedron nanoparticles (NPs), ranging in size and FCC dopants. Remarkably, our model (trained on periodic slabs) accurately predicts the DFT segregation energies of the SAA NPs. The results provide further evidence supporting the use of our model as a general tool for the rapid prediction of SAA segregation energies. By creating a framework to predict the metal segregation from bulk surfaces to NPs, we can accelerate the SAA catalyst design while simultaneously unraveling key physicochemical properties driving thermodynamic stabilization of SAAs.

Polaronic Signatures in Doped and Undoped Cesium Lead Halide Perovskite Nanocrystals through a Photoinduced Raman Mode

Lead halide perovskites (LHPs) are promising candidates for photovoltaic applications as they exhibit large carrier diffusion lengths and long carrier lifetimes among many other interesting properties. One of the widely accepted mechanisms for these properties is polaron formation, which is mainly driven by octahedral distortions of the inorganic framework. Since structure modifications of the framework largely affect associated distortions, we investigated Mn-doped and undoped CsPbX₃ (where X = Cl, Br, Cl/Br) using a local probe via micro-Raman spectroscopy and density functional theory (DFT) calculations for polaron formation. Our results highlight a new vibrational lattice mode at 132 cm^-1 due to polaronic distortion upon photoinduction. From the DFT studies, we have shown that the polaronic states are dominated by the B-site cation in the perovskite structure, but it is the strong covalent overlap of the halide which determines its stability. This elucidation to map polaronic signatures with excellent spatial resolution using traditional Raman spectroscopy can be used as a simple tool to understand the structural changes and their impacted electronic properties and thus design superior devices using its in situ applications.

Selective Interactions between Free-Atom-like d-States in Single-Atom Alloy Catalysts and Near-Frontier Molecular Orbitals

In the limit of dilute alloying-the so-called "single-atom alloy" (SAA) regime-certain bimetallic systems exhibit weak mixing between constituent metal wave functions, resulting in sharp, single-atom-like electronic states localized on the dilute component of the alloy. This work shows that when these sharp states are appropriately positioned relative to given molecular orbitals, selective hybridization is enhanced, in accordance with intuitive principles of molecular orbital theory. We demonstrate the phenomenon for activation pathways of crotonaldehyde, a model α,β-unsaturated aldehyde relevant to a wide range of chemical manufacturing. This analysis suggests new possible strategies for selectivity control in heterogeneous catalysis.

Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence

Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.

Mo single atoms in the Cu(111) surface as improved catalytic active centers for deoxygenation reactions

The surface Mo-doped Cu(111) catalyst feature improved performance towards deoxygenation reactions, acting as a single-atom alloy capable of breaking Brønsted–Evans–Polanyi relations for carbonyl bond scissions.