Methanol Synthesis from CO2/CO Mixture on Cu-Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search

Thermochemical CO2 Reduction to Methanol over Metal-Based Single-Atom Catalysts (SACs): Outlook and Challenges for Developments

The conversion of thermodynamically inert CO2 into methanol holds immense promise for addressing the pressing environmental and energy challenges of our time. This article offers a succinct overview of the development of single-atom catalysts (SACs) for thermochemical hydrogenation of CO2 to methanol, encompassing research advancements, advantages, potential hurdles, and other essential aspects related to these catalysts. Our aim of this work is to provide a deeper understanding of the intricacies of the catalytic structures of the single-atom sites and their unique structure-activity relationships in catalyzing the conversion of CO2 to methanol. We also present insights into the optimal design of SACs, drawing from our own research and those of fellow scientists. This research thrust is poised to contribute significantly to the development of next-generation SACs, which are crucial in advancing the sustainable production of methanol from CO2.

Artificial intelligence driven molecule adsorption prediction (AIMAP) applied to chirality recognition of amino acid adsorption on metals

Predicting the adsorption structure of molecules has long been a challenging topic given the coupled complexity of surface binding sites and molecule flexibility. Here, we develop AIMAP, an Artificial Intelligence Driven Molecule Adsorption Prediction tool, to achieve the general-purpose end-to-end prediction of molecule adsorption structures. AIMAP features efficient exploration of the global potential energy surface of the adsorption system based on global neural network (G-NN) potential, by rapidly screening qualified adsorption patterns and fine searching using stochastic surface walking (SSW) global optimization. We demonstrate the AIMAP efficiency in constructing the Cu-HCNO6 adsorption database, encompassing 1 182 351 distinct adsorption configurations of 9592 molecules on three copper surfaces. AIMAP is then utilized to identify the best adsorption structure for 18 amino acids (AAs) on achiral Cu surfaces and the chiral Cu(3,1,17) S surface. We find that AAs chemisorb on copper surfaces in their highest deprotonated state, through both the carboxylate-amino skeleton and side groups. The chiral recognition is identified for the d-preference of Asp, Glu, and Tyr, and l-preference for His. The physical origin for the enantiospecific adsorption is thus rationalized, pointing to the critical role of the competitive adsorption between functional side groups and the carboxylate-amino skeleton at surface low-coordination sites.

Improving Molecule-Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO2 Hydrogenation

Density functional theory is widely used to gain insights into molecule-metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA) density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the self-interaction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface reaction network, namely, CO2 hydrogenation on Cu. We show that the mGGA DF used, namely, rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals and molecules, as well as molecule-metal surface adsorption and activation energies. Hence, it is a better choice for constructing molecule-metal surface reaction networks.

Visualizing the gas-sensitive structure of the CuZn surface in methanol synthesis catalysis

Methanol formation over Cu/ZnO catalysts is linked with a catalytically active phase created by contact between Cu nanoparticles and Zn species whose chemical and structural state depends on reaction conditions. Herein, we use variable-temperature scanning tunneling microscopy at elevated pressure conditions combined with X-ray photoelectron spectroscopy measurements to investigate the surface structures and chemical states that evolve when a CuZn/Cu(111) surface alloy is exposed to reaction gas mixtures. In CO2 hydrogenation conditions, Zn stays embedded in the CuZn surface, but once CO gas is added to the mixture, the Zn segregates onto the Cu surface. The Zn segregation is CO-induced, and establishes a new dynamic state of the catalyst surface where Zn is continually exchanged at the Cu surface. Candidates for the migrating few-atom Zn clusters are further identified in time-resolved imaging series. The findings point to a significant role of CO affecting the distribution of Zn in the multiphasic ZnO/CuZn/Cu catalysts.

Strained few-layer MoS2 with atomic copper and selectively exposed in-plane sulfur vacancies for CO2 hydrogenation to methanol

In-plane sulfur vacancies (Sv) in molybdenum disulfide (MoS2) were newly unveiled for CO2 hydrogenation to methanol, whereas edge Sv were found to facilitate methane formation. Thus, selective exposure and activation of basal plane is crucial for methanol synthesis. Here, we report a mesoporous silica-encapsulated MoS2 catalysts with fullerene-like structure and atomic copper (Cu/MoS2@SiO2). The main approach is based on a physically constrained topologic conversion of molybdenum dioxide (MoO2) to MoS2 within silica. The spherical curvature enables the generation of strain and Sv in inert basal plane. More importantly, fullerene-like structure of few-layer MoS2 can selectively expose in-plane Sv and reduce the exposure of edge Sv. After promotion by atomic copper, the resultant Cu/MoS2@SiO2 exhibits stable specific methanol yield of 6.11 molMeOH molMo-1 h-1 with methanol selectivity of 72.5% at 260 °C, much superior to its counterparts lacking the fullerene-like structure and copper decoration. The reaction mechanism and promoting role of copper are investigated by in-situ DRIFTS and in-situ XAS. Theoretical calculations demonstrate that the compressive strain facilitates Sv formation and CO2 hydrogenation, while tensile strain accelerates the regeneration of active sites, rationalizing the critical role of strain.

An optimal Fe-C coordination ensemble for hydrocarbon chain growth: a full Fischer-Tropsch synthesis mechanism from machine learning

Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) because of its great significance in industry has attracted huge attention since its discovery. For Fe-based catalysts, after decades of efforts, even the product distribution remains poorly understood due to the lack of information on the active site and the chain growth mechanism. Herein powered by a newly developed machine-learning-based transition state (ML-TS) exploration method to treat properly reaction-induced surface reconstruction, we are able to resolve where and how long-chain hydrocarbons grow on complex in situ-formed Fe-carbide (FeCx) surfaces from thousands of pathway candidates. Microkinetics simulations based on first-principles kinetics data further determine the rate-determining and the selectivity-controlling steps, and reveal the fine details of the product distribution in obeying and deviating from the Anderson-Schulz-Flory law. By showing that all FeCx phases can grow coherently upon each other, we demonstrate that the FTS active site, namely the A-P5 site present on reconstructed Fe3C(031), Fe5C2(510), Fe5C2(021), and Fe7C3(071) terrace surfaces, is not necessarily connected to any particular FeCx phase, rationalizing long-standing structure-activity puzzles. The optimal Fe-C coordination ensemble of the A-P5 site exhibits both Fe-carbide (Fe4C square) and metal Fe (Fe3 trimer) features.

How Solid Surfaces Control Stability and Interactions of Supported Cationic CuI(dppf) Complexes─A Solid-State NMR Study

Organometallic complexes are frequently deposited on solid surfaces, but little is known about how the resulting complex-solid interactions alter their properties. Here, a series of complexes of the type Cu(dppf)(L_x)⁺ (dppf = 1,1'-bis(diphenylphosphino)ferrocene, L_x = mono- and bidentate ligands) were synthesized, physisorbed, ion-exchanged, or covalently immobilized on solid surfaces and investigated by ³¹P MAS NMR spectroscopy. Complexes adsorbed on silica interacted weakly and were stable, while adsorption on acidic γ-Al₂O₃ resulted in slow complex decomposition. Ion exchange into mesoporous Na-[Al]SBA-15 resulted in magnetic inequivalence of ³¹P nuclei verified by ³¹P-³¹P RFDR and ¹H-³¹P FSLG HETCOR. DFT calculations verified that a MeCN ligand dissociates upon ion exchange. Covalent immobilization via organic linkers as well as ion exchange with bidentate ligands both lead to rigidly bound complexes that cause broad ³¹P CSA tensors. We thus demonstrate how the interactions between complexes and functional surfaces determine and alter the stability of complexes. The applied Cu(dppf)(L_x)⁺ complex family members are identified as suitable solid-state NMR probes for investigating the influence of support surfaces on deposited inorganic complexes.

Electrochemical hydrogen evolution on Pt-based catalysts from a theoretical perspective

Hydrogen evolution reaction (HER) by splitting water is a key technology toward a clean energy society, where Pt-based catalysts were long known to have the highest activity under acidic electrochemical conditions but suffer from high cost and poor stability. Here, we overview the current status of Pt-catalyzed HER from a theoretical perspective, focusing on the methodology development of electrochemistry simulation, catalytic mechanism, and catalyst stability. Recent developments in theoretical methods for studying electrochemistry are introduced, elaborating on how they describe solid-liquid interface reactions under electrochemical potentials. The HER mechanism, the reaction kinetics, and the reaction sites on Pt are then summarized, which provides an atomic-level picture of Pt catalyst surface dynamics under reaction conditions. Finally, state-of-the-art experimental solutions to improve catalyst stability are also introduced, which illustrates the significance of fundamental understandings in the new catalyst design.

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.

Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning

We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with legacy optimizers but without the need for subsequent re-optimization with quantum chemistry methods. For randomly sampled initial coordinates of small organic query molecules, we report systematic improvement of equilibrium and transition state geometry output as training set sizes increase. Out-of-sample S_N2 reactant complexes and transition state geometries have been predicted using the LBFGS and the QST2 algorithms with an root-mean-square deviation (RMSD) of 0.16 and 0.4 Å-after training on up to 200 reactant complex relaxations and transition state search trajectories from the QMrxn20 dataset, respectively. For geometry optimizations, we have also considered relaxation paths up to 5'595 constitutional isomers with sum formula C₇H₁₀O₂ from the QM9-database. Using the resulting OQML models with an LBFGS optimizer reproduces the minimum geometry with an RMSD of 0.14 Å, only using ∼6000 training points obtained from normal mode sampling along the optimization paths of the training compounds without the need for active learning. For converged equilibrium and transition state geometries, subsequent vibrational normal mode frequency analysis indicates deviation from MP2 reference results by on average 14 and 26 cm^-1, respectively. While the numerical cost for OQML predictions is negligible in comparison to density functional theory or MP2, the number of steps until convergence is typically larger in either case. The success rate for reaching convergence, however, improves systematically with training set size, underscoring OQML's potential for universal applicability.