Computational Methods in Heterogeneous Catalysis

Gelation and yielding behavior of polymer–nanoparticle hydrogels

Polymer–nanoparticle hydrogels are a unique class of self‐assembled, shear‐thinning, yield‐stress fluids that have demonstrated potential utility in many impactful applications. Here, we present a thorough analysis of the gelation and yielding behavior of these materials with respect to the polymer and nanoparticle component stoichiometry. Through comprehensive rheological and diffusion studies, we reveal insights into the structural dynamics of the polymer nanoparticle network that identify that stoichiometry plays a key role in gelation and yielding, ultimately enabling the development of hydrogel formulations with unique shear‐thinning and yield‐stress behaviors. Access to these materials opens new doors for interesting applications in a variety of fields including tissue engineering, drug delivery, and controlled solution viscosity.

Energy contour exploration with potentiostatic kinematics

We introduce a method of exploring potential energy contours (PECs) in complex dynamical systems based on potentiostatic kinematics wherein the systems are evolved with minimal changes to their potential energy. We construct a simple iterative algorithm for performing potentiostatic kinematics, which uses an estimate curvature to predict new configuration-space coordinates on the PEC and a potentiostat term component to correct for errors in prediction. Our methods are then applied to atomic structure models using an interatomic potential for energy and force evaluations as would commonly be invoked in a molecular dynamics simulation. Using several model systems, we assess the stability and accuracy of the method on different hyperparameters in the implementation of the potentiostatic kinematics. Our implementation is open source and available within the atomic simulation environment package.

MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning

Recent advances in machine learning technologies and their applications have led to the development of diverse structure-property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic feature vectors calculates their interactions. The results of 6239 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides not only predictions of target properties but also more detailed physicochemical insights.

1T-MoS2 Coordinated Bimetal Atoms as Active Centers to Facilitate Hydrogen Generation

Anchoring single metal atoms has been demonstrated as an effective strategy to boost the catalytic performance of non-noble metal 1T-MoS₂ towards hydrogen evolution reaction (HER). However, the dual active sites on 1T-MoS₂ still remain a great challenge. Here, first-principles calculations were performed to systematically investigate the electrocatalytic HER activity of single and dual transition metal (TM) atoms bound to the 1T-MoS₂ monolayer (TM@1T-MoS₂). The resulted Ti@1T-MoS₂ exhibits excellent structural stability, near-thermoneutral adsorption of H* and ultralow reaction barrier (0.15 eV). It is a promising single metal atom catalyst for HER, outperformed the reported Co, Ni and Pd anchoring species. Surprisingly, by further introducing Pd atoms coordinated with S atoms or S vacancies on the Ti@1T-MoS₂ surface, the resulted catalyst not only maintains the high HER activity of Ti sites, but also achieves new dual active moiety due to the appropriate H* adsorption free energy on Pd sites. This work is of great significance for realizing dual active centers on 1T-MoS₂ nanosheets and offers new thought for developing high-performance electrocatalysts for HER.

Dressing of Cu Atom over Nickel Cluster Stimulating the Poisoning-Free CO Oxidation: An Ab Initio Study

In this work using first-principles calculations based on spin-polarized density functional theory (DFT), the role of the Cu atom in degrading the poisoning of carbon monoxide (CO) over Ni_nCu clusters is unveiled. The search has been initiated with the examination of structural, magnetic, and electronic properties of Ni_n+1 and Ni_nCu clusters (1 ≤ n ≤ 12). X-ray absorption near-edge structure (XANES) spectra of Ni K-edge are computed to extract the information on the oxidation states and coordination environment of metal sites of the clusters. This study is operated with the two forms of dispersion corrections, i.e., D2 and D3, with standard DFT (with LDA and GGA functionals) for the consideration of van der Waals interactions during CO adsorption. The PBE and PBE-D3 approaches are found to be capable of yielding the experimentally observed preferential site for CO adsorption. The effect of spin-polarization on the reactivity of transition metals (TMs) toward CO adsorption is crucially assessed by the electronic reactivity descriptors such as d-band center, d-band width, and fractional filling of d-band using a spin-polarized d-band center model. The effective charge transfer from Cu to Ni atoms makes Ni atoms more efficient of charge and is attributed to the degrading adsorption of CO over Ni_nCu clusters. The Ni₁₂Cu cluster stands out with good CO oxidation activity for the Langmuir-Hinshelwood (L-H) reaction pathway.

Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis

The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, structural alloys, and energetic materials. Recently, a Co₂₅Mo₄₅Fe₁₀Ni₁₀Cu₁₀ HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NH_x (x = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

Hollow-Shell-Structured Mesoporous Silica-Supported Palladium Catalyst for an Efficient Suzuki-Miyaura Cross-Coupling Reaction

The construction of a high stability heterogeneous catalyst for privileged common catalysis is a benefit in regard to reuse and separation. Herein, a palladium diphenylphosphine-based hollow-shell-structured mesoporous catalyst (HS@PdPPh2@MSN) was prepared by immobilizing bis((diphenylphosphino)ethyltriethoxysilane)palladium acetate onto the inner wall of a mesoporous organicsilicane hollow shell, whose surface was protected by a –Si(Me)3 group. Electron microscopies confirmed its hollow-shell-structure, and structural analyses and characterizations revealed its well-defined single-site active species within the silicate network. As presented in this study, the newly constructed HS@PdPPh2@MSN enabled an efficient Suzuki-Miyaura cross-coupling reaction for varieties of substrates with up to 95% yield in mild conditions. Meanwhile, it could be reused at least five times with good activity, indicating its excellent stability and recyclability. Furthermore, the cost-effective and easily synthesized HS@PdPPh2@MSN made it a good candidate for employment in fine chemical engineering.

Tuning acid–base cooperativity to create bifunctional fiber catalysts for one-pot tandem reactions in water

A simple and straightforward route to create acid–base bifunctional fiber catalysts for efficient one-pot tandem deprotection-Knoevenagel reactions in water.

First-principles microkinetic simulations revealing the scaling relations and structure sensitivity of CO2 hydrogenation to C1 & C2 oxygenates on Pd surfaces

CO₂ hydrogenation to alcohols and other oxygenates on Pd(211) and Pd(111) surfaces was studied by microkinetic modelling. Energy scaling relations on two surfaces were established. Activity plots as a function of reaction conditions were identified.