Multiscale Modeling Combined with Active Learning for Microstructure Optimization of Bifunctional Catalysts

Design and screening of bimetallic catalysts for nitric oxide reduction by CO: a study of kinetic Monte Carlo simulation based on first-principles calculations

Nitric oxide (NO) emissions pose a significant environmental challenge, and the development of effective catalysts for NO reduction is crucial. This study investigates the potential of striped bimetallic catalysts for NO reduction by CO using kinetic Monte Carlo (KMC) simulations based on first-principles calculations. The simulations reveal that the activity on the striped Ni-Pt-Pt (111) surface is 1-2 orders of magnitude higher than that on the terraced Ni-Pt-Pt (111) surface at the same temperatures, demonstrating the importance of defect engineering. Sensitivity analysis identifies CO oxidation as the rate-determining step, although the 2N* association barrier is higher than CO oxidation, highlighting the need to consider reaction conditions in kinetic simulations. Volcano plots based on the formation energies of NO* and CO* successfully predict the striped Ni-Pd-Pd (111) and Ni-Rh-Rh (111) surfaces as optimal catalysts, which were further validated through DFT calculations and ab initio molecular dynamics simulations. This study offers valuable insights for designing high-performance bimetallic catalysts for NO reduction and underscores the importance of considering specific reaction conditions in kinetic simulations.

What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

We mine from the literature experimental data on the CO₂ electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict faradaic efficiencies for 11 CO₂ reduction reaction products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO₂ reduction reactions trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO₂ reduction reaction measurements, which result in quantitative disagreement between experiments.

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.

MatOpt: A Python Package for Nanomaterials Design Using Discrete Optimization

Novel materials are being enabled by advances in synthesis techniques that achieve ever better control over the atomic-scale structure of materials. The pace of materials development has been further increased by high-throughput computational experiments guided by informatics and machine learning. We have previously demonstrated complementary approaches using mathematical optimization models to search through highly combinatorial design spaces of atomic arrangements, guiding the design of nanostructured materials. In this paper, we highlight the common features of materials optimization problems that can be efficiently modeled via mixed-integer linear optimization models. To take advantage of these commonalities, we have created MatOpt, a Python package that formalizes the process of representing the design space and formulating optimization models for the on-demand design of nanostructured materials. This tool serves to bridge the gap between practitioners with expertise in materials science and those with expertise in formulating and solving mathematical optimization models, effectively lowering the barriers for applying rigorous numerical optimization capabilities during nanostructured materials development.

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

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.