Investigating High-Performance Non-Precious Transition Metal Oxide Catalysts for Nitrogen Reduction Reaction: A Multifaceted DFT–kMC–LSTM Approach

Abstract

The need for non-precious-metal catalysts for the nitrogen reduction reaction (NRR) is growing due to the high cost of precious-metal catalysts. Transition metal oxides (TMOs) are a promising option, but there is limited experimental and computational evidence for their use. The present work is a comprehensive investigation of multiple TMOs utilizing a multifaceted approach. Specifically, we integrated density functional theory (DFT) to analyze thermodynamic and electronic properties, kinetic Monte Carlo (kMC) to simulate the entire reaction pathway for candidate materials, and long−short-term memory (LSTM) methods to incorporate the long-term surface degradation within the kinetic model. Using this approach, we predicted that V 2 O 3 would exhibit superior NRR activity compared to a noble Ru catalyst, with a turnover frequency 1000 times higher, while also demonstrating stable performance for over 10,000 h. It is noteworthy that one of the key factors contributing to the high performance of TMOs is their unique ability to upshift the O 2p-band due to the tensile strain generated by N 2 -TM site binding. This allows for swift H transfer to adjacent N-containing species on TM sites by weakening H binding strength, leading to an accelerated NRR process. Overall, this work provides a holistic investigation of TMOs through a comprehensive DFT−kMC−LSTM approach and demonstrates their unique ability to leverage complementary H transfer between TM and O sites rather than suppressing the competing hydrogen evolution reaction (HER) processes.

Introducing Hybrid Modeling with Time-Series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization

“…Although the current case study pertaining to the complex and nontrivial simulation of batch crystallization of dextrose showcases that the series hybrid model outperforms the parallel hybrid model, this may not always be true. For instance, a common scenario arises during the simulation of catalyst reactions, wherein it is needed to estimate 100+ kinetic parameters (i.e., rate constant, activation energy, reaction order, and others) for more than 10 competing reactions to ensure accurate prediction of system states. , In such a case, employing an integrated series hybrid model may not be ideal, as the search space to estimate all the kinetic parameters has very high dimensions. Consequently, a parallel hybrid model can prove to be a more effective choice as it can easily learn to estimate the trajectory for a handful of key system states (e.g., precursor concentration, reactor temperature, product yield, and others) instead of estimating 100+ different kinetic parameters.…”

Optimal Surrogate Models for Predicting the Elastic Moduli of Metal–Organic Frameworks via Multiscale Features

“…Similarly, SOAP descriptors provide a numerical representation of the local atomic environment surrounding each atom within a specified distance. By encoding information about chemical bonds and short-range intermolecular interactions (for example, π–π stacking) using spherical harmonics and radial basis functions, SOAP descriptors can effectively capture the local chemical and physical information on an MOF. , Additionally, as the prediction of material performance degradation over timeknown as kinetic databecomes increasingly vital, , it is also essential in the field of MOFs to characterize their time-dependent behaviors . Due to the proficiency of SOAP descriptors in accurately representing the atomic environment, including the type of neighboring atoms and short-range intermolecular interactions, they are well suited for not only predicting static data but also projecting kinetic data.…”

First-Principles-Based Kinetic Monte Carlo Model of Hydrogen Evolution Reaction under Realistic Conditions: Solvent, Hydrogen Coverage and Electric Field Effects

“…Kinetic Monte Carlo (KMC) is an effective method to capture the transient evolution of the reactions taking place on catalytic surface. , KMC simulations can bridge the elementary reactions and macroscopic experimental performance of the catalyst under realistic conditions. , For example, Kim et al developed a KMC model to study the role of sulfur vacancy in MoS 2 catalyzed HER reaction by simulating the HER polarization curve. In that study, hydronium ions (H 3 O + ) are considered for the protons, and the effect of the operation voltage was also investigated.…”