Molybdenum Carbide Nanocatalysts at Work in the in Situ Environment: A Density Functional Tight-Binding and Quantum Mechanical/Molecular Mechanical Study

Reinforcement learning for in silico determination of adsorbate-substrate structures

Reinforcement learning (RL) methods have helped to define the state of the art in the field of modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the discovery of novel algorithms. In this work, we present a RL method, based on Q-learning, for the structural determination of adsorbate@substrate models in silico, where the minimization of the energy landscape resulting from adsorbate interactions with a substrate is made by actions on states (translations and rotations) chosen from an agent's policy. The proposed RL method is implemented in an early version of the reinforcement learning software for materials design and discovery (RLMaterial), developed in Python3.x. RLMaterial interfaces with deMon2k, DFTB+, ORCA, and Quantum Espresso codes to compute the adsorbate@substrate energies. The RL method was applied for the structural determination of (i) the amino acid glycine and (ii) 2-amino-acetaldehyde, both interacting with a boron nitride (BN) monolayer, (iii) host-guest interactions between phenylboronic acid and β-cyclodextrin and (iv) ammonia on naphthalene. Density functional tight binding calculations were used to build the complex search surfaces with a reasonably low computational cost for systems (i)-(iii) and DFT for system (iv). Artificial neural network and gradient boosting regression techniques were employed to approximate the Q-matrix or Q-table for better decision making (policy) on next actions. Finally, we have developed a transfer-learning protocol within the RL framework that allows learning from one chemical system and transferring the experience to another, as well as from different DFT or DFTB levels.

A new active learning approach for adsorbate-substrate structural elucidation in silico

Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, such as finding the optimum adsorption geometry and energy, is challenging, due to the huge number of possibilities of assembling the adsorbate on the surface. In the current work, we have developed an artificial intelligence (AI) approach based on an active learning (AL) method for adsorption optimization on the surface of materials. AL uses machine learning (ML) regression algorithms and their uncertainties to make a decision (based on a policy) for the next unexplored structures to be computed, increasing, though, the probability of finding the global minimum with a small number of calculations. The methodology allows an accurate and automated structural elucidation of the adsorbate on the surface, based on the minimization of the total electronic energy. The new AL method for adsorption optimization was developed and implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial) program and was applied for C₆₀@TiO₂ anatase (101). It marks another software extension with a new feature in addition to the automatic structural elucidation of defects in materials and of nanoparticles as well. SCC-DFTB calculations were used to build the complex search surfaces with a reasonably low computational cost. An artificial neural network (NN) was employed in the AL framework evaluated together with two uncertainty quantification methods: K-fold cross-validation and non-parametric bootstrap (BS) resampling. Also, two different acquisition functions for decision-making were used: expected improvement (EI) and the lower confidence bound (LCB).

Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond

Important contemporary biological and materials problems often depend on interactions that span orders of magnitude differences in spatial and temporal dimensions. This Tutorial Review attempts to provide an introduction to such fascinating problems through a series of case studies, aimed at beginning researchers, graduate students, postdocs and more senior colleagues who are changing direction to focus on multiscale aspects of their research. The choice of specific examples is highly personal, with examples either chosen from our own work or outstanding multiscale efforts from the literature. I start with various embedding schemes, as exemplified by polarizable continuum models, 3-D RISM, molecular DFT and frozen-density embedding. Next, QM/MM (quantum mechanical/molecular mechanical) techniques are the workhorse of pm-to-nm/ps-to-ns simulations; examples are drawn from enzymes and from nanocatalysis for oil-sands upgrading. Using polarizable force-fields in the QM/MM framework represents a burgeoning subfield; with examples from ion channels and electron dynamics in molecules subject to strong external fields, probing the atto-second dynamics of the electrons with RT-TDDFT (real-time - time-dependent density functional theory) eventually coupled with nuclear motion through the Ehrenfest approximation. This is followed by a section on coarse graining, bridging dimensions from atoms to cells. The penultimate chapter gives a quick overview of multiscale approaches that extend into the meso- and macro-scales, building on atomistic and coarse-grained techniques to enter the world of materials engineering, on the one hand, and cell biology, on the other. A final chapter gives just a glimpse of the burgeoning impact of machine learning on the structure-dynamics front. I aim to capture the excitement of contemporary leading-edge breakthroughs in the description of physico-chemical systems and processes in complex environments, with only enough historical content to provide context and aid the next generation of methodological development. While I aim also for a clear description of the essence of methodological breakthroughs, equations are kept to a minimum and detailed formalism and implementation details are left to the references. My approach is very selective (case studies) rather than exhaustive. I think that these case studies should provide fodder to build as complete a reference tree on multiscale modelling as the reader may wish, through forward and backward citation analysis. I hope that my choices of cases will excite interest in newcomers and help to fuel the growth of multiscale modelling in general.

Evidence of water dissociation and hydrogenation on molybdenum carbide nanocatalyst for hydroprocessing reactions

Cubic molybdenum carbide, α-MoC1−x, was used for simultaneous water dissociation and hydrogenation reactions. Hydroprocessing reactions of heavy hydrocarbons under different hybrid environments consisting gaseous water, steam, and hydrogen at 623...

Computational study of reverse water gas shift reaction on Cu38 cluster model and Cu slab model

Density functional theory (DFT) calculation has been applied to investigate the adsorption behaviors of reactive adsorbate and the reaction pathway of reverse water gas shift (RWGS) reaction on Cu[Formula: see text] cluster and Cu slab surface. The possible adsorption configuration, sites and energies of reactive intermediates on Cu[Formula: see text] cluster and Cu slab surface have been calculated to reveal the effects between Cu[Formula: see text] cluster and Cu slab surface. In addition, transition states, reaction energies and activation barriers were calculated to RWGS mechanism on Cu[Formula: see text] cluster and Cu slab model. Compared to the mechanism of RWGS on different surfaces, it was found the Cu[Formula: see text] cluster facilitates the RWGS reaction. The intrinsic differences between Cu cluster and Cu slab model suggest that surface defects play a pivotal role in RWGS reaction.

Towards operando computational modeling in heterogeneous catalysis

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.

Efficient Catalysts for Cyclohexane Dehydrogenation Synthesized by Mo-Promoted Growth of 3D Block Carbon Coupled with Mo2C

Cyclohexane can serve as a good dihydrogen (H₂) carrier and a safer medium to store and transport H₂, as it is liquid under ambient conditions and it has a relatively high hydrogen density per unit volume (0.056 g(H₂)/cm³(Cy)_liq.). However, cyclohexane can release H₂ only with efficient cyclohexane dehydrogenation catalysts. Here, we report the synthesis of three-dimensional micron-sized block carbon-molybdenum carbide (BCMC) composite materials that can serve as noble-metal-free catalysts for cyclohexane dehydrogenation. The materials are synthesized by a facile hydrothermal synthetic route, and their structures and morphologies are characterized by various analytical techniques. The results show that the BCMCs, along with specific morphologies, form when a source of Mo is included in the precursor and that the sizes of the microparticles in them can be tailored by changing the relative amount of Mo used for their synthesis. The as-synthesized BCMC materials exhibit high catalytic activity for cyclohexane dehydrogenation while remaining stable and maintaining their catalytic activities during the reaction. The materials' catalytic activity increases as the amount of Mo used to make the materials is increased. This is further found to be because the BCMC materials containing higher amounts of ammonium molybdate or higher densities of Mo₂C provide substantially lower activation energies for the reaction. These materials can be expected to find industrial applications for catalytic production of H₂ from hydrocarbons.

Synergistic Effects of C/α-MoC and Ag for Efficient Oxygen Reduction Reaction

It remains challenging to prepare highly active and stable catalysts from earth-abundant elements for the oxygen reduction reaction (ORR). Herein we report a facile method to synthesize cost-effective heterogeneous C/α-MoC/Ag electrocatalysts. Rotating disc electrode (RDE) experiments revealed that the obtained C/α-MoC/Ag exhibited much superior catalytic performance for ORR than that of C/Ag, C/α-MoC, or even the conventional Pt/C. First-principles calculations indicated that the enhanced activity could be attributed to the efficient synergistic effects between Ag and α-MoC/C by which the energy barrier for O₂ dissociation has been substantially reduced. Furthermore, Li-air and Al-air cells were assembled to demonstrate the unprecedented electrochemical performance of C/α-MoC/Ag nanocomposites surpassing the Pt/C. Thus experimental results and theoretical calculations together showed that the heterogeneous C/α-MoC/Ag nanocomposites are a promising alternative to platinum for applications in industrial metal-air batteries.

Conversion of Methyl Mercaptan to Hydrocarbons over H-ZSM-5 Zeolite: DFT/BOMD Study

Methyl mercaptan-a harmful impurity in natural gas-may be selectively converted into H₂S and hydrocarbons [methyl mercaptan to hydrocarbon (M2TH) process], using zeolite catalysts. When M2TH is compared with the well-known MTH (methanol to hydrocarbons) process, significant differences emerge, essentially regarding the formation and distribution of products. Density functional theory (DFT) and Born-Oppenheimer molecular dynamics (BOMD) were employed to reveal possible origins for the experimentally observed differences. We established a close similarity between DFT intrinsic (electronic) reaction profiles in the stepwise mechanism of methanol and mercaptan dehydration, although no variance in reactivity was revealed. BOMD simulations at the experimental temperature of 823 K reveal rapid hydrogen abstraction from the methyl group in mercaptan, adsorbed in the zeolite cavity in the presence of the methoxy intermediate. The formation of •CH₂SH radical is 10 times faster than that of •CH₂OH at the same temperature. The varied reactivity of methanol and mercaptan in MTH and M2TH processes, respectively, can therefore first be attributed to very rapid hydrogen abstraction in mercaptan, which occurs in the zeolite cavity, following the formation of surface methoxy.

Exterior and small carbide particle promoted platinum electrocatalyst for efficient methanol oxidation

Carbides have a synergistic effect on noble metal based electrocatalysts.

High coverage adsorption and co-adsorption of CO and H2 on Ru(0001) from DFT and thermodynamics

The adsorption and co-adsorption of CO and H₂ at different coverages on p(4 × 4) Ru(0001) have been computed using periodic density functional theory (GGA-RPBE) and atomistic thermodynamics.