Thermodynamic rules for zeolite formation from machine learning based global optimization

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Stereomutations in silicate oligomerization: the role of steric hindrance and intramolecular hydrogen bonding

Stereomutation has previously been explored in the literature with regard to the different mechanisms and activation energy barriers between different forms. However, the forces which govern stereomutations within the intermediate steps in chemical reactions have not been explored previously, a topic addressed in this article. The process of silicate oligomerization has been chosen in this study and it is demonstrated that steric hindrance of molecules and intramolecular hydrogen bonding govern the stereomutation of intermediate pentacoordinate silicon compounds in the silicate oligomerization process. It could be observed that the combined effect of intramolecular hydrogen bonding and steric hindrance in the silicate oligomerization process facilitates the conventional berry pseudorotation mechanism rather than other pseudorotation mechanisms reported in the literature.

Homolytic H2 dissociation for enhanced hydrogenation catalysis on oxides

The limited surface coverage and activity of active hydrides on oxide surfaces pose challenges for efficient hydrogenation reactions. Herein, we quantitatively distinguish the long-puzzling homolytic dissociation of hydrogen from the heterolytic pathway on Ga2O3, that is useful for enhancing hydrogenation ability of oxides. By combining transient kinetic analysis with infrared and mass spectroscopies, we identify the catalytic role of coordinatively unsaturated Ga3+ in homolytic H2 dissociation, which is formed in-situ during the initial heterolytic dissociation. This site facilitates easy hydrogen dissociation at low temperatures, resulting in a high hydride coverage on Ga2O3 (H/surface Ga3+ ratio of 1.6 and H/OH ratio of 5.6). The effectiveness of homolytic dissociation is governed by the Ga-Ga distance, which is strongly influenced by the initial coordination of Ga3+. Consequently, by tuning the coordination of active Ga3+ species as well as the coverage and activity of hydrides, we achieve enhanced hydrogenation of CO2 to CO, methanol or light olefins by 4-6 times.

Silica-water superstructure and one-dimensional superionic conduit in Earth's mantle

Water in Earth's deep interior is predicted to be hydroxyl (OH-) stored in nominally anhydrous minerals, profoundly modulating both structure and dynamics of Earth's mantle. Here, we use a high-dimensional neuro-network potential and machine learning algorithm to investigate the weight percent water incorporation in stishovite, a main constituent of the subducted oceanic crust. We found that stishovite and water prefer forming medium- to long-range ordered superstructures, featuring one-dimensional (1D) water channels. Synthesizing single crystals of hydrous stishovite, we verified the ordering of OH- groups in the water channels through optical and nuclear magnetic resonance spectroscopy and found an average H-H distance of 2.05(3) Å, confirming simulation results. Upon heating, H atoms were predicted to behave fluid-like inside the channels, leading to an exotic 1D superionic state. Water-bearing stishovite could feature high ionic mobility and strong electrical anisotropy, manifesting as electrical heterogeneity in Earth's mantle.

Coupled oxygen desorption and structural reconstruction accompanying reduction of copper oxide

Understanding structural transformation and phase transition accompanying reactions in a solid as a catalyst or oxygen carrier is important to the design and optimization of many catalytic or chemical looping reaction processes. Herein, we combined density functional theory calculation with the stochastic surface walking global optimization approach to track the structural transformation accompanying the reduction of CuO upon releasing oxygen. We then used machine learning (ML) methods to correlate the structural properties of CuO_x with varying x. By decomposing a reduction step into oxygen detachment and structural reconstruction, we identified two types of pathways: (1) uniform reduction with minimal structural changes; (2) segregated reduction with significant reconstruction. The results of ML analysis showed that the most important feature is the radial distribution functions of Cu-O at a percentage of oxygen vacancy [C(O_V)] < 50% and Cu-Cu at C(O_V) > 50% for CuO_x formation. These features reflect the underlying physicochemical origin, i.e., Cu-O breaking and Cu-Cu formation in the respective stage of reduction. Phase diagram analysis indicates that CuO will be reduced to Cu₂O under a typical oxygen uncoupling condition. This work demonstrates the complexity of solid structural transformation and the potential of ML methods in studying solid state materials involved in many chemical processes.

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme-the "Zeolite Sorting Hat"-that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si-O distances around 3.2-3.4 Å as the key discriminatory factor.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Optimal machine learning feature selection for assessing the mechanical properties of a zeolite framework

In this study, 45 and 249 critical features were discovered among 896 zeolite descriptors generated by the matminer package for estimating the shear and bulk moduli of zeolites, respectively. A database containing the mechanical properties of 873 zeolite structures, calculated using density functional theory, was used to train the machine learning regression model. The results of using these critical features with the LightGBM algorithm were rigorously compared with those from other regressors as well as with different sets of features. The comparison results indicate that the surrogate model with critical features increases the prediction accuracy of the bulk and shear moduli of zeolites by 17.3% and 10.6%, respectively, and reduces the prediction uncertainty by one-third of that achieved using previously available features. The suggested features originating from several physical and chemical groups highlight the unveiled relationships between the features and mechanical properties of zeolites. The robustness of the constructed model with 356 features was confirmed by applying a set of different training-test set ratios. We believe that the suggested critical features of zeolites can help to understand the mechanical behavior of a half million unlabeled hypothetical zeolite structures and accelerate the discovery of novel zeolites with unprecedented mechanical properties.

Machine learning potential era of zeolite simulation

Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked.

Simple structure descriptors quantifying the diffusion of ethene in small-pore zeolites: Insights from molecular dynamic simulations

Small-pore zeolites with 8-rings are pivotal catalytic materials to produce light olefins from non-petroleum resources employing methanol-to-olefins or syngas-to-olefins processes. The constraints of cage openings on the diffusion of light...

Monte carlo simulations and experiments of all-silica zeolite LTA assembly combining structure directing agents that match cage sizes

We investigated the influence of organic structure-directing agents (OSDAs) on the formation rates of all-silica zeolite LTA using both simulations and experiments, to shed light on the crystallization process. We compared syntheses using one OSDA with a diameter close to the size of the large cavity in LTA, and two OSDAs of diameters matching the sizes of both the small and large LTA cavities. Reaction-ensemble Monte Carlo (RxMC) simulations predict a speed up of LTA formation using two OSDAs matching the LTA pore sizes; this qualitative result is confirmed by experimental studies of crystallization kinetics, which find a speedup in all-silica LTA crystallization of a factor of 3. Analyses of simulated rings and their Si-O-Si angular energies during RxMC crystallizations show that all ring sizes in the faster crystallization exhibit lower angular energies, on average, than in the slower crystallization, explaining the origin of the speedup through packing effects.

In Situ Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment

PdAg alloy is an industrial catalyst for acetylene-selective hydrogenation in excess ethene. While significant efforts have been devoted to increase the selectivity, there has been little progress in the catalyst performance at low temperatures. Here by combining a machine-learning atomic simulation and catalysis experiment, we clarify the surface status of PdAg alloy catalyst under the reaction conditions and screen out a rutile-TiO₂ supported Pd₁Ag₃ catalyst with high performance: i.e., 85% selectivity at >96% acetylene conversion over a 100 h period in an experiment. The machine-learning global potential energy surface exploration determines the Pd-Ag-H bulk and surface phase diagrams under the reaction conditions, which reveals two key bulk compositions, Pd₁Ag₁ (R3̅m) and Pd₁Ag₃ (Pm3̅m), and quantifies the surface structures with varied Pd:Ag ratios under the reaction conditions. We show that the catalyst activity is controlled by the PdAg patterns on the (111) surface that are variable under reaction conditions, but the selectivity is largely determined by the amount of Pd exposure on the (100) surface. These insights provide the fundamental basis for the rational design of a better catalyst via three measures: (i) controlling the Pd:Ag ratio at 1:3, (ii) reducing the nanoparticle size to limit PdAg local patterns, (iii) searching for active supports to terminate the (100) facets.

The dome of gold nanolized for catalysis

Gold is noble in bulk but turns out to be a superior catalyst at the nanoscale when supported on oxides, in particular titania. The critical thickness for activity, namely two-layer gold particles on titania, observed two decades ago represents one of the most influential mysteries in the recent history of heterogeneous catalysis. By developing a Bayesian optimization controlled global potential energy surface exploration tool with machine learning potential, here we determine the atomic structures of gold particles within ∼2 nm on a TiO2 surface. We show that the smallest stable Au nanoparticle is Au24 which is pinned on the oxygen-rich TiO2 and exhibits an unprecedented dome architecture made by a single-layer Au sheet but with an apparent two-atomic-layer height. Importantly, this has the highest activity for CO oxidation at room temperature. The physical origin of the high activity is the outstanding electron storage ability of the nano-dome, which activates the lattice oxygen of the oxide. The determined CO oxidation mechanism, the simulated rate and the fitted apparent energy barrier are consistent with known experimental facts, providing key evidence for the presence of both the high-activity Au dome and the low-activity close-packed Au particles in real catalysts. The future direction for the preparation of active and stable Au-based catalysts is thus outlined.

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.