RESUMEN
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
RESUMEN
Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy. GAME-Net is trained on a well-balanced chemically diverse dataset with C1-4 molecules with functional groups including N, O, S and C6-10 aromatic rings. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory. Applied to biomass and plastics (up to 30 heteroatoms), adsorption energies are predicted with a mean absolute error of 0.016 eV per atom. The framework represents a tool for the fast screening of catalytic materials, particularly for systems that cannot be simulated by traditional methods.
RESUMEN
The identification of the active sites and the derivation of structure-performance relationships are central for the development of high-performance heterogeneous catalysts. Here, a platform of platinum nanostructures, ranging from single atoms to nanoparticles of ≈4 nm supported on activated- and N-doped carbon (AC and NC), is employed to systematically assess nuclearity and host effects on the activity, selectivity, and stability in dibromomethane hydrodebromination, a key step in bromine-mediated methane functionalization processes. For this purpose, catalytic evaluation is coupled to in-depth characterization, kinetic analysis, and mechanistic studies based on density functional theory. Remarkably, the single atom catalysts achieve exceptional selectivity toward CH3 Br (up to 98%) when compared to nanoparticles and any previously reported system. Furthermore, the results reveal unparalleled specific activity over 1.3-2.3 nm-sized platinum nanoparticles, which also exhibit the highest stability. Additionally, host effects are found to markedly affect the catalytic performance. Specifically, on NC, the activity and CH3 Br selectivity are enhanced, but significant fouling occurs. On the other hand, AC-supported platinum nanostructures deactivate due to sintering and bromination. Simulations and kinetic fingerprints demonstrate that the observed reactivity patterns are governed by the H2 dissociation abilities of the catalysts and the availability of surface H-atoms.
RESUMEN
The electroreduction of carbon dioxide using renewable electricity is an appealing strategy for the sustainable synthesis of chemicals and fuels. Extensive research has focused on the production of ethylene, ethanol and n-propanol, but more complex C4 molecules have been scarcely reported. Herein, we report the first direct electroreduction of CO2 to 1-butanol in alkaline electrolyte on Cu gas diffusion electrodes (Faradaic efficiency=0.056 %, j1-Butanol =-0.080â mA cm-2 at -0.48â V vs. RHE) and elucidate its formation mechanism. Electrolysis of possible molecular intermediates, coupled with density functional theory, led us to propose that CO2 first electroreduces to acetaldehyde-a key C2 intermediate to 1-butanol. Acetaldehyde then undergoes a base-catalyzed aldol condensation to give crotonaldehyde via electrochemical promotion by the catalyst surface. Crotonaldehyde is subsequently electroreduced to butanal, and then to 1-butanol. In a broad context, our results point to the relevance of coupling chemical and electrochemical processes for the synthesis of higher molecular weight products from CO2 .
