Machine-Learning-Accelerated DFT Conformal Sampling of Catalytic Processes.

Computational modeling of catalytic processes at gas/solid interfaces plays an increasingly important role in chemistry, enabling accelerated materials and process optimization and rational design. However, efficiency, accuracy, thoroughness, and throughput must be enhanced to maximize its practical impact. By combining interpolation of DFT energetics via highly accurate Machine-Learning Potentials with conformal techniques for building the training database, we present here an original approach (that we name Conformal Sampling of Catalytic Processes, CSCP), to accelerate and achieve an accurate and thorough sampling of novel systems by exporting existing information on a worked-out case. We use methanol decomposition (of interest in the field of hydrogen production and storage) as a test catalytic reaction. Starting from worked-out Pt-based systems, we show that after only two iterations of active-learning CSCP is able to provide reaction energy diagrams for a set of 7 diverse systems (Pd, Ni, Au, Ag, Cu, Co, Fe) leading to DFT-accuracy-level predictions. Cases exhibiting a change in adsorption sites and mechanisms are also successfully reproduced as tests of catalytic path modification. The CSCP approach thus offers itself as an operative tool to fully take advantage of accumulated information to achieve high-throughput sampling of catalytic processes.

Functionalized Amorphous Carbon Materials via Reactive Molecular Dynamics Simulations.

We derive a database of atomistic structural models of amorphous carbon materials endowed with exohedral functional groups. We start from phases previously derived using the DynReaxMas method for reactive molecular dynamics simulations, which exhibit atomistic and medium-length-scale features in excellent agreement with available experimental data. Given a generic input structure/phase, we develop postprocessing simulation algorithms mimicking experimental preparation protocols aimed at: (1) "curing" the phase to decrease the defect concentration; (2) automatically selecting the most reactive carbon atoms via interaction with a probe molecular species, and (3) stabilizing the phase by saturating the valence of carbon atoms with single-bond functional groups. Although we focus on oxygen-bearing functionalities, they can be replaced with other monovalent groups, such as -H, -COOH, -CHO, so that the protocol is quite general. We finally classify reactive sites in terms of their location within the structural framework and coordination environment (edges, tunnels, rings, aromatic carbons becoming aliphatic) and try to single out descriptors that correlate with tendency to functionalization.

Covalent surface modification of single-layer graphene-like BC6N nanosheets with reactive nitrenes for selective ammonia sensing via DFT modeling.

Novel graphene-like nanomaterials with a non-zero bandgap are important for the design of gas sensors. The selectivity toward specific targets can be tuned by introducing appropriate functional groups on their surfaces. In this study, we use first-principles simulations, in the form of density functional theory (DFT), to investigate the covalent functionalization of a single-layer graphitized BC6N with azides to yield aziridine-functionalized adducts and explore their possible use to realize ammonia sensors. First, we determine the most favorable sites for physical adsorption and chemical reaction of methylnitrene, arising from the decomposition of methylazide, onto a BC6N monolayer. Then, we examine the thermodynamics of the [1 + 2]-cycloaddition reaction of various phenylnitrenes and perfluorinated phenylnitrenes para-substituted with (R = CO2H, SO3H) groups, demonstrating favorable energetics. We also monitor the effect of the functionalization on the electronic properties of the nanosheets via density of states and band structure analyses. Finally, we test four dBC6N to gBC6N substrates in the sensing of ammonia. We show that, thanks to their hydrogen bonding capabilities, the functionalized BC6N can selectively detect ammonia, with interaction energies varying from -0.54 eV to -1.37 eV, even in presence of competing gas such as CO2and H2O, as also confirmed by analyzing the change in the electronic properties and the values of recovery times near ambient temperature. Importantly, we model the conductance of a selected substrate alone and in presence of NH3to determine its effect on the integrated current, showing that humidity and coverage conditions should be properly tuned to use HO2C-functionalized BC6N-based nanomaterials to develop selective gas sensors for ammonia.

Combined experimental and computational study of the photoabsorption of the monodoped and nondoped nanoclusters Au24Pt(SR)18, Ag24Pt(SR)18, and Ag25(SR)18.

Assessing the accuracy of first-principles computational approaches is instrumental to predict electronic excitations in metal nanoclusters with quantitative confidence. Here we describe a validation study on the optical response of a set of monolayer-protected clusters (MPC). The photoabsorption spectra of Ag25(DMBT)18-, Ag24Pt(DMBT)182- and Au24Pt(SC4H9)18, where DMBT is 2,4-dimethylbenzenethiolate and SC4H9 is n-butylthiolate, have been obtained at low temperature and compared with accurate TDDFT calculations. An excellent match between theory and experiment, with typical deviations of less than 0.1 eV, was obtained, thereby validating the accuracy and reliability of the proposed computational framework. Moreover, an analysis of the TDDFT simulations allowed us to ascribe all relevant spectral features to specific transitions between occupied/virtual orbital pairs. The doping effect of Pt on the optical response of these ultrasmall MPC systems was identified and discussed.

Dichroism of plasmonic chiral nanoalloys by rational design.

Time-dependent density functional theory (TDDFT) simulations are conducted on a series of chiral gold/silver alloy nanowires to explore whether silver doping can produce an enhancement of circular dichroism at the plasmon resonance in these systems, and to identify the quantum-mechanical origin of the observed effects. We find a strong plasmonic dichroism when one or two helixes of gold atoms are substituted by silver in a linear chiral nanotube, whose pure gold counterpart does not display any plasmonic dichroism, and we rationalize this finding in terms of "decoupling" the destructive interference of excitations in the pure gold nanotube via alloying. However, further attempts to increase the plasmonic dichroism by considering multi-shell gold nanowires in which one entire shell is doped with silver did not produce the desired effect, but rather a decrease in circular dichroism. We show that this latter result is due to a more severe destructive interference in the dipole excitation contributions, and suggest that further amplification should be possible in principle by properly tuning simultaneously the nanowire structure and chemical ordering.

Atomistic mechanisms underlying plastic flow at ultralow yield stress in ductile carbon aerogels.

We investigated carbon aerogel samples with super low densities of 0.013 g cm-3 (graphite is 2.5) and conducted compression experiments showing a very low yield stress of 5-8 kPa. To understand the atomistic mechanisms operating in these super low density aerogels, we present a computational study of the mechanical response of very low-density amorphous carbonaceous materials. We start from our previously derived atomistic models (based on the DynReaxMas method) with a density of 0.16 g cm-3 representing the core regions of carbon aerogels. We considered three different phases exhibiting either a fiber-like clump morphology interconnected with string-like units or a more reticulated framework. We subjected these phases to compression and shear deformations and analyzed the resulting plastic response via an inherent-structure protocol. Strikingly, we find that these materials possess shear plastic relaxation modes with extremely low values of yield stress, negligible with respect to the finite values predicted outside this "zero-stress" region. This is followed by a succession of two additional regimes with increasing yield stress values. Our analysis of the atomistic relaxation mechanisms finds that these modes have a collective and cooperative character, taking the form of nanoscopic shear bands within the clumps. These findings rationalize our experimental observations of very low-stress plastic deformation modes in carbon aerogels, providing the first steps for developing a predictive multi-scale modeling of the mechanical properties of aerogel materials.

Understanding Ionic Diffusion Mechanisms in Li2S Coatings for Solid-State Batteries: Development of a Tailored Reactive Force Field for Multiscale Simulations.

In order to investigate Li2S as a potential protective coating for lithium anode batteries using superionic electrolytes, we need to describe reactions and transport for systems at scales of >10,000 atoms for time scales beyond nanoseconds, which is most impractical for quantum mechanics (QM) calculations. To overcome this issue, here, we first report the development of the reactive analytical force field (ReaxFF) based on density functional theory (DFT) calculations on model systems at the PBE0/TZVP and M062X/TZVP levels. Then, we carry out reactive molecular dynamics simulations (RMD) for up to 20 ns to investigate the diffusion mechanisms in bulk Li2S as a function of vacancy density, determining the activation barrier for diffusion and conductivity. We show that RMD predictions for diffusion and conductivity are comparable to experiments, while results on model systems are consistent with and validated by short (10-100 ps) ab initio molecular dynamics (AIMD). This new ReaxFF for Li2S systems enables practical RMD on spatial scales of 10-100 nm (10,000 to 10 million atoms) for the time scales of 20 ns required to investigate predictively the interfaces between electrodes and electrolytes, electrodes and coatings, and coatings and electrolytes during the charging and discharging processes.

A Resolution of Identity Technique to Speed up TDDFT with Hybrid Functionals: Implementation and Application to the Magic Cluster Series Au8n+4(SC6H5)4n+8 (n = 3-6).

The Resolution of Identity (RI) technique has been employed to speed up the use of hybrid exchange-correlation (xc) functionals at the TDDFT level using the Hybrid Diagonal Approximation. The RI has been implemented within the polTDDFT algorithm (a complex damped polarization method) in the AMS/ADF suite of programs. A speedup factor of 30 has been obtained with respect to a previous numerical implementation, albeit with the same level of accuracy. Comparison of TDDFT simulations with the experimental photoabsorption spectra of the cluster series Au8n+4(SR)4n+8(n = 3-6; R = C6H5) showed the excellent accuracy and efficiency of the method. Results were compared with those obtained via the more simplified and computationally cheaper TDDFT+TB and sTDDFT methods. The present method represents an accurate as well as computationally affordable approach to predict photoabsorption spectra of complex species, realizing an optimal compromise between accuracy and computational efficiency, and is suitable for applications to large metal clusters with sizes up to several hundreds of atoms.

Cyclohexane Oxidative Dehydrogenation on Graphene-Oxide-Supported Cobalt Ferrite Nanohybrids: Effect of Dynamic Nature of Active Sites on Reaction Selectivity.

In this work, we investigated cyclohexane oxidative dehydrogenation (ODH) catalyzed by cobalt ferrite nanoparticles supported on reduced graphene oxide (RGO). We aim to identify the active sites that are specifically responsible for full and partial dehydrogenation using advanced spectroscopic techniques such as X-ray photoelectron emission microscopy (XPEEM) and X-ray photoelectron spectroscopy (XPS) along with kinetic analysis. Spectroscopically, we propose that Fe3+/Td sites could exclusively produce benzene through full cyclohexane dehydrogenation, while kinetic analysis shows that oxygen-derived species (O*) are responsible for partial dehydrogenation to form cyclohexene in a single catalytic sojourn. We unravel the dynamic cooperativity between octahedral and tetrahedral sites and the unique role of the support in masking undesired active (Fe3+/Td) sites. This phenomenon was strategically used to control the abundance of these species on the catalyst surface by varying the particle size and the wt % content of the nanoparticles on the RGO support in order to control the reaction selectivity without compromising reaction rates which are otherwise extremely challenging due to the much favorable thermodynamics for complete dehydrogenation and complete combustion under oxidative conditions.

What Contributes to the Measured Chiral Optical Response of the Glutathione-Protected Au25 Nanocluster?

The water-soluble glutathione-protected [Au25(GSH)18]-1 nanocluster was investigated by integrating several methodologies such as molecular dynamics simulations, essential dynamics analysis, and state-of-the-art time-dependent density functional theory calculations. Fundamental aspects such as conformational, weak interactions and solvent effects, especially hydrogen-bonds, were included and found to play a fundamental role in assessing the optical response of this system. Our analysis demonstrated not only that the electronic circular dichroism is extremely sensitive to the solvent presence but also that the solvent itself plays an active role in the optical activity of such system, forming a chiral solvation shell around the cluster. Our work demonstrates a successful strategy to investigate in detail chiral interfaces between metal nanoclusters and their environments, applicable, e.g., to chiral electronic interactions between clusters and biomolecules.

Substitutional p-Type Doping in NbS2 -MoS2 Lateral Heterostructures Grown by MOCVD.

Monolayer MoS2 has attracted significant attention owing to its excellent performance as an n-type semiconductor from the transition metal dichalcogenide (TMDC) family. It is however strongly desired to develop controllable synthesis methods for 2D p-type MoS2 , which is crucial for complementary logic applications but remains difficult. In this work, high-quality NbS2 -MoS2 lateral heterostructures are synthesized by one-step metal-organic chemical vapor deposition (MOCVD) together with monolayer MoS2 substitutionally doped by Nb, resulting in a p-type doped behavior. The heterojunction shows a p-type transfer characteristic with a high on/off current ratio of ≈104 , exceeding previously reported values. The band structure through the NbS2 -MoS2 heterojunction is investigated by density functional theory (DFT) and quantum transport simulations. This work provides a scalable approach to synthesize substitutionally doped TMDC materials and provides an insight into the interface between 2D metals and semiconductors in lateral heterostructures, which is imperative for the development of next-generation nanoelectronics and highly integrated devices.

Oxidation and de-alloying of PtMn particle models: a computational investigation.

We present a computational study of the energetics and mechanisms of oxidation of Pt-Mn systems. We use slab models and simulate the oxidation process over the most stable (111) facet at a given Pt2Mn composition to make the problem computationally affordable, and combine Density-Functional Theory (DFT) with neural network potentials and metadynamics simulations to accelerate the mechanistic search. We find, first, that Mn has a strong tendency to alloy with Pt. This tendency is optimally realized when Pt and Mn are mixed in the bulk, but, at a composition in which the Mn content is high enough such as for Pt2Mn, Mn atoms will also be found in the surface outmost layer. These surface Mn atoms can dissociate O2 and generate MnOx species, transforming the surface-alloyed Mn atoms into MnOx surface oxide structures supported on a metallic framework in which one or more vacancy sites are simultaneously created. The thus-formed vacancies promote the successive steps of the oxidation process: the vacancy sites can be filled by surface oxygen atoms, which can then interact with Mn atoms in deeper layers, or subsurface Mn atoms can intercalate into interstitial sites. Both these steps facilitate the extraction of further bulk Mn atoms into MnOx oxide surface structures, and thus the progress of the oxidation process, with typical rate-determining energy barriers in the range 0.9-1.0 eV.

Mechanisms of the Oxygen Evolution Reaction on NiFe2O4 and CoFe2O4 Inverse-Spinel Oxides.

Spinel ferrites, especially Nickel ferrite, NiFe2O4, and Cobalt ferrite, CoFe2O4, are efficient and promising anode catalyst materials in the field of electrochemical water splitting. Using density functional theory, we extensively investigate and quantitatively model the mechanism and energetics of the oxygen evolution reaction (OER) on the (001) facets of their inverse-spinel structure, thought as the most abundant orientations under reaction conditions. We catalogue a wide set of intermediates and mechanistic pathways, including the lattice oxygen mechanism (LOM) and adsorbate evolution mechanism (AEM), along with critical (rate-determining) O-O bond formation barriers and transition-state structures. In the case of NiFe2O4, we predict a Fe-site-assisted LOM pathway as the preferred OER mechanism, with a barrier (ΔG ⧧) of 0.84 eV at U = 1.63 V versus SHE and a turnover frequency (TOF) of 0.26 s-1 at 0.40 V overpotential. In the case of CoFe2O4, we find that a Fe-site-assisted LOM pathway (ΔG ⧧ = 0.79 eV at U = 1.63 V vs SHE, TOF = 1.81 s-1 at 0.40 V overpotential) and a Co-site-assisted AEM pathway (ΔG ⧧ = 0.79 eV at bias > U = 1.34 V vs SHE, TOF = 1.81 s-1 at bias >1.34 V) could both play a role, suggesting a coexistence of active sites, in keeping with experimental observations. The computationally predicted turnover frequencies exhibit a fair agreement with experimentally reported data and suggest CoFe2O4 as a more promising OER catalyst than NiFe2O 4 in the pristine case, especially for the Co-site-assisted OER pathway, and may offer a basis for further progress and optimization.

Coupling between Plasmonic and Molecular Excitations: TDDFT Investigation of an Ag-Nanorod/BODIPY-Dye Interaction.

A time-dependent density functional theory (TDDFT) computational approach is employed to study the optical coupling between a plasmonic system (a Ag50 nanorod) and a fluorescent dye (BODIPY). It is found that the BODIPY dye can interact with a plasmonic system in a rather different and selective way according to the mutual orientation of the fragments. Indeed, (i) the plasmon excitation turns out to be sensitive to the presence of the BODIPY transition and (ii) this can lead to amplify or suppress the resonance accordingly to the relative orientation of the corresponding transition dipoles. To understand the coupling mechanism, we analyze the shape of the induced density in real space and the Individual Component Map of the Oscillator Strength (ICM-OS) plots and achieve a simple rationalization and insight on the origin and features of the coupling. The resulting possibility of understanding plasmon/fluorophore interactions by simple qualitative arguments opens the way to a rational design of hybrid (plasmon + dye) systems with the desired optical behavior.

Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery.

X-ray photoelectron spectroscopy (XPS) is a powerful surface analysis technique widely applied in characterizing the solid electrolyte interphase (SEI) of lithium metal batteries. However, experiment XPS measurements alone fail to provide atomic structures from a deeply buried SEI, leaving vital details missing. By combining hybrid ab initio and reactive molecular dynamics (HAIR) and machine learning (ML) models, we present an artificial intelligence ab initio (AI-ai) framework to predict the XPS of a SEI. A localized high-concentration electrolyte with a Li metal anode is simulated with a HAIR scheme for ∼3 ns. Taking the local many-body tensor representation as a descriptor, four ML models are utilized to predict the core level shifts. Overall, extreme gradient boosting exhibits the highest accuracy and lowest variance (with errors ≤ 0.05 eV). Such an AI-ai model enables the XPS predictions of ten thousand frames with marginal cost.

Exploring the materials space in the smallest particle size range: from heterogeneous catalysis to electrocatalysis and photocatalysis.

Ultrasmall clusters of subnanometer size can possess unique and even unexpected physical and chemical propensities which make them interesting in various fields of basic science and for potential applications, such as catalysis, photocatalysis, electrocatalysis, and optical and chemical sensors, just to name a few examples. These small particles often offer the tunability of their performance in an atom-by-atom fashion and an economic atom-efficient use of the metal loading. In this paper we review recent progress in the characterization and theory of well-defined subnanometer clusters in catalytic processes, and discuss their optical properties and stability, along with the potential of the size-selected clusters for the understanding of catalytic processes and for the development of new classes of catalysts.