Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks.

Data-driven machine learning force fields (MLFs) are more and more popular in atomistic simulations and exploit machine learning methods to predict energies and forces for unknown structures based on the knowledge learned from an existing reference database. The latter usually comes from density functional theory calculations. One main drawback of MLFs is that physical laws are not incorporated in the machine learning models, and instead, MLFs are designed to be very flexible to simulate complex quantum chemistry potential energy surface (PES). In general, MLFs have poor transferability, and hence, a very large trainset is required to span all the target feature space to get a reliable MLF. This procedure becomes more troublesome when the PES is complicated, with a large number of degrees of freedom, in which building a large database is inevitable and very expensive, especially when accurate but costly exchange-correlation functionals have to be used. In this manuscript, we exploit a high-dimensional neural network potential (HDNNP) on Pt clusters of sizes from 6 to 20 as one example. Our standard level of energy calculation is DFT GGA (PBE) using a plane wave basis set. We introduce an approximate but fast level with the PBE functional and a minimal atomic orbital basis set, and then, a more accurate but expensive level, using a hybrid functional or nonlocal vdW functional and a plane wave basis set, is reliably predicted by learning the difference with HDNNP. The results show that such a differential approach (named ΔHDNNP) can deliver very accurate predictions (error <10 meV/atom) in reference to converged basis set energies as well as more accurate but expensive xc functionals. The overall speedup can be as large as 900 for a 20 atom Pt cluster. More importantly, ΔHDNNP shows much better transferability due to the intrinsic smoothness of the delta potential energy surface, and accordingly, one can use much smaller trainset data to obtain better accuracy than the conventional HDNNP. A multilayer ΔHDNNP is thus proposed to obtain very accurate predictions versus expensive nonlocal vdW functional calculations in which the required trainset is further reduced. The approach can be easily generalized to any other machine learning methods and opens a path to study the structure and dynamics of Pt clusters and nanoparticles

Tuning the Hydrogenation Selectivity of an Unsaturated Aldehyde via Single-Atom Alloy Catalysts

Selective hydrogenation of α,β-unsaturated aldehydes to produce unsaturated alcohols remains a challenge in catalysis. Here, we explore, on the basis of first-principles simulations, single-atom alloy (SAA) catalysts on copper as a class of catalytic materials to enhance the selectivity for C═O bond hydrogenation in unsaturated aldehydes by controlling the binding strength of the C═C and C═O bonds. We show that on SAA of early transition metals such as Ti, Zr, and Hf, the C═O binding mode of acrolein is favored but the strong binding renders subsequent hydrogenation and desorption impossible. On SAA of late-transition metals, on the other hand, the C═C binding mode is favored and C═C bond hydrogenation follows, resulting in the production of undesired saturated aldehydes. Mid-transition metals (Cr and Mn) in Cu(111) appear as the optimal systems, since they favor acrolein adsorption via the C═O bond but with a moderate binding strength, compatible with catalysis. Additionally, acrolein migration from the C═O to the C═C binding mode, which would open the low energy path for C═C bond hydrogenation, is prevented by a large barrier for this process. SAA of Cr in Cu appears as an optimal candidate, and kinetic simulations show that the selectivity for propenol formation is controlled by preventing the acrolein migration from the more stable C═O to the less stable C═C binding mode and subsequent H-migration and by the formation of the O-H bond from the monohydrogenated intermediate. Dilute alloy catalysts therefore enable tuning the binding strength of intermediates and transition states, opening control of catalytic activity and selectivity

Theory and experiments join forces to characterize the electrocatalytic interface

International audienceElectrocatalysis is gaining impetus as a key technology in fuel cells and for the medium-term energy storage in the context of intermittent, renewable energy sources such as wind and solar power. Furthermore, electrocatalysis promises to convert rather inert molecules such as CO 2 and N 2 into reduced products such as CO and ammonia under relatively mild conditions (1, 2). Harnessing the full power of electrocatal-ysis is, however, hampered by a lack of understanding of the governing physical and chemical processes at the metal-electrolyte interface. In PNAS, Cheng et al. (3) bring key insight to the characterization of reaction intermediates during CO 2 electroreduction via first-principles molecular dynamics modeling. This reaction is timely and has, over the last few years, served as the playground for advanced atomistic modeling of elec-trocatalysis (4-9). The general lack of understanding is due to the inherent complexity of the electrified interface and its characterization. The characterization is difficult since most metal-liquid interfaces are amorphous. Therefore, no long-range ordering can be detected. Experimentally , the characterizations heavily rely on spectroscopy that provides average molecular orientations [infrared (IR) and Raman] or elemental composition and oxidation states (X-ray photoelectron, X-ray absorption near-edge, etc.) (1

Rh single atoms on TiO2 dynamically respond to reaction conditions by adapting their site.

Single-atom catalysts are widely investigated heterogeneous catalysts; however, the identification of the local environment of single atoms under experimental conditions, as well as operando characterization of their structural changes during catalytic reactions are still challenging. Here, the preferred local coordination of Rh single atoms is investigated on TiO2 during calcination in O2, reduction in H2, CO adsorption, and reverse water gas shift (RWGS) reaction conditions. Theoretical and experimental studies clearly demonstrate that Rh single atoms adapt their local coordination and reactivity in response to various redox conditions. Single-atom catalysts hence do not have static local coordinations, but can switch from inactive to active structure under reaction conditions, hence explaining some conflicting literature accounts. The combination of approaches also elucidates the structure of the catalytic active site during reverse water gas shift. This insight on the real nature of the active site is key for the design of high-performance catalysts