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

A Theoretical Study of Models for X2Y2 Zintl Ions

Ab initio and extended Hückel calculations have been used to discuss the bonding scheme in X₂Y₂ neutral and ionic main group clusters. A qualitative analysis suggests that two different electron counts, 20 and 22, are possible for the butterfly structures of these systems. This results from two orbital crossings in the correlation diagram for the tetrahedral (T_d) -\u3e butterfly (C_2v) -\u3e square-planar (D_2h) transformation. Detailed ab initio computations substantiate this analysis and show that the 20-electron butterfly structure becomes increasingly favored over the tetrahedral one in X₂Y₂ clusters when the 2 atoms have increasing electronegativity difference. These results are in agreement with the known structures for the Pb₂Sb₂²­­­­­­̄ and Sb₂Bi₂²­­­­­­̄ clusters (tetrahedral-like) and the Tl₂Te₂²­­­­­­̄ one (butterfly-like)

Early stages of water/hydroxyl phase generation at transition metal surfaces – synergetic adsorption and O–H bond dissociation assistance

International audienceThe dissociation of water is a key elementary step in many processes. From density functional theory, we show on several transition metal surfaces (Ru, Co, Rh, Ir, Ni, Pd and Pt) that water prefers to chemisorb as a H-bonded dimer, one molecule being chemisorbed by the O atom, but the second one developing only a weak interaction with the surface. Counterintuitively, the molecule in the dimer that shows the smallest activation energy for O–H dissociation is the one interacting weakly with the surface. The H-bonded dimer provides a clear synergy for its chemisorption and assists the dissociation of the H-bond acceptor water molecule. Two different classes of O–H activation pathways are clearly identified with a linear activation energy–reaction energy relationship, of Brønstedt–Evans–Polanyi type

Role of water in metal catalyst performance for ketone hydrogenation: a joint experimental and theoretical study on levulinic acid conversion into gamma-valerolactone

While Ru is a poor hydrogenation catalyst compared to Pt or Pd in the gas phase, it is efficient under aqueous phase conditions in the hydrogenation of ketones such as the conversion of levulinic acid into gamma-valerolactone. Combining DFT calculations and experiments, we demonstrate that water is responsible for the enhanced reactivity of Ru under those conditions