A density functional theory study on how γ-Al2O3 - Boehmite transformation affects carbon evolution during aqueous-phase reaction.

Gamma-alumina (γ-Al2O3), one of the most common materials, is commercially used in many catalytic applications, including the active catalyst and support. However, the problem of fast deactivation makes the utilization of the γ-Al2O3 challenging. This work elucidates the mechanism of coke formation consisting of coke deposition and evolution on γ-Al2O3(110) surfaces in differential conditions, including; clean and hydroxylation γ-Al2O3(110) in terms of partial and fully hydroxylation of OH/γ-Al2O3(110) and AlOOH(010), respectively. We demonstrated that the γ-Al2O3(110) surface is proper for atomic coke deposition and dimerization in the initial state, where the presence of OH species promotes the coke evolution to higher coke, Cn (where n ≥ 3). Also, the higher coke formation thermodynamically preferred the cyclic form to the aliphatic one. The electron transfer from substrates to adsorbed coke illustrates the role of the electron donor of catalyst surfaces corresponding to the electron acceptor of adsorbed cokes.

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys.

This work introduced the high-throughput phase prediction of PtPd-based high-entropy alloys via the algorithm based on a combined Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) and artificial neural network (ANN) technique. As the first step, the KKR-CPA was employed to generate 2,720 data of formation energy and lattice parameters in the framework of the first-principles density functional theory. Following the data generation, 15 features were selected and verified for all HEA systems in each phase (FCC and BCC) via ANN. The algorithm exhibited high accuracy for all four prediction models on 36,556 data from 9139 HEA systems with 137,085 features, verified by R2 closed to unity and the mean relative error (MRE) within 5%. From this dataset comprising 5002 and 4137 systems of FCC and BCC phases, it can be realized based on the highest tendency of HEA phase formation that (1) Sc, Co, Cu, Zn, Y, Ru, Cd, Os, Ir, Hg, Al, Si, P, As, and Tl favor FCC phase, (2) Hf, Ga, In, Sn, Pb, and Bi favor BCC phase, and (3) Ti, V, Cr, Mn, Fe, Ni, Zr, Nb, Mo, Tc, Rh, Ag, Ta, W, Re, Au, Ge, and Sb can be found in both FCC and BCC phases with comparable tendency, where all predictions are in good agreement with the data from the literature. Thus, the combination of KKR-CPA and ANN can reduce the computational cost for the screening of PtPd-based HEA and accurately predict the structure, i.e., FCC, BCC, etc.