An effective scheme to determine surface energy and its relation with adsorption energy

Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO2 Reduction Reaction

“…To understand the role of active atoms on the top site, we classified each data point in terms of the identity of active atoms on the top site, thereby decomposing the Gaussian distributions, as shown in Figures S6–S8. Cu as the top site has the weakest adsorption of COOH*, CO*, and CHO* than that on other atoms, which is consistent with the trend in the previous reports. ,, The phenomenon indicates that Cu as the active top site is impossible for the designed FeCoNiCuMo HEA system because the intermediates are more likely to be adsorbed on other metal atoms than Cu atoms. Moreover, to accelerate CO 2 + H + + e – → COOH*, COOH* + H + + e – → CO* + H 2 O, and CO* + H + + e – → CHO* simultaneously, it is desired that the variations of Δ E ad‑COOH* and Δ E ad‑CHO* are relatively larger while the variation of Δ E ad‑CO* is smaller on specific top site.…”

“…To understand the role of active atoms on the top site, we classified each data point in terms of the identity of active atoms on the top site, thereby decomposing the Gaussian distributions, as shown in Figures S6−S8. Cu as the top site has the weakest adsorption of COOH*, CO*, andCHO* than that on other atoms, which is consistent with the trend in the previous reports 3,4,35. The phenomenon indicates that Cu as the active top site is impossible for the designed FeCoNiCuMo HEA system because the intermediates are more likely to be adsorbed on other metal atoms than Cu atoms.…”

Application of neural network in metal adsorption using biomaterials (BMs): a review

“…However, developing a neural network-based scheme that can predict surface energies on sorbents can assist in optimizing the relative stability of adsorbent surfaces for spontaneous adsorption of targeted metal pollutants on available active sites. [196][197][198][199] In addition to developing new computational strategies, the researchers emphasize on meaningful experimental results to understand the equilibrium, saturation and regenerative potential of adsorption systems from a chemical science point of view. 56,200 The possibility of overtting is the second major issue that needs to be addressed by researchers while applying ANN algorithms to predict biomaterial systems' efficacy for wastewater treatments.…”

High-entropy alloy catalysts: high-throughput and machine learning-driven design

“…The designed SQSs can model disordered alloys with atomic resolution and the radial distribution function of a random system is a quintessential concept for the generation of realistic random structures [44] . Moreover, by combining artificial neural networks (ANNs) and evolutionary algorithms, our group proposed a neural evolution structure (NES) generation methodology for HEA structure generation [Figure 2] [45] . According to pair distribution functions and atomic properties, a model is first trained on smaller unit cells and then the larger unit cell is generated by the inverse design approach.…”

“…We believe that HT From left to right: an input structure (a template mesh with N sites), a representation of the N input arrays, the ANNs, a representation of the N output vectors, the atom type associated with each output vector and the generated configuration. Reproduced with permission [45] . Copyright 2021, AIP Publishing.…”