Luca

Lerne von mir: Ein künstliches neuronales Netzwerk, eine Art maschineller Lernalgorithmus, wird verwendet, um den Zusammenhang zwischen der CO₂‐Adsorptionskapazität und den Struktureigenschaften von porösem Kohlenstoff zu modellieren. Danach wird es als implizites Modell verwendet, um die CO₂‐Adsorptionskapazität von unbekannten porösen Kohlenstoffen basierend auf ihren Struktureigenschaften genau vorherzusagen.

Abstract

Porous carbons with different textural properties exhibit great differences in CO₂ adsorption capacity. It is generally known that narrow micropores contribute to higher CO₂ adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO₂ adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO₂ adsorption of porous carbons and corresponding textural properties. The trained neural network is further employed as an implicit model to estimate its ability to predict the CO₂ adsorption capacity of unknown porous carbons. Interestingly, the practical CO₂ adsorption amounts are in good agreement with predicted values using surface area, micropore and mesopore volumes as the input values simultaneously. This unprecedented deep learning neural network (DNN) approach, a type of machine learning algorithm, exhibits great potential to predict gas adsorption and guide the development of next‐generation carbons.

Abstract

In the present work, we revisit the problem of atomic orbitals from the positions mostly dictated by semiempirical approaches in quantum chemistry. To construct basis set, having proper nodal structure and simple functional form of orbitals and representing atomic properties with reasonable accuracy, authors propose an Ansatz based on gradual improvement of hydrogen atomic orbitals. According to it, several basis sets with different numbers of variable parameters are considered and forms of orbitals are obtained for the 2nd-row elements either by minimization of their ground state energy (direct problem) or by extracting from atomic spectra (inverse problem). It is shown that so-derived three- and four-parametric basis sets provide accurate description of atomic properties, being, however, substantially provident for computational requirements and, what is more important, simple to handle in analytic models of quantum chemistry. Since the discussed Ansatz allows a generalization for heavier atoms, our results may be considered not only as a solution for light elements, but also as a proof of concept with possible further extension to a wider range of elements.