The technology adopts the Gibbs energy descriptor [Gδ(T)], which only needs information about the composition, enthalpy of formation and atomic density of the material. The descriptor can be used to predict the Gibbs free energy that reacts with CH4, thus obtaining the corresponding conversion....
E. et al. Vapor-phase metalation by atomic layer deposition in a metal–organic framework. J. Am. Chem. Soc. 135, 10294–10297 (2013). Article CAS Google Scholar Seo, Y. K. et al. Energy-efficient dehumidification over hierarchically porous metal–organic frameworks as advanced water ...
M. Prediction of physicochemical parameters by atomic contributions. J. Chem. Inf. Comput. Sci. 39, 868–873 (1999). Article Google Scholar Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996). Article Google ...
5e) [65]: (1) delocalized π bonds (CC) form radicals due to oxygen plasma, which reduce the number of CC and initially form CO bonds; (2) C-OH is stabilized by transferring one H atom from the same or adjacent position on CO bonds; (3) at the same time, intramolecular ...
The general format for an isotope using this tool is , where X is an elemental symbol, A is the mass number, and Z is the atomic number. The Isotope tool can be used in much the same way as the Superscript and Subscript tools. The problem below asks us to complete the given ...
Because chemical pattern representations are relatively new, the number of interfaces where the user can graphically create patterns is limited. Examples of editors to handle SMARTS notation are MarvinSketch [29], JSME [44], SMARTeditor [45], and the PubChem’s Sketcher web editor [46,47]. ...
The Mg level in black pepper pericarp was more than that mentioned in spinach (87 mg/100 g), tuna (64 mg/100 g) and brown rice (44 mg/100 g), and only slightly lower than that in almonds (270 mg/100 g) [34,35,36]. Table 4 Mineral contents in black pepper pericarp Full size...
However, a thorough atomic-level understanding of structural and electronic properties of the isolated, single active metal sites (CUS and mCUS) as well as the structure–activity relationship continues to be a major challenge; many crucial issues remain unanswered. This lack of information is due ...
instance, P. Popelier developed an NN model for the accurate estimation of atomic multipole moments of water clusters43. Similarly, some of us have recently reported a simple atomistic NN for the prediction of QTAIM partial charges of gas-phase main-element (C, H, O, and N) compounds44. ...
On-the-fly machine learning of atomic potential in density functional theory structure optimization. Phys. Rev. Lett. 120, 026102 (2018). Article CAS PubMed Google Scholar Christensen, A. S. et al. QML: a Python toolkit for quantum machine learning. GitHub https://github.com/qmlcode/qml...