Table 4 The best 5 distance-spectral graphical invariant for \(CNC_6[n]\) with \(1\le n\le 10\).

From: Predictive potential of distance-related spectral graphical descriptors for structure-property modeling of thermodynamic properties of polycyclic hydrocarbons with applications

\(CNC_6[n]\)

\(En_H\)

\(Es_{GA^2}\)

\(En_{GA^2}\)

\(En_{DD}\)

\(Es_{ABC^2}\)

\(CNC_6{[}1{]}\)

32.6030

63.6081

33.5744

166.0818

28.3634

\(CNC_6{[}2{]}\)

75.8024

151.8531

77.6014

410.3461

58.4956

\(CNC_6{[}3{]}\)

136.6019

280.5523

141.0622

760.2151

100.2979

\(CNC_6{[}4{]}\)

214.9346

442.9552

221.5188

1215.3431

154.4797

\(CNC_6{[}5{]}\)

310.9049

644.8226

321.0271

1776.4108

220.5881

\(CNC_6{[}6{]}\)

424.5764

879.8015

436.9177

2443.6358

298.7547

\(CNC_6{[}7{]}\)

556.0342

1150.0168

571.3838

3217.4930

388.8993

\(CNC_6{[}8{]}\)

705.2066

1465.6511

726.9697

4097.6440

490.8686

\(CNC_6{[}9{]}\)

870.5932

1801.4729

893.6640

5077.6889

604.0102

\(CNC_6{[}10{]}\)

1055.0350

2190.3040

1085.3135

6170.4290

730.0064