INFORMAZIONI SU QUESTO ARTICOLO

Cita

Introduction

The molecular descriptor is the final result of logic and mathematical procedure which transform chemical information encoded within a symbolic representation of a molecule into a useful member or the result of some standardized experiments. Attention is paid to the term “useful” with its double meanings. It means that the number can give more insights into the interpretation of the molecular properties and / or is able to take part in a model for the prediction of some interesting property of the molecules.

The numerical invariants of chemical graphs are increasingly being used for a single number characterization of the corresponding chemical compounds [5]. These invariants are named in the chemical literature as topological indices [1, 2] or graph-theocratical indices [29]. The former term is the more common of the two. Topological indices have found application in various areas of chemistry, physics, mathematics, informatics, biology, etc [29], but their most important use to date is in the non-empirical Quantitative Structure- Property Relationships (QSPR) and Quantitative Structure -Activity Relationships (QSAR) [4, 24, 26, 27].

Survey on Graph Theocratical Matrices

Vertex Adjacency Matrix: The term vertex adjacency matrix was first introduced in chemical graph theory by Mallion in his interesting paper [20] on graph theocratical aspects of the ring current theory. Below we give the vertex adjacency matrix of the vertex labeled graph G.

et G = (V,E) be a grapLh where V = {1,2,3,··· ,n} the vertex adjacency matrix of a graph with vertex set V = {1,2,3,··· ,n} is the n × n matrix in which aij = 1 if and only if there is an adjacency from vertex i to vertex j. Each diagonal entry in the adjacency matrix of a graph is zero. i.e.,

aij={1,if vi is adjacent to vj;0,otherwise.$$\begin{array}{} \displaystyle {a_{{\rm{ij}}}} = \left\{ \begin{array}{*{20}{l}} {1,}&{{\text{if}\;}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ {0,}&{{\rm{otherwise}}.} \end{array}\right. \end{array}$$

Vertex Zagreb Adjacency Matrix: Motivated by Zagreb matrix [10] we define new adjacency matrix based on the vertex degrees which is as follows:

let G = (V,E) be a graph where V = {1,2,3,··· ,n} then the vertex Zagreb adjacency matrix Z1(G) of a graph G is defined as follows i.e.,

aij={1,if vi is adjacent to vj;deg(vi)2if vi=vj;0,otherwise.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} {1,}&{{\text{if}\;}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ {deg{{\left( {{v_i}} \right)}^2}}&{{\text{if}\;}{v_i} = {v_j};}\\ {0,}&{{\rm{otherwise}}.} \end{array}\right. \end{array}$$

Forgotten Adjacency Matrix: Recently, Gutman and Furtula [7] have studied the forgotten topological index F(G) of a molecular graph G. Based on the definition of adjacency matrix and vertex degrees of a graph G, we define the forgotten adjacency matrix as follows:

let G = (V,E) be a graph where V = {1,2,3,···, n} then the forgotten adjacency matrix F(G) of a graph G is defined as follows i.e.,

aij={1,if vi is adjacent to vj;deg(vi)3if vi=vj;0,otherwise.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} {1,}&{{\text{if}\;}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ {deg{{\left( {{v_i}} \right)}^3}}&{{\text{if}\;}{v_i} = {v_j};}\\ {0,}&{{\rm{otherwise}}.} \end{array}\right. \end{array}$$

Harmonic Matrix: Motivated by Harmonic index [6] of a molecular graph G, the harmonic matrix is defined as follows:

let G = (V,E) be a graph where V = {1,2,3,··· ,n} then the harmonic matrix H(G) of a graph G is defined as follows i.e.,

aij={2deg(vi)+deg(vj),if vi is adjacent to vj;0,otherwise.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} {\frac{2}{{deg({v_i}) + deg({v_j})}},}&{{\text{if}\;}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ {0,}&{{\rm{otherwise}}{\rm{.}}} \end{array}\right. \end{array}$$

Geometric-Airthmetic Matrix: Again on the same lines of harmonic index, we define GA- matrix [28] based on a GA-index of a molecular graph G as:

let G = (V,E) be a graph where V = {1,2,3,··· ,n} then the harmonic matrix H(G) of a graph G is defined as follows i.e.,

aij={2deg(vi)deg(vj)deg(vi)+deg(vj),if vi is adjacent to vj;0,otherwise.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} {\frac{{2\sqrt {deg({v_i})deg({v_j})} }}{{deg({v_i}) + deg({v_j})}},}&{{\text{if}\;}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ {0,}&{{\rm{otherwise}}.} \end{array}\right. \end{array}$$

Degree-Sum Matrix: Ramane et. al [22] have introduced degree-sum matrix associated with a graph and obtained some upper and lower bounds for its eigenvalues.

let G = (V,E) be a graph where V = {1,2,3,··· ,n} then the degree-sum matrix H(G) of a graph G is defined as follows i.e.,

aij={deg(vi)+deg(vj),if vivj.0,otherwise.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} {deg({v_i}) + deg({v_j}),}&{{\text{if}\;}{v_i} \ne {v_j}.}\\ {0,}&{{\rm{otherwise}}{\rm{.}}} \end{array}\right. \end{array}$$

Laplacian Matrix: In [11] Gutman and Zhou have put forward the Laplacian matrix. The Laplacian matrix sometimes also called a Kirchoff matrix [3] due to its role in matrix tree theorem, Implicitin the electrical network work of Kirchoff in his paper Kirchoff also introduced the concept of the spanning tree.

let G = (V,E) be a graph where V = {1,2,3,··· ,n} then the Laplacian matrix L(G) of a graph G is defined as follows i.e.,

aij={1,if vivj and vi is adjacent to vj;0if vivj and vi is not adjacent to vj;degvi,vi=vj.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} { - 1,}&{{\text{if}\;}{v_i} \ne {v_j}{\rm{ and }}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ 0&{{\text{if}\;}{v_i} \ne {v_j}{\rm{ and }}{v_i}{\rm{ is not adjacent to }}{v_j};}\\ {deg{v_i},}&{{v_i} = {v_j}.} \end{array}\right. \end{array}$$

Sum-Connectivity Matrix: The sum-connectivity matrix denoted by SCij was introduced independently by Zhou and Trianjstic [34] it is defined as follows:

let G = (V,E) be a graph where V = {1,2,3,··· ,n} then the harmonic matrix SCij(G) of a graph G is defined as follows i.e.,

aij={1deg(vi)+deg(vj),if vi is adjacent to vj;0,otherwise.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} {\frac{1}{{\sqrt {deg({v_i}) + deg({v_j})} }},}&{{\text{if}\;}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ {0,}&{{\rm{otherwise}}.} \end{array}\right. \end{array}$$

Vertex Randić Matrix: The vertex-connectivity matrix denoted by Rij introduced by Randic [23]. It can be regarded as edge-weighted matrix of the graph defined as:

let G = (V,E) be a graph where V = {1,2,3,··· ,n} then the harmonic matrix R(G) of a graph G is defined as follows i.e.,

aij={1deg(vi)deg(vj),if vi is adjacent to vj;0,otherwise.$$\begin{array}{} \displaystyle {a_{ij}} = \left\{ \begin{array}{*{20}{l}} {\frac{1}{{\sqrt {deg({v_i})deg({v_j})} }},}&{{\text{if}\;}{v_i}{\;\text{is adjacent to}\;}{v_j};}\\ {0,}&{{\rm{otherwise}}.} \end{array}\right. \end{array}$$

The characteristic polynomial and corresponding energy

Vertex Adjacency Energy. The characteristic polynomial of A(G) is denoted by

fn(G,a)=det(aIA(G)).$$\begin{array}{} \displaystyle {f_n}(G,a) = det(aI - A(G)). \end{array}$$

Since A(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

a1a2a3,an.$$\begin{array}{} \displaystyle {a_1} \ge {a_2} \ge {a_3} \ge \cdots , \ge {a_n}. \end{array}$$

The energy of G is then defined as

E(G)=i=1n|ai|.$$\begin{array}{} \displaystyle E(G) = \mathop \sum \limits_n^{i = 1} \left| {{a_i}} \right|. \end{array}$$

Vertex Zagreb Adjacency Energy. The characteristic polynomial of Z1(G) is denoted by

fn(G,z)=det(zIZ1(G)).$$\begin{array}{} \displaystyle {f_n}(G,z) = det(zI - {Z_1}(G)). \end{array}$$

Since Z1(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

z1z2z3,zn.$$\begin{array}{} \displaystyle {z_1} \ge {z_2} \ge {z_3} \ge \cdots , \ge {z_n}. \end{array}$$

The vertex Zagreb energy of G is then defined as

Z1E(G)=i=1n|zi|.$$\begin{array}{} \displaystyle {Z_1}E(G) = \mathop \sum \limits_n^{i = 1} \left| {{z_i}} \right|. \end{array}$$

Forgotten Energy. The characteristic polynomial of F(G) is denoted by

fn(G,f)=det(fIF(G)).$$\begin{array}{} \displaystyle {f_n}(G,f) = det(fI - F(G)). \end{array}$$

Since F(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

f1f2f3,fn.$$\begin{array}{} \displaystyle {f_1} \ge {f_2} \ge {f_3} \ge \cdots , \ge {f_n}. \end{array}$$

The forgotten energy of G is then defined as

FE(G)=i=1n|fi|.$$\begin{array}{} \displaystyle FE(G) = \mathop \sum \limits_n^{i = 1} \left| {{f_i}} \right|. \end{array}$$

Harmonic Energy. The characteristic polynomial of H(G) is denoted by

fn(G,h)=det(hIH(G)).$$\begin{array}{} \displaystyle {f_n}(G,h) = det(hI - H(G)). \end{array}$$

Since H(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

h1h2h3,hn.$$\begin{array}{} \displaystyle {h_1} \ge {h_2} \ge {h_3} \ge \cdots , \ge {h_n}. \end{array}$$

The harmonic energy of G is then defined as

HE(G)=i=1n|hi|.$$\begin{array}{} \displaystyle HE(G) = \sum\limits_{i = 1}^n | {h_i}|. \end{array}$$

Geometric-Airthmetic Energy. The characteristic polynomial of GA(G) is denoted by

fn(G,g)=det(gIGA(G)).$$\begin{array}{} \displaystyle {f_n}(G,g) = det(gI - GA(G)). \end{array}$$

Since GA(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

g1g2g3,gn.$$\begin{array}{} \displaystyle {g_1} \ge {g_2} \ge {g_3} \ge \cdots , \ge {g_n}. \end{array}$$

The Geometric-Airthmetic energy of G is then defined as

GAE(G)=i=1n|gi|.$$\begin{array}{} \displaystyle GAE(G) = \mathop \sum \limits_n^{i = 1} |{g_i}|. \end{array}$$

Degree-Sum Energy. The characteristic polynomial of DS(G) is denoted by

fn(G,d)=det(dIDS(G)).$$\begin{array}{} \displaystyle {f_n}(G,d) = det(dI - DS(G)). \end{array}$$

Since DS(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

d1d2d3,dn.$$\begin{array}{} \displaystyle {d_1} \ge {d_2} \ge {d_3} \ge \cdots , \ge {d_n}. \end{array}$$

The Degree-Sum energy of G is then defined as

DSE(G)=i=1n|di|.$$\begin{array}{} \displaystyle DSE(G) = \mathop \sum \limits_n^{i = 1} |{d_i}|. \end{array}$$

Laplacian Energy. The characteristic polynomial of L(G) is denoted by

fn(G,λ)=det(λIL(G)).$$\begin{array}{} \displaystyle {f_n}(G,\lambda ) = det(\lambda I - L(G)). \end{array}$$

Since L(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

λ1λ2λ3,λn.$$\begin{array}{} \displaystyle {\lambda _1} \ge {\lambda _2} \ge {\lambda _3} \ge \cdots , \ge {\lambda _n}. \end{array}$$

The Laplacian energy of G is then defined as

LE(G)=i=1n|λi|.$$\begin{array}{} \displaystyle LE(G) = \mathop \sum \limits_n^{i = 1} |{\lambda _i}|. \end{array}$$

Sum-Connectivity Energy. The characteristic polynomial of SC(G) is denoted by

fn(G,μ)=det(μISC(G)).$$\begin{array}{} \displaystyle {f_n}(G,\mu ) = det(\mu I - SC(G)). \end{array}$$

Since SC(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

μ1μ2μ3,μn.$$\begin{array}{} \displaystyle {\mu _1} \ge {\mu _2} \ge {\mu _3} \ge \cdots , \ge {\mu _n}. \end{array}$$

The Sum-Connectivity energy of G is then defined as

SCE(G)=i=1n|μi|.$$\begin{array}{} \displaystyle SCE(G) = \mathop \sum \limits_n^{i = 1} |{\mu _i}|. \end{array}$$

Vertex Randić Energy. The characteristic polynomial of R(G) is denoted by

fn(G,r)=det(rIR(G)).$$\begin{array}{} \displaystyle {f_n}(G,r) = det(rI - R(G)). \end{array}$$

Since R(G) is real and symmetric, its eigenvalues are real numbers and we label them in non-increasing order as follows:

r1r2r3,rn.$$\begin{array}{} \displaystyle {r_1} \ge {r_2} \ge {r_3} \ge \cdots , \ge {r_n}. \end{array}$$

The Vertex Randić Energy of G is then defined as

RE(G)=i=1n|ri|.$$\begin{array}{} \displaystyle RE(G) = \mathop \sum \limits_n^{i = 1} |{r_i}|. \end{array}$$

The Use of Graph Theoretical Matrices in QSPR Studies

We have used nine Graph Theoretical Matrices viz, vertex-adjacency matrix, vertex Zagreb adjacency Matrix, forgotten adjacency matrix, harmonic matrix, geometric-airthmetic matrix, degree-sum matrix, laplacian matrix, sum-connectivity matrix and vertex Randi ć matrix respectively for modeling eight representative physical properties [boiling points(BP), molar volumes (mv) at 20°C, molar refractions (mr) at 20°C, heats of vaporization (hv) at 25°C, surface tensions (st) 20°C and melting points (mp)] of the 68 alkanes from n-butanes to nonanes. Values for these property were taken from Dejan Plavsi ć et. al [21]. The corresponding energy of the above said matrices and the experimental values for the physical properties of 68 alkanes are listed in Table 1 and 2 respectively.

S.No.Alkanebp(°C)mv(cm3)mr(cm3)hv(kJ)ct(°C)cp(atm)st(dyne/cm)mp(°C)
1Butane-0.500152.0137.47-138.35
22-methyl propane-11.730134.9836-159.60
3Pentane36.074115.20525.265626.42196.6233.3116.00-129.72
42-methyl butane27.852116.42625.292324.59187.7032.915.00-159.90
52,2 dimethylpropane9.503112.07425.724321.78160.6031.57-16.55
6Hexane68.740130.68829.906631.55234.7029.9218.42-95.35
72-methylpentane60.271131.93329.945929.86224.9029.9517.38-153.67
83 -methyalpentane63.282129.71729.801630.27231.2030.8318.12-118.00
92,2-methylbutane49.741132.74429.934727.69216.2030.6716.30-99.87
102,3 -dimethylbutane57.988130.24029.810429.12227.1030.9917.37-128.54
11Heptanes98.427146.54034.550436.55267.5527.0120.26-90.61
122-methylhexane90.052147.65634.590834.80257.9027.219.29-118.28
133-methylhexane91.850145.82134.459735.08262.4028.119.79-119.40
143-ethylpentane93.475143.51734.282735.22267.6028.620.44-118.60
152,2-dimethylpentane79.197148.69534.616632.43247.7028.418.02-123.81
162,3 -dimethylpentane89.784144.15334.323734.24264.6029.219.96-119.10
172,4-dimethylpentane80.500148.94934.619232.88247.1027.418.15-119.24
183,3-dimethylpentane86.064144.53034.332333.02263.003019.59-134.46
19Octane125.665162.59239.192241.48296.2024.6421.76-56.79
202-methylheptane117.647163.66339.231639.68288.0024.820.60-109.04
213-methylheptane118.925161.83239.100139.83292.0025.621.17-120.50
224-methylheptane117.709162.10539.117439.67290.0025.621.00-120.95
233-ethylhexane118.53160.0738.9439.40292.0025.7421.51
242,2-dimethylhexane10.84164.2839.2537.29279.0025.619.60-121.18
252,3-dimethylhexane115.607160.3938.9838.79293.0026.620.99
262,4-dimethylhexane109.42163.0939.1337.76282.0025.820.05-137.50
272,5-dimethylhexane109.10164.6939.2537.86279.002519.73-91.20
283,3-dimethy lhexane111.96160.8739.0037.93290.8427.220.63-126.10
293,4-dimethy lhexane117.72158.8138.8439.02298.0027.421.64
303 -ethyl-2-methylpentane115.65158.7938.8338.52295.0027.421.52-114.96
313 -ethyl-3 -methylpentane118.25157.0238.7137.99305.0028.921.99-90.87
322,2,3-trimethylpentane109.84159.5238.9236.91294.0028.220.67-112.27
332,2,4-trimethylpentane99.23165.0839.2635.13271.1525.518.77-107.38
342,3,3-trimethylpentane114.76157.2938.7637.22303.002921.56-100.70
352,3,4-trimethylpentane113.46158.8538.8637.61295.0027.621.14-109.21
362,2, 3,3-tetramethylbutane106.470270.824.5
36Nonane150.79178.7143.8446.44322.0022.7422.92-53.52
372-methyloctane143.26179.7743.8744.65315.0023.621.88-80.40
383-methyloctane144.18177.9543.7244.75318.0023.722.34-107.64
394-methyloctane142.48178.1543.7644.75318.3023.0622.34-113.20
403-ethylheptane143.00176.4143.6444.81318.0023.9822.81-114.90
414-ethylheptane141.20175.6843.4944.81318.3023.9822.81
422,2-dimethylheptane132.69180.5043.9142.28302.0022.820.80-113.00
432,3-dimethylheptane140.50176.6543.6343.79315.0023.7922.34-116.00
442,4-dimethylheptane133.50179.1243.7342.87306.0022.723.30
452,5-dimethylheptane136.00179.3743.8443.87307.8022.721.30
462,6- dimethylheptane135.21180.9143.9242.82306.0023.720.83-102.90
473,3- dimethylheptane137.300176.89743.687042.66314.0024.1922.01
483,4- dimethylheptane140.600175.34943.547343.84322.7024.7722.80
493,5- dimethylheptane136.000177.38643.637942.98312.3023.5921.77
504,4- dimethylheptane135.200176.89743.602242.66317.8024.1822.01
513-ethyl-2-methylhexane138.000175.44543.655043.84322.7024.7722.80
524-ethyl-2-methylhexane133.800177.38643.647242.98330.3025.5621.77
533-ethyl-3-methylhexane140.600173.07743.268044.04327.2025.6623.22
542,2,4- trimethylhexane126.540179.22043.763840.57301.0023.3920.51-120.00
552,2,5- trimethylhexane124.084181.34643.935640.17296.6022.4120.04-105.78
562,3,3- trimethylhexane137.680173.78043.434742.23326.1025.5622.41-116.80
572,3,4- trimethylhexane139.000173.49843.491742.93324.2025.4622.80
582,3,5- trimethylhexane131.340177.65643.647441.42309.4023.4921.27-127.80
593,3,4- trimethylhexane140.460172.05543.340742.28330.6026.4523.27-101.20
603,3-diethylpentane146.168170.18543.113443.36342.8026.9423.75-33.11
612,2-dimethyl-3-ethylpentane133.830174.53743.457142.02322.6025.9622.38-99.20
622,3-dimethyl-3-ethylpentane142.000170.09342.954242.55338.6026.9423.87
632,4-dimethyl-3-ethylpentane136.730173.80443.403742.93324.2025.4622.80-122.20
642,2,3,3-tetramethylpentane140.274169.49543.214741.00334.5027.0423.38-99.0
652,2,3,4- tetramethylpentane133.016173.55743.435941.00319.6025.6621.98-121.09
662,2,4,4- tetramethylpentane122.284178.25643.874738.10301.6024.5820.37-66.54
672,3,3,4- tetramethylpentane141.551169.92843.201641.75334.5026.8523.31-102.12

S.No.AlkaneE(G)Z1E(G)FE(G)HE(G)GAE(G)DSE(G)LE(G)SCE(G)RE(G)
1Butane2.82810182.8444.26818.16662.5163
22-methyl propane2.82811.243301.732318.42261.7322
3Pentane4.47214.00125.9993.2745.28625.87482.983.414
42-methyl butane5.22616382.864.75826.25382.723.154
52,2 dimethylpropane420681.63.226.8881.7882
6Hexane6.98817.99344.0866.78833.698103.7684.236
72-methylpentane6.06414.999463.2545.72234.168103.153.528
83 -methyalpentane6.920463.9246.44234.168103.6524.23
92,2-methylbutane5.81823.99276.0012.7985.02434.485102.8243.224
102,3 -dimethylbutane6.00422582.9065.29234.628102.9443.334
11Heptanes8.05421.99941.984.597.86841.58124.2844.732
122-methylhexane7.72824.00153.9914.2827.26642.1211.9994.174.376
133-methylhexane7.8824544.3767.45643.05811.9994.1124.632
153-ethylpentane6.920564.5867.65438.08104.244.828
162,2-dimethylpentane6.7227.99983.9993.1765.96643.0612.0013.243.582
172,3 -dimethylpentane7.66424.99965.9993.9666.9242.64812.0013.8724.404
182,4-dimethylpentane6.15620463.2266.14843.015103.3123.632
193,3-dimethylpentane6.5962883.9993.9446.80243.266123.6665.74
20Octane9.51626505.3249.31249.496144.765.468
212-methylheptane8.76428624.7928.29450.09144.334.82
223-methylheptane9.40827.999625.1388.92449.996144.6085.41
234-methylheptane8.82827.999624.7348.40250.09144.2984.974
243-ethylhexane7.8824545.2829.03450.0911.9994.5365.502
252,2-dimethylhexane8.31231.99991.9994.0087.52251.14143.8924.424
262,3-dimethylhexane8.64630.00173.9994.3767.9550.671154.1984.792
272,4-dimethylhexane8.56430744.3147.86249.82514.0014.1144.678
282,5-dimethylhexane8.47230743.7147.09550.671143.7614.468
293,3-dimethylhexane8.5231.998924.3347.77251.14143.9684.752
303,4-dimethylhexane9.33230745.0028.58450.41314.0014.55.41
313-ethyl-2-methylpentane7.66429704.5884.5151.016144.164.916
323-ethyl-3-methylpentane7.5963291.2465.0488.52651.14144.3365.488
332,2,3 -trimethylpentane7.334.0011043.9027.22851.876163.8084.448
342,2,4-trimethylpentane7.38433.9991043.14446.38651.701143.0563.684
352,3,3-trimethylpentane8.05434104.0014.0267.37447.686144.0684.6
362,3,4-trimethylpentane8.4243286.0014.0027.551.24144.094.574
362,2,3,3-tetramethylbutane7.212381342.8165.89252.69815.9983.1793.5
37Nonane10.6283058.0015.88410.43257.432165.5746.028
382-methyloctane10.2523270.0015.3429.79258.07165.225.22
393-methyloctane10.4723269.9985.66210.04258.0715.9995.4185.954
404-methyloctane10.3843270.0015.589.9758.07165.3545.858
413-ethylheptane10.56428.12669.9995.86410.21458.0715.9995.0395.49
424-ethylheptane10.49232705.7910.13857.842165.35.902
432,2-dimethylheptane9.33635.99999.9994.5028.75459.21164.3554.916
442,3 -dimethylheptane10.17634.00181.9995.2029.4958.694165.1185.632
452,4-dimethylheptane9.5083481.9994.7288.86658.694164.7185.132
462,5 -dimethylheptane10.1523481.9995.1629.43858.694165.0925.598
472,6- dimethylheptane10.09635.99999.9994.5648.53659.2115.9994.644.98
483,3- dimethylheptane9.46433.999825.1949.32458.694165.0725.632
493,4- dimethylheptane10.3123481.9995.459.67259.07915.9995.2623.939
503,5- dimethylheptane10.2933.99982.0015.4189.62858.978165.245.85
514,4- dimethylheptane9.4336.00199.9994.7448.7359.588164.6845.146
523-ethyl-2-methylhexane10.19833.99981.9995.359.60658.694165.1845.73
534-ethyl-2-methylhexane10.17634.00181.9995.3089.5558.69415.915.1585.698
543-ethyl-3-methylhexane10.2623699.9995.5049.5659.21155.2525.952
552,2,4- trimethylhexane9.1338111.9994.2428.13459.81415.9994.3724.796
562,2,5- trimethylhexane9.0637.9931124.0228.01259.814164.2564.582
572,3,3- trimethylhexane9.337.9991124.4188.3459.81416.1764.4964.97
582,3,4- trimethylhexane10.0963693.9995.0689.18459.30716.0015.025.648
592,3,5- trimethylhexane9.33636.017944.3668.42859.30715.9994.54.918
603,3,4- trimethylhexane10.03644112.0025.0869.05259.814164.9945.67
613,3-diethylpentane10.472361005.7937.73659.2115.554.9033.042
622,2-dimethyl-3-ethylpentane9.337.99959.8144.5286.954112.003164.4764.498
632,3-dimethyl-3-ethylpentane10.06238.00161.8775.1168.43112.00116.0013.9764.376
642,4-dimethyl-3-ethylpentane8.8843157.3685.0888.7874.999144.8185.582
652,2,3,3-tetramethylpentane8.984260.94.247.3514215.9984.1224.672
662,2,3,4- tetramethylpentane9.024060.4083.937.7512416.0014.1684.614
672,2,4,4- tetramethylpentane7.9364260.93.0566.61142164.443.726
682,3,3,4- tetramethylpentane9.15239.24357.2784.147.9612414.9994.9924.74

Regression Models

We have tested the following linear regression model

P=A+B(TI)$$\begin{array}{} \displaystyle P = A + B\left( {TI} \right) \end{array}$$

where P = physical property, TI = topological index.

Using (3.1), we have obtained the following different linear models for each degree based topological index, which are listed below.

Vertex adjacency energyE(G):

bp=51.397+[E(G)]19.268$$\begin{array}{} \displaystyle bp = - 51.397 + \left[ {E\left( G \right)} \right]19.268 \end{array}$$

mv=75.5727+EG10.1894$$\begin{array}{} \displaystyle mv = 75.5727 + \left[ {E\left( G \right)} \right]10.1894 \end{array}$$

mr=13.1325+[E(G)]3.0530$$\begin{array}{} \displaystyle mr = 13.1325 + \left[ {E\left( G \right)} \right]3.0530 \end{array}$$

hv=10.0231+[E(G)]3.3206$$\begin{array}{} \displaystyle hv = 10.0231 + \left[ {E\left( G \right)} \right]3.3206 \end{array}$$

ct=91.9103+[E(G)]23.1023$$\begin{array}{} \displaystyle ct = 91.9103 + \left[ {E\left( G \right)} \right]23.1023 \end{array}$$

cp=28.9043+[E(G)]0.0858$$\begin{array}{} \displaystyle cp = 28.9043 + \left[ {E\left( G \right)} \right]0.0858 \end{array}$$

st=11.0052+[E(G)]1.1474$$\begin{array}{} \displaystyle st = 11.0052 + \left[ {E\left( G \right)} \right]1.1474 \end{array}$$

mp=145.3088+[E(G)]4.4911$$\begin{array}{} \displaystyle mp = - 145.3088 + \left[ {E\left( G \right)} \right]4.4911 \end{array}$$

Vertex Zagreb adjacency energyZ1E(G):

bp=13.358+[Z1E(G)]4.1092$$\begin{array}{} \displaystyle bp = - 13.358 + \left[ {{Z_1}E\left( G \right)} \right]4.1092 \end{array}$$

mv=94.7149+[Z1E(G)]2.2017$$\begin{array}{} \displaystyle mv = 94.7149 + \left[ {{Z_1}E\left( G \right)} \right]2.2017 \end{array}$$

mr=18.6119+[Z1E(G)]0.6778$$\begin{array}{} \displaystyle mr = 18.6119 + \left[ {{Z_1}E\left( G \right)} \right]0.6778 \end{array}$$

hv=14.0714+[Z1E(G)]0.7822$$\begin{array}{} \displaystyle hv = 14.0714 + \left[ {{Z_1}E\left( G \right)} \right]0.7822 \end{array}$$

ct=211.4016+[Z1E(G)]1.0167$$\begin{array}{} \displaystyle ct = 211.4016 + \left[ {{Z_1}E\left( G \right)} \right]1.0167 \end{array}$$

cp=32.606[Z1E(G)]0.0988$$\begin{array}{} \displaystyle cp = 32.606 - \left[ {{Z_1}E\left( G \right)} \right]0.0988 \end{array}$$

st=14.4567+[Z1E(G)]0.2108$$\begin{array}{} \displaystyle st = 14.4567 + \left[ {{Z_1}E\left( G \right)} \right]0.2108 \end{array}$$

mp=139.2218+[Z1E(G)]1.0322$$\begin{array}{} \displaystyle mp = - 139.2218 + \left[ {{Z_1}E\left( G \right)} \right]1.0322 \end{array}$$

Forgotten adjacency energy FE(G):

bp=49.581+[FE(G)]0.829$$\begin{array}{} \displaystyle bp = 49.581 + \left[ {FE\left( G \right)} \right]0.829 \end{array}$$

mv=127.4698+[FE(G)]0.4683$$\begin{array}{} \displaystyle mv = 127.4698 + \left[ {FE\left( G \right)} \right]0.4683 \end{array}$$

mr=29.0759+[FE(G)]0.1390$$\begin{array}{} \displaystyle mr = 29.0759 + \left[ {FE\left( G \right)} \right]0.1390 \end{array}$$

hv=490.0581[FE(G)]5.8696$$\begin{array}{} \displaystyle hv = 490.0581 - \left[ {FE\left( G \right)} \right]5.8696 \end{array}$$

ct=131.1926+[FE(G)]5.1377$$\begin{array}{} \displaystyle ct = 131.1926 + \left[ {FE\left( G \right)} \right]5.1377 \end{array}$$

cp=31.4563[FE(G)]0.0249$$\begin{array}{} \displaystyle cp = 31.4563 - \left[ {FE\left( G \right)} \right]0.0249 \end{array}$$

st=18.4178+[FE(G)]0.034156$$\begin{array}{} \displaystyle st = 18.4178 + \left[ {FE\left( G \right)} \right]0.034156 \end{array}$$

mp=123.9450+[FE(G)]0.2094$$\begin{array}{} \displaystyle mp = - 123.9450 + \left[ {FE\left( G \right)} \right]0.2094 \end{array}$$

harmonic energyHE(G):

bp=29.71+[HE(G)]32.08$$\begin{array}{} \displaystyle bp = - 29.71 + \left[ {HE\left( G \right)} \right]32.08 \end{array}$$

mv=100.054+[HE(G)]13.9260$$\begin{array}{} \displaystyle mv = 100.054 + \left[ {HE\left( G \right)} \right]13.9260 \end{array}$$

mr=20.4799+[HE(G)]4.2371$$\begin{array}{} \displaystyle mr = 20.4799 + \left[ {HE\left( G \right)} \right]4.2371 \end{array}$$

hv=31.9676+[HE(G)]1.4827$$\begin{array}{} \displaystyle hv = 31.9676 + \left[ {HE\left( G \right)} \right]1.4827 \end{array}$$

ct=118.2041+[HE(G)]38.4066$$\begin{array}{} \displaystyle ct = 118.2041 + \left[ {HE\left( G \right)} \right]38.4066 \end{array}$$

cp=27.5611+[HE(G)]0.4722$$\begin{array}{} \displaystyle cp = 27.5611 + \left[ {HE\left( G \right)} \right]0.4722 \end{array}$$

st=12.1028+[HE(G)]1.9642$$\begin{array}{} \displaystyle st = 12.1028 + \left[ {HE\left( G \right)} \right]1.9642 \end{array}$$

mp=137.1760+[HE(G)]6.6745$$\begin{array}{} \displaystyle mp = - 137.1760 + \left[ {HE\left( G \right)} \right]6.6745 \end{array}$$

Geometric-Arithmetic energyGAE(G):

bp=36.99+[GAE(G)]18.984$$\begin{array}{} \displaystyle bp = - 36.99 + \left[ {GAE\left( G \right)} \right]18.984 \end{array}$$

mv=90.04705+[GAE(G)]9.0967$$\begin{array}{} \displaystyle mv = 90.04705 + \left[ {GAE\left( G \right)} \right]9.0967 \end{array}$$

mr=18.0357+[GAE(G)]2.6920$$\begin{array}{} \displaystyle mr = 18.0357 + \left[ {GAE\left( G \right)} \right]2.6920 \end{array}$$

hv=34.6289+[GAE(G)]0.5068$$\begin{array}{} \displaystyle hv = 34.6289 + \left[ {GAE\left( G \right)} \right]0.5068 \end{array}$$

ct=113.4305+[GAE(G)]22.2200$$\begin{array}{} \displaystyle ct = 113.4305 + \left[ {GAE\left( G \right)} \right]22.2200 \end{array}$$

cp=27.3753+[GAE(G)]0.2895$$\begin{array}{} \displaystyle cp = 27.3753 + \left[ {GAE\left( G \right)} \right]0.2895 \end{array}$$

st=12.6092+[GAE(G)]1.0425$$\begin{array}{} \displaystyle st = 12.6092 + \left[ {GAE\left( G \right)} \right]1.0425 \end{array}$$

mp=133.5245+[GAE(G)]3.28$$\begin{array}{} \displaystyle mp = - 133.5245 + \left[ {GAE\left( G \right)} \right]3.28 \end{array}$$

Degree sum energyDSE(G):

bp=59.931+[DSE(G)]0.897$$\begin{array}{} \displaystyle bp = 59.931 + \left[ {DSE\left( G \right)} \right]0.897 \end{array}$$

mv=138.925+[DSE(G)]0.402892$$\begin{array}{} \displaystyle mv = 138.925 + \left[ {DSE\left( G \right)} \right]0.402892 \end{array}$$

mr=31.1877+[DSE(G)]0.1300$$\begin{array}{} \displaystyle mr = 31.1877 + \left[ {DSE\left( G \right)} \right]0.1300 \end{array}$$

hv=32.5560+[DSE(G)]0.1074$$\begin{array}{} \displaystyle hv = 32.5560 + \left[ {DSE\left( G \right)} \right]0.1074 \end{array}$$

ct=219.9439+[DSE(G)]1.1727$$\begin{array}{} \displaystyle ct = 219.9439 + \left[ {DSE\left( G \right)} \right]1.1727 \end{array}$$

cp=32.1358[DSE(G)]0.04447$$\begin{array}{} \displaystyle cp = 32.1358 - \left[ {DSE\left( G \right)} \right]0.04447 \end{array}$$

st=18.4122+[DSE(G)]0.04367$$\begin{array}{} \displaystyle st = 18.4122 + \left[ {DSE\left( G \right)} \right]0.04367 \end{array}$$

mp=122.6285+[DSE(G)]0.2430$$\begin{array}{} \displaystyle mp = - 122.6285 + \left[ {DSE\left( G \right)} \right]0.2430 \end{array}$$

Laplacian energyLE(G):

bp=105.998+[LE(G)]0.795$$\begin{array}{} \displaystyle bp = 105.998 + \left[ {LE\left( G \right)} \right]0.795 \end{array}$$

mv=61.7923+[LE(G)]7.1569$$\begin{array}{} \displaystyle mv = 61.7923 + \left[ {LE\left( G \right)} \right]7.1569 \end{array}$$

mr=9.2778+[LE(G)]2.1462$$\begin{array}{} \displaystyle mr = 9.2778 + \left[ {LE\left( G \right)} \right]2.1462 \end{array}$$

hv=28.1626+[LE(G)]0.7323$$\begin{array}{} \displaystyle hv = 28.1626 + \left[ {LE\left( G \right)} \right]0.7323 \end{array}$$

ct=71.1141+[LE(G)]15.5601$$\begin{array}{} \displaystyle ct = 71.1141 + \left[ {LE\left( G \right)} \right]15.5601 \end{array}$$

cp=31.3140+[LE(G)]0.1221$$\begin{array}{} \displaystyle cp = 31.3140 + \left[ {LE\left( G \right)} \right]0.1221 \end{array}$$

st=10.9055+[LE(G)]0.755115$$\begin{array}{} \displaystyle st = 10.9055 + \left[ {LE\left( G \right)} \right]0.755115 \end{array}$$

mp=146.7815+[LE(G)]2.8215$$\begin{array}{} \displaystyle mp = - 146.7815 + \left[ {LE\left( G \right)} \right]2.8215 \end{array}$$

Sum connectivity energySCE(G):

bp=57.214+[SCE(G)]39.634$$\begin{array}{} \displaystyle bp = - 57.214 + \left[ {SCE\left( G \right)} \right]39.634 \end{array}$$

mv=79.5397+[SCE(G)]19.1531$$\begin{array}{} \displaystyle mv = 79.5397 + \left[ {SCE\left( G \right)} \right]19.1531 \end{array}$$

mr=14.5174+[SCE(G)]5.762765$$\begin{array}{} \displaystyle mr = 14.5174 + \left[ {SCE\left( G \right)} \right]5.762765 \end{array}$$

hv=32.3464+[SCE(G)]1.4459$$\begin{array}{} \displaystyle hv = 32.3464 + \left[ {SCE\left( G \right)} \right]1.4459 \end{array}$$

ct=86.5805+[SCE(G)]47.1321$$\begin{array}{} \displaystyle ct = 86.5805 + \left[ {SCE\left( G \right)} \right]47.1321 \end{array}$$

cp=26.5275+[SCE(G)]0.7319$$\begin{array}{} \displaystyle cp = 26.5275 + \left[ {SCE\left( G \right)} \right]0.7319 \end{array}$$

st=0.7399+[SCE(G)]2.3432$$\begin{array}{} \displaystyle st = 0.7399 + \left[ {SCE\left( G \right)} \right]2.3432 \end{array}$$

mp=144.7607+[SCE(G)]8.7341$$\begin{array}{} \displaystyle \end{array}$$

Vertex Randic energy RE(G):

bp=33.08+[RE(G)]30.673$$\begin{array}{} \displaystyle bp = - 33.08 + \left[ {RE\left( G \right)} \right]30.673 \end{array}$$

mv=4740.533522[RE(G)]960.1500$$\begin{array}{} \displaystyle mv = 4740.533522 - \left[ {RE\left( G \right)} \right]960.1500 \end{array}$$

mr=20.0386+[RE(G)]4.0559$$\begin{array}{} \displaystyle mr = 20.0386 + \left[ {RE\left( G \right)} \right]4.0559 \end{array}$$

hv=24.5656+[RE(G)]2.8952$$\begin{array}{} \displaystyle hv = 24.5656 + \left[ {RE\left( G \right)} \right]2.8952 \end{array}$$

ct=116.5371+[RE(G)]36.2135$$\begin{array}{} \displaystyle ct = 116.5371 + \left[ {RE\left( G \right)} \right]36.2135 \end{array}$$

cp=25.3061+[RE(G)]0.9224$$\begin{array}{} \displaystyle cp = 25.3061 + \left[ {RE\left( G \right)} \right]0.9224 \end{array}$$

st=13.07611+[RE(G)]1.6338$$\begin{array}{} \displaystyle st = 13.07611 + \left[ {RE\left( G \right)} \right]1.6338 \end{array}$$

mp=113.7685+[RE(G)]1.0137$$\begin{array}{} \displaystyle mp = - 113.7685 + \left[ {RE\left( G \right)} \right]1.0137 \end{array}$$

Statical parameters for the linear QSPR model for E(G).

Physical PropertiesNabrsF
Boiling point68-51.39719.2680.95910.54257763.634
Molar volume6575.572710.18940.9057.62675286.565
Molar refraction6513.13253.05300.9122.19071311.823
Heats of vaporization6510.02313.32060.9681.3755935.676
Critical temperature6891.910323.10230.93616.0513496.344
Critical Pressure6828.90430.08580.006565.7690.003
Surface tension6411.00521.14740.8710.9705194.411
Melting point52-145.30884.49110.31625.88865.528

Statical parameters for the linear QSPR model for Z1E(G).

Physical PropertiesNabrsF
Boiling point68-13.3584.10920.83720.46726154.120
Molar volume6594.7149142.2017750.8439.6547155.135
Molar refraction6518.61190.6778o.8732.6070201.663
Heats of vaporization6514.07140.78220.7363.707574.461
Critical temperature68211.40161.01670.85523.6776180.025
Critical Pressure6832.606-0.09880.030119.5705.061
Surface tension6414.45760.21080.7271.3560769.337
Melting point52-137.17626.67450.31225.92245.383

Statical parameters for the linear QSPR model for FE(G).

Physical PropertiesNabrsF
Boiling point6849.5810.8290.52431.828825.020
Molar volume65127.46980.46830.55114.989227.500
Molar refraction6529.07590.13900.554.461627.365
Heats of vaporization65490.0581-5.86960.4104.994812.738
Critical temperature68131.19265.13770.52538.898325.158
Critical Pressure6831.4563-0.02490.024135.5750.038
Surface tension6418.41780.03410.3691.834719.750
Melting point52-123.94500.20940.18726.79871.821

Statical parameters for the linear QSPR model for HE(G).

Physical PropertiesNabrsF
Boiling point68-29.7132.080.82521.1104140.91
Molar volume65100.05413.92600.68213.137554.809
Molar refraction6520.47994.23710.6983.827859.766
Heats of vaporization6531.96761.48270.8562.8351172.070
Critical temperature67118.204138.40660.80826.9494123.915
Critical Pressure6827.56110.47220.01824.66710.022
Surface tension6412.10281.96420.8041.1734113.396
Melting point52-137.17626.67450.23926.49443.018

Statical parameters for the linear QSPR model for GAE(G).

Physical PropertiesNabrsF
Boiling point68-36.9918.9840.87018.4148205.922
Molar volume6590.04109.09670.79111.0019104.98
Molar refraction6518.03572.69200.7873.2997102.211
Heats of vaporization6534.62890.50680.892.4722246.160
Critical temperature68113.430522.22000.83325.3083149.343
Critical Pressure6827.37530.28950.02024.6610.027
Surface tension6412.60921.04250.7671.265488.825
Melting point52-132.523.280.20826.68692.256

Statical parameters for the linear QSPR model for DSE(G).

Physical PropertiesNabrsF
Boiling point6859.9310.8970.59729.984836.560
Molar volume65138.9350.40280.54915.009927.250
Molar refraction6531.18770.13000.5964.288834.79
Heats of vaporization6532.55600.10740.4814.802718.918
Critical temperature68219.94391.17270.63835.115645.347
Critical Pressure6832.1358-0.04440.04581.2160.133
Surface tension6418.41220.04360.5401.661725.459
Melting point52-122.6280.24300.24426.45713.164

Statical parameters for the linear QSPR model for LE (G).

Physical PropertiesNabrsF
Boiling point68105.9980.7950.95111.6074618.389
Molar volume6561.79237.15690.9644.7915822.621
Molar refraction659.27782.14620.9721.26241065.626
Heats of vaporization6528.16260.73230.9242.0990365.873
Critical temperature6871.114115.56010.93516.1580462.301
Critical Pressure6831.3140-0.12210.01424.66980.012
Surface tension6410.90550.75510.8341.0888141.725
Melting point52-146.78152.82150.30126.01514.989

Statical parameters for the linear QSPR model for SCE(G).

Physical PropertiesNabrsF
Boiling point68-57.21439.6350.90715.7086307.683
Molar volume6579.539719.15310.82310.1925132.724
Molar refraction6514.51745.76270.8332.9563142.822
Heats of vaporization6532.34641.444590.9311.9928412.801
Critical temperature6886.580547.3210.88321.4546233.650
Critical Pressure6826.52750.73190.02524.66420.043
Surface tension640.73992.34320.8471.0489157.493
Melting point52-144.76078.73410.27826.20844.181

Statical parameters for the linear QSPR model for R(G).

Physical PropertiesNabrsF
Boiling point68-33.0830.6730.77623.596599.608
Molar volume654740.5335-960.15000.65513.574347.350
Molar refraction6520.03864.05590.6564.033547.567
Heats of vaporization6524.56562.89520.7643.5361988.107
Critical temperature68116.537136.21350.74930.307184.165
Critical Pressure6825.30610.92240.03529.000.083
Surface tension6413.07611.63380.6671.4698749.788
Melting point52-113.76851.01370.03527.26550.061

Discussion and Concluding Remarks

By inspection of the data in Tables 3 to 11, it is possible to draw a number of conclusions for the given energy like invariants.

First, the famous and much studied invariant, energy of a graph found more suitable tool to predict physical property of alkane, especially Boiling points, Molar volume, Surface tension, Critical temperature, Heats of vaporization and Molar refractions of alkanes with correlation coefficient values r = 0.959,0.905,0.871,0.968 and 0.912 respectively.

Motivated by vertex Zagreb energy. Here we introduced a new topological invariant namely, vertex Zagreb adjacency energy. The QSPR study of vertex Zagreb energy reveals that Z1E(G) can be useful in predicting the Boiling points, Critical temperatures, Molar volumes and Molar refraction of alkanes also from Table 4,we can see that the correlation coefficient value of Z1E(G) with physical properties of alkanes lies between 0.030 to 0.873.

In addition by using the recently advocated idea of using Forgotten index in QSPR studies, we introduced Forgotten adjacency energy. The QSPR study of FE(G) shows that the idea of using FE(G) in QSPR study does not make sense. Since the correlation coefficient values FE(G) with physical properties of alkanes lies between 0.024 to 0.551.

The harmonic index did not attract anybody’s attention, especially, not of chemists. No chemical applications of the harmonic index were reported so far, but knowing the present situation in the mathematical chemistry. We here explore the chemical applications of harmonic index. The Table 6 reveals that harmonic energy is also useful tool in predicting the Boiling point, Heats of vaporization, Surface tensions and Critical temperature of alkanes with correlation coefficient values r = 0.825,0.856,0.804 and 0.808, respectively.

The QSPR study of Geometric-arithmetic energy reveals that the predicting power of GA- energy for the physical properties Boiling points, Heats of vaporization and Critical temperatures of alkanes with correlation coefficient values r = 0.870,0.89 and 0.833 respectively.

In addition the results for degree sum energy revealed that the recent advocated idea of using degree sum energy doesn’t pass the test.

The so called Laplacian energy shows remarkably good correlation with the Boiling points, Molar volumes, Molar refractions, Heats of vaporization, Surface tensions and Critical temperatures of alkanes with correlation coefficient values r = 0.951,0.964,0.972,0.924,0.83 and r = 0.935 respectively. Further, the correlation coefficient values lies between 0.014 to 0.972. In fact the predicting power of Laplacian energy to the critical pressures of alkanes is almost nil.

The sum connectivity energy shows similar correlation properties [ ]. The QSPR study in Table 10 reveals that the predicting power of sum connectivity energy is remarkably good. Infarct the sum connectivity energy can be use as a tool to predict the Heats of vaporization of alkanes. The correlation coefficient value of sum connectivity energy with the Heats of vaporization of alkanes is 0.931. Further the range of correlation coefficient value is 0.025 to 0.931. In fact, the predicting power of sum connectivity energy with Critical pressures of alkanes is almost nil.

The QSPR study of Vertex randic energy does not pass the test.

From practical point of view, topological indices for which the absolute values of correlation coefficient are less than 0.8 can be characterized as useless. Thus the QSPR study of 9 topological indices with physical properties of 68 alkanes helps us to characterize useful topological indices with absolute value of correlation coefficient lies between 0.8 to 0.972.

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