ACGC plus: a general group contribution framework for diverse properties prediction

Abstract

Atomic connectivity group contribution (ACGC) is a method developed by atomic adjacent group (AAG), shape factors and atomic connectivity factors (ACFs) for property prediction. As a crucial parameter for dealing with the challenge of accurately predicting properties of isomers, ACFs are defined for describing the global position of each group in a molecule. In this work, ACFs plus (ACFs+) is proposed to describe the local position of a group by considering the contribution of the core atom in AAG and nearby atoms. As such, ACGC plus (ACGC+) models are developed with ACFs+ to predict key phase transition properties of organic compounds (i.e., Δ_fusS, Δ_vapH°, Δ_subH°, Δ_fusH, T_b, T_m, T_c, P_c, and V_c). Both predictability and robustness are rigorously validated using external validation and cross-validation. The R²_test values for phase transition entropy and enthalpy range from 0.906 to 0.992, the R²_test values for critical properties are greater than 0.989, and the R²_test values for T_b and T_m are 0.979 and 0.845, respectively, which indicate high predictability of ACGC + models. The R² values for all properties are close to the R²_train values, which further validates the stability of the ACGC+ model. Furthermore, the mean absolute errors of the ACGC+ models decreased by 1.44%–7.91% compared to the ACGC models. These results demonstrate that the ACGC+ method provides high accuracy in predicting the properties of phase transitions.

Graphical abstract

Keywords

group contribution method / atomic connectivity group / shape factor / phase transition properties / thermodynamic properties

References

[1] Fonseca L A A P , Cremasco M A . Group contribution methods to predict enthalpy of vaporization of aromatic and terpene ketones at 298.15 K. Fluid Phase Equilibria, 2021, 538: 113009

[2] Plehiers P P , Lengyel I , West D H , Marin G B , Stevens C V , Van Geem K M . Fast estimation of standard enthalpy of formation with chemical accuracy by artificial neural network correction of low-level-of-theory ab initio calculations. Chemical Engineering Journal, 2021, 426: 131304

[3] Padmakumar G , Velusamy K , Prasad B V S S S , Lijukrishnan P , Pandey G K , Selvaraj P . Thermal-hydraulic effects of inserts in a fast reactor fuel bundle. Nuclear Engineering and Design, 2018, 332: 226–237

[4] Lewis G N , Gibson G E . The entropy of the elements and the third law of thermodynamics. Journal of the American Chemical Society, 1917, 39(12): 2554–2581

[5] Long E A , Kemp J D . The entropy of deuterium oxide and the third law of thermodynamics. Heat capacity of deuterium oxide from 15 to 298 K. The melting point and heat of fusion. Journal of the American Chemical Society, 1936, 58(10): 1829–1834

[6] Mandelkern L , Quinn F A , Roberts D E . Thermodynamics of crystallization in high polymers. Gutta-percha. Rubber Chemistry and Technology, 1956, 29(4): 1181–1194

[7] Pedersen U R , Costigliola L , Bailey N P , Schrøder T B , Dyre J C . Thermodynamics of freezing and melting. Nature Communications, 2016, 7(1): 12386

[8] Keshavarz M H , Pouretedal H R . A new simple approach to predict entropy of fusion of nitroaromatic compounds. Fluid Phase Equilibria, 2010, 298(1): 24–32

[9] Fan C , Pashley R M . Determining the enthalpy of vaporization of salt solutions using the cooling effect of a bubble column evaporator. Journal of Chemical Education, 2016, 93(9): 1642–1646

[10] Kikic I , Vetere A . Evaluation of several literature equations to predict the vaporization enthalpies at the normal boiling point. Fluid Phase Equilibria, 2011, 309(2): 151–154

[11] Fan C , Shahid M , Pashley R M . The energy balance within a bubble column evaporator. Heat and Mass Transfer, 2018, 54(5): 1313–1321

[12] de Kruif C G , Voogd J , Offringa J C A . Enthalpies of sublimation and vapour pressures of 14 amino acids and peptides. Journal of Chemical Thermodynamics, 1979, 11(7): 651–656

[13] Ribeiro da Silva M A V , Matos M A R , Amaral L M P F . Enthalpies of combustion, vapour pressures, and enthalpies of sublimation of 5-, 6-, and 8-nitroquinoline and 8-nitroquinaldine. Journal of Chemical Thermodynamics, 1997, 29(3): 295–303

[14] Abraham M H , Acree W E Jr . Estimation of enthalpies of sublimation of organic, organometallic, and inorganic compounds. Fluid Phase Equilibria, 2020, 515: 112575

[15] Gharagheizi F , Ilani-Kashkouli P , Acree W E Jr , Mohammadi A H , Ramjugernath D . A group contribution model for determining the sublimation enthalpy of organic compounds at the standard reference temperature of 298 K. Fluid Phase Equilibria, 2013, 354: 265–285

[16] Chickos J S , Acree W E Jr . Enthalpies of vaporization of organic and organometallic compounds, 1880–2002. Journal of Physical and Chemical Reference Data, 2003, 32(2): 519–878

[17] Tao M , Wang Z , Gong J , Hao H , Wang J . Determination of the solubility, dissolution enthalpy, and entropy of pioglitazone hydrochloride (form II) in different pure solvents. Industrial & Engineering Chemistry Research, 2013, 52(8): 3036–3041

[18] Goll E S , Jurs P C . Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model. Journal of Chemical Information and Computer Sciences, 1999, 39(6): 974–983

[19] Sprakel L M J , Kamphuis P , Nikolova A L , Keijsper D J , Schuur B . Solvent selection for extractive distillation processes to separate close-boiling polar systems. Chemical Engineering Research & Design, 2019, 144: 123–134

[20] Sattler K, Feindt H J. Thermal Separation Processes: Principles and Design. Weinheim: VCH Verlagsgesellschaft mbH, 1995

[21] Jaksland C , Hytoft G , Gani R . Computer-aided process design and optimization with novel separation units. Applied Thermal Engineering, 1997, 17(8): 973–980

[22] Karthikeyan M , Glen R C , Bender A . General melting point prediction based on a diverse compound data set and artificial neural networks. Journal of Chemical Information and Modeling, 2005, 45(3): 581–590

[23] Chester T L . Determination of pressure-temperature coordinates of liquid-vapor critical loci by supercritical fluid flow injection analysis. Journal of Chromatography A, 2004, 1037(1): 393–403

[24] Eckert C A , Van Alsten J G , Stoicos T . Supercritical fluid processing. Environmental Science & Technology, 1986, 20(4): 319–325

[25] Rillo C , Gabal M , Lozano M P , Sesé J , Spagna S , Diederichs J , Sager R , Chialvo C , Terry J , Rayner G . et al. Enhancement of the liquefaction rate in small-scale helium liquefiers working near and above the critical point. Physical Review Applied, 2015, 3(5): 051001

[26] Turner B E , Costello C L , Jurs P C . Prediction of critical temperatures and pressures of industrially important organic compounds from molecular structure. Journal of Chemical Information and Computer Sciences, 1998, 38(4): 639–645

[27] Tang B , Yao X , Dong X , Zhao Y , Gong M . Prediction of critical temperature and critical volume of mixtures by a new group contribution method. Industrial & Engineering Chemistry Research, 2025, 64(1): 901–911

[28] Zou H , Zhao H , Lu M , Wang J , Deng Z , Wang J . Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration. Nature Communications, 2025, 16(1): 203

[29] Schreiber R , Vogt C , Werther J , Brunner G . Fluidized bed coating at supercritical fluid conditions. Journal of Supercritical Fluids, 2002, 24(2): 137–151

[30] Qiu Y , Chen G , Liu Q , Qi Z , Zhu K , Song Z . Bridging machine learning and COSMO-SAC for accurate prediction of infinite dilute activity coefficients of binary mixtures. ENGINEERING Chemical Engineering, 2026, 20(1): 4

[31] Pankow J F , Asher W E . SIMPOL.1: a simple group contribution method for predicting vapor pressures and enthalpies of vaporization of multifunctional organic compounds. Atmospheric Chemistry and Physics, 2008, 8(10): 2773–2796

[32] Carreón-Calderón B , Uribe-Vargas V , Ramírez-de-Santiago M , Ramírez-Jaramillo E . Thermodynamic characterization of heavy petroleum fluids using group contribution methods. Industrial & Engineering Chemistry Research, 2014, 53(13): 5598–5607

[33] Carreón-Calderón B , Uribe-Vargas V , Ramírez-Jaramillo E , Ramírez-de-Santiago M . Thermodynamic characterization of undefined petroleum fractions using group contribution methods. Industrial & Engineering Chemistry Research, 2012, 51(43): 14188–14198

[34] Mickoleit E , Jäger A , Grau Turuelo C , Thol M , Bell I H , Breitkopf C . Group contribution method for the residual entropy scaling model for viscosities of branched alkanes. International Journal of Thermophysics, 2023, 44(12): 176

[35] Hwang S Y , Kang J W . Group contribution-based graph convolution network: pure property estimation model. International Journal of Thermophysics, 2022, 43(9): 136

[36] Zhou C , Zhang X , Qi M , Zhao W , Xiang S . Evaluation and modification of group contribution methods for critical properties of organic compounds. Fluid Phase Equilibria, 2024, 579: 114023

[37] Pérez-Correa I , Giunta P D , Francesconi J A , Mariño F J . Artificial neural network for the prediction of physical properties of organic compounds based on the group contribution method. Canadian Journal of Chemical Engineering, 2023, 101(8): 4771–4783

[38] Aouichaoui A R N , Fan F , Mansouri S S , Abildskov J , Sin G . Combining group-contribution concept and graph neural networks toward interpretable molecular property models. Journal of Chemical Information and Modeling, 2023, 63(3): 725–744

[39] Chen J , Qiu Y , Chen W , Cheng H , Duan X , Qi Z , Song Z . FusNet: unlocking molecular fusion properties through machine learning. Frontiers of Chemical Science and Engineering, 2025, 19(9): 81

[40] Ouyang B , Zhang D , Chen Z , Wen Z Q , Luo Z H . Enhancing quantitative structure-property relationship models by integrating complete molecular structure with deep learning. ENGINEERING Chemical Engineering, 2026, 20(3): 16

[41] Joback K G. A unified approach to physical property estimation using multivariate statistical techniques. Dissertation for the Master Degree. Cambridge: Massachusetts Institute of Technology, 1984

[42] Constantinou L , Gani R . New group contribution method for estimating properties of pure compounds. AIChE Journal, 1994, 40(10): 1697–1710

[43] Marrero J , Gani R . Group-contribution based estimation of pure component properties. Fluid Phase Equilibria, 2001, 183: 183–208

[44] Hukkerikar A S , Sarup B , Ten Kate A , Abildskov J , Sin G , Gani R . Group-contribution+ (GC+) based estimation of properties of pure components: improved property estimation and uncertainty analysis. Fluid Phase Equilibria, 2012, 321: 25–43

[45] Sun Y , Sahinidis N V . A new functional group selection method for group contribution models and its application in the design of electronics cooling fluids. Industrial & Engineering Chemistry Research, 2021, 60(19): 7291–7300

[46] Yan F , Cao D , Feng X , Xiong J , Wang Q , Jia Q , Xia S . Atomic connectivity group contribution (ACGC) method for critical properties prediction. Chemical Engineering Science, 2023, 280: 118990

[47] Cao D , Feng X , Wang Q , Jia Q , Xia S , Yan F . Atomic connectivity group contribution method for predicting the phase transition properties of enthalpy and entropy. Industrial & Engineering Chemistry Research, 2023, 62(34): 13701–13709

[48] Feng X , Cao D , Wang Q , Jia Q , Yan F . Atomic connectivity group contribution method for predicting the boiling and melting points of organic compounds. Chemical Engineering Science, 2023, 282: 119357

[49] Xue J , Jia Q , Wang Q , Yan F . A spatial group contribution method for predicting the phase transition properties of organic compounds. Industrial & Engineering Chemistry Research, 2025, 64(29): 14689–14699

[50] Xue J , Feng X , Jia Q , Wang Q , Yan F . A universal spatial group contribution method by 3D-structures for predicting the thermodynamic properties. AIChE Journal, 2025, 71(7): e18823

[51] Huber M L , Lemmon E W , Bell I H , McLinden M O . The NIST REFPROP database for highly accurate properties of industrially important fluids. Industrial & Engineering Chemistry Research, 2022, 61(42): 15449–15472

[52] Gramatica P . Principles of QSAR models validation: internal and external. QSAR & Combinatorial Science, 2007, 26(5): 694–701

[53] Rücker C , Rücker G , Meringer M . Y-randomization and its variants in QSPR/QSAR. Journal of Chemical Information and Modeling, 2007, 47(6): 2345–2357

[54] Montesinos López O A, Montesinos López A, Crossa J. Multivariate Statistical Machine Learning Methods for Genomic Prediction. Cham: Springer, 2022: 109–139

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