Machine learning for molecular thermodynamics

doi:10.1016/j.cjche.2020.10.044

Chinese Journal of Chemical Engineering ›› 2021, Vol. 29 ›› Issue (3): 227-239.DOI: 10.1016/j.cjche.2020.10.044

Machine learning for molecular thermodynamics

Jiaqi Ding¹, Nan Xu¹, Manh Tien Nguyen², Qi Qiao², Yao Shi^1,3, Yi He^1,4, Qing Shao²

1 College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China;
2 Chemical and Materials Engineering Department, University of Kentucky, Lexington, KY 40506, USA;
3 Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China;
4 Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA

Received:2020-08-21 Revised:2020-10-02 Online:2021-05-13 Published:2021-03-28
Contact: Yi He, Qing Shao
Supported by:
Jiaqi Ding, Nan Xu, Dr. Yao Shi and Dr. Yi He acknowledge financial supports from the National Natural Science Foundation of China (21676245 and 51933009), the National Key Research and Development Program of China (2017YFB0702502), and the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (2019R01006). Manh Tien Nguyen, Dr. Qi Qiao and Dr. Qing Shao thank the financial support provided by the Startup Funds of the University of Kentucky.

Machine learning for molecular thermodynamics

Jiaqi Ding¹, Nan Xu¹, Manh Tien Nguyen², Qi Qiao², Yao Shi^1,3, Yi He^1,4, Qing Shao²

1 College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China;
2 Chemical and Materials Engineering Department, University of Kentucky, Lexington, KY 40506, USA;
3 Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China;
4 Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA

通讯作者: Yi He, Qing Shao
基金资助:
Jiaqi Ding, Nan Xu, Dr. Yao Shi and Dr. Yi He acknowledge financial supports from the National Natural Science Foundation of China (21676245 and 51933009), the National Key Research and Development Program of China (2017YFB0702502), and the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (2019R01006). Manh Tien Nguyen, Dr. Qi Qiao and Dr. Qing Shao thank the financial support provided by the Startup Funds of the University of Kentucky.

Abstract

Abstract: Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes. It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems. Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions. This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples. The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data. The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials. The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations. The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering. We will also discuss the perspective of the broad applications of machine learning in chemical engineering.

Key words: Machine learning, Thermodynamic properties, Molecular engineering, Molecular simulation, Force field

摘要： Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes. It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems. Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions. This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples. The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data. The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials. The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations. The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering. We will also discuss the perspective of the broad applications of machine learning in chemical engineering.

关键词: Machine learning, Thermodynamic properties, Molecular engineering, Molecular simulation, Force field

Jiaqi Ding, Nan Xu, Manh Tien Nguyen, Qi Qiao, Yao Shi, Yi He, Qing Shao. Machine learning for molecular thermodynamics[J]. Chinese Journal of Chemical Engineering, 2021, 29(3): 227-239.

Jiaqi Ding, Nan Xu, Manh Tien Nguyen, Qi Qiao, Yao Shi, Yi He, Qing Shao. Machine learning for molecular thermodynamics[J]. 中国化学工程学报, 2021, 29(3): 227-239.

References

[1] P. Hosseinifar, S. Jamshidi, Development of a new generalized correlation to characterize physical properties of pure components and petroleum fractions, Fluid Phase Equilib. 363(2014) 189-198.
[2] Z.A. Makrodimitri, A. Heller, T.M. Koller, M.H. Rausch, M.S.H. Fleys, A.N.R. Bos, G.P. van der Laan, A.P. Fröba, I.G. Economou, Viscosity of heavy n-alkanes and diffusion of gases therein based on molecular dynamics simulations and empirical correlations, J. Chem. Thermodyn. 91(2015) 101-107.
[3] H. Miyamoto, K. Watanabe, Thermodynamic property model for fluid-phase n-butane, Int. J. Thermophys. 22(2) (2001) 459-475.
[4] D. Liu, H. Li, M.S. Noon, D.L. Tomasko, CO₂-induced PMMA swelling and multiple thermodynamic property analysis using Sanchez-Lacombe EOS, Macromolecules 38(10) (2005) 4416-4424.
[5] L.H. Wang, S.T. Lin, A predictive method for the solubility of drug in supercritical carbon dioxide, J. Supercrit. Fluids. 85(2014) 81-88.
[6] E. Voutsas, V. Louli, C. Boukouvalas, K. Magoulas, D. Tassios, Thermodynamic property calculations with the universal mixing rule for EoS/GE models: Results with the Peng-Robinson EoS and a UNIFAC model, Fluid Phase Equilib. 241(1-2) (2006) 216-228.
[7] J.M. Prausnitz, Thermodynamic and transport properties of coal liquids, Fluid Phase Equilib. 35(1-3) (1987) 316-318.
[8] D.A. Sverjensky, E.L. Shock, H.C. Helgeson, Prediction of the thermodynamic properties of aqueous metal complexes to 1000℃ and 5 kb, Geochim. Cosmochim. Acta 61(7) (1997) 1359-1412.
[9] T.A. Pascal, D. Schärf, Y. Jung, T.D. Kühne, On the absolute thermodynamics of water from computer simulations: A comparison of first-principles molecular dynamics, reactive and empirical force fields, J. Chem. Phys. 137(24) (2012).
[10] T.D. Iordanov, G.K. Schenter, B.C. Garrett, Sensitivity analysis of thermodynamic properties of liquid water: A general approach to improve empirical potential, J. Phys. Chem. A 110(2) (2006) 762-771.
[11] E.W. Lemmon, R.T. Jacobsen, Generalized model for the thermodynamic properties of mixtures, Int. J. Thermophys. 20(3) (1999) 825-835.
[12] A. Serra, P. Galdi, R. Tagliaferri, Machine learning for bioinformatics and neuroimaging, Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(5) (2018).
[13] R.K. Vasudevan, K. Choudhary, A. Mehta, R. Smith, G. Kusne, F. Tavazza, L. Vlcek, M. Ziatdinov, S.V. Kalinin, J. Hattrick-Simpers, Materials science in the artificial intelligence age: High-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics, MRS Commun. 9(3) (2019) 821-838.
[14] Z. Chen, X. Huang, End-To-end learning for lane keeping of self-driving cars, in: IEEE Intell. Veh. Symp. Proc. (Iv), 1856-1869(2017).
[15] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Mastering the game of Go with deep neural networks and tree search, Nature 529(7587) (2016) 484-489.
[16] D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. Van Den Driessche, T. Graepel, D. Hassabis, Mastering the game of Go without human knowledge, Nature 550(7676) (2017) 354-359.
[17] J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, B. Li, A. Madabhushi, P. Shah, M. Spitzer, S. Zhao, Applications of machine learning in drug discovery and development, Nat. Rev. Drug Discov. 18(6) (2019) 463-477.
[18] B. Louis, V.K. Agrawal, P.V. Khadikar, Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses, Eur. J. Med. Chem. 45(9) (2010) 4018-4025.
[19] S. Basith, B. Manavalan, T. Hwan Shin, G. Lee, Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening, Med. Res. Rev. 40(4) (2020) 1276-1314.
[20] Y. Jing, Y. Bian, Z. Hu, L. Wang, X.Q.S. Xie, Deep learning for drug design: An artificial intelligence paradigm for drug discovery in the big data era, AAPS J. 20(3) (2018) 1-10.
[21] L. Zhang, J. Tan, D. Han, H. Zhu, From machine learning to deep learning: progress in machine intelligence for rational drug discovery, Drug Discov. Today 22(11) (2017) 1680-1685.
[22] F. Zhong, J. Xing, X. Li, X. Liu, Z. Fu, Z. Xiong, D. Lu, X. Wu, J. Zhao, X. Tan, F. Li, X. Luo, Z. Li, K. Chen, M. Zheng, H. Jiang, Artificial intelligence in drug design, Sci. China Life Sci. 61(10) (2018) 1191-1204.
[23] J. Graser, S.K. Kauwe, T.D. Sparks, Machine learning and energy minimization approaches for crystal structure predictions: A review and new horizons, Chem. Mater. 30(11) (2018) 3601-3612.
[24] P.S. Gromski, A.B. Henson, J.M. Granda, L. Cronin, How to explore chemical space using algorithms and automation, Nat. Rev. Chem. 3(2) (2019) 119-128.
[25] B. Sanchez-Lengeling, A. Aspuru-Guzik, Inverse molecular design using machine learning: Generative models for matter engineering, Science 361(6400) (2018) 360-365.
[26] J. Timoshenko, D. Lu, Y. Lin, A.I. Frenkel, Supervised machine-learning-based determination of three-dimensional structure of metallic nanoparticles, J. Phys. Chem. Lett. 8(20) (2017) 5091-5098.
[27] A. Seko, T. Maekawa, K. Tsuda, I. Tanaka, Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single-and binary-component solids, Phys. Rev. B -Condens. Matter Mater. Phys. 89(5) (2014), 054303.
[28] X. Wan, W. Feng, Y. Wang, H. Wang, X. Zhang, C. Deng, N. Yang, Materials discovery and properties prediction in thermal transport via materials informatics: A mini review, Nano Lett. 19(6) (2019) 3387-3395.
[29] L. Ward, C. Wolverton, Atomistic calculations and materials informatics: A review, Curr. Opin. Solid State Mater. Sci. 21(3) (2017) 167-176.
[30] J. Wei, X. Chu, X. Sun, K. Xu, H. Deng, J. Chen, Z. Wei, M. Lei, Machine learning in materials science, InfoMat 1(3) (2019) 338-358.
[31] N. Meftahi, M.L. Walker, M. Enciso, B.J. Smith, Predicting the enthalpy and Gibbs energy of sublimation by QSPR modeling, Sci. Rep. 8(1) (2018) 1-9.
[32] A. Varamesh, A. Hemmati-Sarapardeh, M.K. Moraveji, A.H. Mohammadi, Generalized models for predicting the critical properties of pure chemical compounds, J. Mol. Liq. 240(2017) 777-793.
[33] Z. Zhang, H. Li, H. Chang, Z. Pan, X. Luo, Machine learning predictive framework for CO₂ thermodynamic properties in solution, J. CO₂ Util. 26(2018) 152-159.
[34] J. Wang, X. Yang, Z. Zeng, X. Zhang, X. Zhao, Z. Wang, New methods for prediction of elastic constants based on density functional theory combined with machine learning, Comput. Mater. Sci. 138(2017) 135-148.
[35] W.C. Herndon, P.C. Nowak, D.A. Connor, P. Lin, Empirical model calculations for thermodynamic and structural properties of condensed polycyclic aromatic hydrocarbons, J. Am. Chem. Soc. 114(1) (1992) 41-47.
[36] N.S.H. Narayana Moorthy, S.A. Martins, S.F. Sousa, M.J. Ramos, P.A. Fernandes, Classification study of solvation free energies of organic molecules using machine learning techniques, RSC Adv. 4(106) (2014) 61624-61630.
[37] V.E. Kuz’min, P.G. Polishchuk, A.G. Artemenko, S.A. Andronati, Interpretation of QSAR models based on random forest methods, Mol. Inform. 30(6-7) (2011) 593-603.
[38] D.S. Palmer, N.M. O’Boyle, R.C. Glen, J.B.O. Mitchell, Random forest models to predict aqueous solubility, J. Chem. Inf. Model. 47(1) (2007) 150-158.
[39] R. Ramakrishnan, M. Hartmann, E. Tapavicza, O.A. Von Lilienfeld, Electronic spectra from TDDFT and machine learning in chemical space, J. Chem. Phys. 143(8) (2015), 084111.
[40] L. Ruddigkeit, R. van Deursen, L.C. Blum, J.L. Reymond, Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17, J. Chem. Inf. Model. 52(11) (2012) 2864-2875.
[41] J.S. Delaney, ESOL: Estimating aqueous solubility directly from molecular structure, J. Chem. Inf. Comput. Sci. 44(3) (2004) 1000-1005.
[42] R. Ramakrishnan, P.O. Dral, M. Rupp, O.A. Von Lilienfeld, Quantum chemistry structures and properties of 134 kilo molecules, Sci. Data 1(1) (2014) 140022.
[43] W.R. Smith, I. Nezbeda, J. Kolafa, F. Moučka, Recent progress in the molecular simulation of thermodynamic properties of aqueous electrolyte solutions, Fluid Phase Equilib. 466(2018) 19-30.
[44] S. Deublein, B. Eckl, J. Stoll, S.V. Lishchuk, G. Guevara-Carrion, C.W. Glass, T. Merker, M. Bernreuther, H. Hasse, J. Vrabec, Ms2: A molecular simulation tool for thermodynamic properties, Comput. Phys. Commun. 182(11) (2011) 2350-2367.
[45] T. Méndez-Morales, J. Carrete, O. Cabeza, L.J. Gallego, L.M. Varela, Molecular dynamics simulations of the structural and thermodynamic properties of imidazolium-based ionic liquid mixtures, J. Phys. Chem. B 115(38) (2011) 11170-11182.
[46] Z. Gong, Y. Wu, L. Wu, H. Sun, Predicting thermodynamic properties of alkanes by high-throughput force field simulation and machine learning, J. Chem. Inf. Model. 58(12) (2018) 2502-2516.
[47] A.O. Oliynyk, E. Antono, T.D. Sparks, L. Ghadbeigi, M.W. Gaultois, B. Meredig, A. Mar, High-throughput machine-learning-driven synthesis of full-Heusler compounds, Chem. Mater. 28(20) (2016) 7324-7331.
[48] C. Zhang, X. Jiang, R. Zhang, X. Wang, H. Yin, X. Qu, Z.K. Liu, High-throughput thermodynamic calculations of phase equilibria in solidified 6016 Al-alloys, Comput. Mater. Sci. 167(2019) 19-24.
[49] S. Kirklin, B. Meredig, C. Wolverton, High-throughput computational screening of new Li-Ion battery anode materials, Adv. Energy Mater. 3(2) (2013) 252-262.
[50] T. Zhang, Z. Cai, S. Chen, Chemical trends in the thermodynamic stability and band gaps of 980 halide double perovskites: A high-throughput firstprinciples study, ACS Appl. Mater. Interfaces 12(18) (2020) 20680-20690.
[51] A.P. Bartók, M.C. Payne, R. Kondor, G. Csányi, Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons, Phys. Rev. Lett. 104(13) (2010) 136403.
[52] M.A. Wood, A.P. Thompson, Extending the accuracy of the SNAP interatomic potential form, J. Chem. Phys. 148(24) (2018) 241721.
[53] V. Botu, R. Ramprasad, Learning scheme to predict atomic forces and accelerate materials simulations, Phys. Rev. B -Condens. Matter Mater. Phys. 92(9) (2015) 094306.
[54] H. Wang, L. Zhang, J. Han, W.E. DeePMD-kit, A deep learning package for many-body potential energy representation and molecular dynamics, Comput. Phys. Commun. 228(2018) 178-184.
[55] A. Khorshidi, A.A. Peterson, Amp: A modular approach to machine learning in atomistic simulations, Comput. Phys. Commun. 207(2016) 310-324.
[56] W.L. Jorgensen, D.S. Maxwell, J. Tirado-Rives, Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids, J. Am. Chem. Soc. 118(45) (1996) 11225-11236.
[57] M.J. Robertson, J. Tirado-Rives, W.L. Jorgensen, Improved peptide and protein torsional energetics with the OPLS-AA force field, J. Chem. Theory Comput. 11(7) (2015) 3499-3509.
[58] B.R. Brooks, R.E. Bruccoleri, B.D. Olafson, D.J. States, S. Swaminathan, M. Karplus, CHARMM: A program for macromolecular energy, minimization, and dynamics calculations, J. Comput. Chem. 4(2) (1983) 187-217.
[59] C.I. Bayly, K.M. Merz, D.M. Ferguson, W.D. Cornell, T. Fox, J.W. Caldwell, P.A. Kollman, P. Cieplak, I.R. Gould, D.C. Spellmeyer, A second generation force field for the simulation of proteins, nucleic acids, and organic molecules, J. Am. Chem. Soc. 117(19) (1995) 5179-5197.
[60] D.C. Elton, Z. Boukouvalas, M.D. Fuge, P.W. Chung, Deep learning for molecular design -A review of the state of the art, Mol. Syst. Des. Eng. 4(4) (2019) 828-849.
[61] R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, C. Kim, Machine learning in materials informatics: recent applications and prospects, Npj Comput. Mater. 3(1) (2017) 54.
[62] K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Machine learning for molecular and materials science, Nature 559(7715) (2018) 547-555.
[63] V.L. Deringer, M.A. Caro, G. Csányi, Machine learning interatomic potentials as emerging tools for materials science, Adv. Mater. 31(46) (2019) 1-16.
[64] A.P. Bartók, S. De, C. Poelking, N. Bernstein, J.R. Kermode, G. Csányi, M. Ceriotti, Machine learning unifies the modeling of materials and molecules, Sci. Adv. 3(12) (2017) 1-9.
[65] F. Noé, A. Tkatchenko, K.R. Müller, C. Clementi, Machine learning for molecular simulation, Annu. Rev. Phys. Chem. 71(2020) 361-390.
[66] Y. Liu, T. Zhao, W. Ju, S. Shi, Materials discovery and design using machine learning, J. Mater. 3(3) (2017) 159-177.
[67] J. Schmidt, M.R.G. Marques, S. Botti, M.A.L. Marques, Recent advances and applications of machine learning in solid-state materials science, Npj Comput. Mater. 5(1) (2019) 1-36.
[68] L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, M. Scheffler, Big data of materials science: critical role of the descriptor, Phys. Rev. Lett. 114(10) (2015) 105503.
[69] S. Shi, J. Gao, Y. Liu, Y. Zhao, Q. Wu, W. Ju, C. Ouyang, R. Xiao, Multi-scale computation methods: Their applications in lithium-ion battery research and development, Chinese Phys. B. 25(1) (2015) 18212.
[70] AIChE, Design Institute for Physical Property Research, DIPPR Project 801, 2005, https://www.aiche.org/dippr/projects/801.
[71] P.J. Linstrom, Nist standard reference database number 69, NIST Chemistry WebBook (2003). https://webbook.nist.gov/chemistry.
[72] Z. Obermeyer, E.J. Emanuel, Predicting the future—big data, machine learning, and clinical medicine, N. Engl. J. Med. 375(13) (2016) 1216.
[73] M.M. Hamad, A.A. Jihad, An enhanced technique to clean data in the data warehouse, in: 2011 Dev. E-Systems Eng., IEEE, 306-311(2011).
[74] Z. Gong, P. Zhong, W. Hu, Diversity in machine learning, IEEE Access. 7(2019) 64323-64350.
[75] R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley, 2000.
[76] Z. Wang, Y. Su, S. Jin, W. Shen, J. Ren, X. Zhang, J.H. Clark, A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties, Green Chem. 22(12) (2020) 3867-3876.
[77] D.A. Saldana, L. Starck, P. Mougin, B. Rousseau, L. Pidol, N. Jeuland, B. Creton, Flash point and cetane number predictions for fuel compounds using quantitative structure property relationship (QSPR) methods, Energy Fuels 25(9) (2011) 3900-3908.
[78] Y. Pan, J. Jiang, R. Wang, H. Cao, J. Zhao, Quantitive structure -Property relationship studies for predicting flash points of organic compounds using support vector machines, QSAR Comb. Sci. 27(8) (2008) 1013-1019.
[79] B.J. Frey, Pattern Classification, in: Graph. Model. Mach. Learn. Digit. Commun., The MIT Press, 1998.
[80] A. Seko, A. Togo, I. Tanaka, Descriptors for Machine Learning of Materials Data, in: Nanoinformatics, Springer Singapore, Singapore, 3-23(2018).
[81] I. Guyon, A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res. 3(2003) 1157-1182.
[82] L. Yu, H. Liu, Efficient feature selection via analysis of relevance and redundancy, J. Mach. Learn. Res. 5(Oct) (2004) 1205-1224.
[83] G. Chandrashekar, F. Sahin, A survey on feature selection methods, Comput. Electr. Eng. 40(1) (2014) 16-28.
[84] M.B. Kursa, W.R. Rudnicki, Feature selection with the boruta package, J. Stat. Softw. 36(11) (2010) 1-13.
[85] J. Cai, J. Luo, S. Wang, S. Yang, Feature selection in machine learning: A new perspective, Neurocomputing 300(2018) 70-79.
[86] Y. Su, Z. Wang, S. Jin, W. Shen, J. Ren, M.R. Eden, An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures, AIChE J. 65(9) (2019) 1-11.
[87] E. Asgari, M.R.K. Mofrad, Continuous distributed representation of biological sequences for deep proteomics and genomics, PLoS One 10(11) (2015) e0141287.
[88] M. Olivecrona, T. Blaschke, O. Engkvist, H. Chen, Molecular de-novo design through deep reinforcement learning, J. Cheminform. 9(1) (2017) 48.
[89] Z. Wang, Y. Su, W. Shen, S. Jin, J.H. Clark, J. Ren, X. Zhang, Predictive deep learning models for environmental properties: The direct calculation of octanol-water partition coefficients from molecular graphs, Green Chem. 21(16) (2019) 4555-4565.
[90] S. Jaeger, S. Fulle, S. Turk, Mol2vec: Unsupervised machine learning approach with chemical intuition, J. Chem. Inf. Model. 58(1) (2018) 27-35.
[91] D. Rogers, M. Hahn, Extended-connectivity fingerprints, J. Chem. Inf. Model. 50(5) (2010) 742-754.
[92] D. Weininger, SMILES, a chemical language and information system: 1: Introduction to methodology and encoding rules, J. Chem. Inf. Comput. Sci. 28(1) (1988) 31-36.
[93] A.P. Bartók, R. Kondor, G. Csányi, On representing chemical environments, Phys. Rev. B 87(18) (2013) 184115.
[94] A. Grisafi, D.M. Wilkins, G. Csányi, M. Ceriotti, Symmetry-adapted machine learning for tensorial properties of atomistic systems, Phys. Rev. Lett. 120(3) (2018) 036002.
[95] L. Himanen, M.O.J. Jäger, E.V. Morooka, F. Federici Canova, Y.S. Ranawat, D.Z. Gao, P. Rinke, A.S. Foster, DScribe: Library of descriptors for machine learning in materials science, Comput. Phys. Commun. 247(2020) 106949.
[96] J. Behler, M. Parrinello, Generalized neural-network representation of highdimensional potential-energy surfaces, Phys. Rev. Lett. 98(14) (2007) 146401.
[97] J. Behler, Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations, Phys. Chem. Chem. Phys. 13(40) (2011) 17930.
[98] J. Behler, Atom-centered symmetry functions for constructing highdimensional neural network potentials, J. Chem. Phys. 134(7) (2011) 074106.
[99] E. Alpaydin, Introduction to Machine Learning, fourth ed., MIT Press, 2020.
[100] M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, A.J. Aljaaf, A systematic review on supervised and unsupervised machine learning algorithms for data science, in: Supervised Unsupervised Learn. Data Sci., Springer, 3-21(2020).
[101] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521(7553) (2015) 436-444.
[102] J. Schmidhuber, Deep learning in neural networks: An overview, Neural Netw. 61(2015) 85-117.
[103] C.E. Rasmussen, Gaussian Processes in Machine Learning, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 63-71(2004).
[104] W.-C. Lu, X.-B. Ji, M.-J. Li, L. Liu, B.-H. Yue, L.-M. Zhang, Using support vector machine for materials design, Adv. Manuf. 1(2) (2013) 151-159.
[105] T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, A.Y. Wu, An efficient k-means clustering algorithm: Analysis and implementation, IEEE Trans. Pattern Anal. Mach. Intell. 24(7) (2002) 881-892.
[106] S. Salvador, P. Chan, Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms, in: 16th IEEE Int. Conf. Tools with Artif. Intell., IEEE Comput. Soc, 576-584(2004).
[107] D.A. Reynolds, T.F. Quatieri, R.B. Dunn, Speaker Verification using adapted Gaussian mixture models, Digit. Signal Process. 10(1-3) (2000) 19-41.
[108] S. Shalev-Shwartz, S. Ben-David, Understanding Machine Learning, Cambridge University Press, Cambridge, 2014.
[109] T.J. Fortin, A. Laesecke, Viscosity measurements of aviation turbine fuels, Energy Fuels 29(9) (2015) 5495-5506.
[110] W. Hu, Z. Deng, G. Xie, Energy loss in pulse detonation engine due to fuel viscosity, Math. Probl. Eng. 2014(2014) 1-5.
[111] D.A. Saldana, L. Starck, P. Mougin, B. Rousseau, N. Ferrando, B. Creton, Prediction of density and viscosity of biofuel compounds using machine learning methods, Energy Fuels 26(4) (2012) 2416-2426.
[112] Accelrys Software Inc, Materials Studio (2020). https://www.3ds.com/products-services/biovia/products/molecular-modeling-simulation/bioviamateri.
[113] I. Iguyon, A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res. 3(2003) 1157-1182.
[114] J.B.O. Mitchell, Machine learning methods in chemoinformatics, Wiley Interdiscip. Rev. Comput. Mol. Sci. 4(5) (2014) 468-481.
[115] K.R. Müller, S. Mika, G. Rätsch, K. Tsuda, B. Schölkopf, An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks. 12(2) (2001) 181-201.
[116] G. Cai, Z. Liu, L. Zhang, S. Zhao, C. Xu, Quantitative structure-property relationship model for hydrocarbon liquid viscosity prediction, Energy Fuels 32(3) (2018) 3290-3298.
[117] A. Murata, K. Tochigi, H. Yamamoto, Prediction of the liquid viscosities of pure components and mixtures using neural network and ASOG group contribution methods, Mol. Simul. 30(7) (2004) 451-457.
[118] D. Eisenberg, A.D. Mclachlan, Solvation energy in protein folding and binding, Nature 319(6050) (1986) 199-203.
[119] A. Chremos, J.F. Douglas, Polyelectrolyte association and solvation, J. Chem. Phys. 149(16) (2018) 163305.
[120] C.N. Pace, B.A. Shirley, M. McNutt, K. Gajiwala, Forces contributing to the conformational stability of proteins, FASEB J. 10(1) (1996) 75-83.
[121] D.S. Palmer, J.L. McDonagh, J.B.O. Mitchell, T. van Mourik, M.V. Fedorov, Firstprinciples calculation of the intrinsic aqueous solubility of crystalline druglike molecules, J. Chem. Theory Comput. 8(9) (2012) 3322-3337.
[122] S.T. Lin, S.I. Sandler, Henry’s law constant of organic compounds in water from a group contribution model with multipole corrections, Chem. Eng. Sci. 57(14) (2002) 2727-2733.
[123] C. Panayiotou, Equation-of-state models and quantum mechanics calculations, Ind. Eng. Chem. Res. 42(7) (2003) 1495-1507.
[124] D.L. Mobley, J.P. Guthrie, FreeSolv: A database of experimental and calculated hydration free energies, with input files, J. Comput. Aided. Mol. Des. 28(7) (2014) 711-720.
[125] A.V. Marenich, C.P. Kelly, J.D. Thompson, G.D. Hawkins, C.C. Chambers, D.J. Giesen, P. Winget, C.J. Cramer, D.G. Truhlar, Minnesota Solvation DatabaseVersion 2012, University of Minnesota, Minneapolis, 2012, http://comp.chem.umn.edu/mnsol/.
[126] H. Lim, Y.J. Jung, Delfos: Deep learning model for prediction of solvation free energies in generic organic solvents, Chem. Sci. 10(36) (2019) 8306-8315.
[127] T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, (2013) arXiv:1301.3781[cs.CL]. http://arxiv.org/abs/1301.3781.
[128] K.K. Yang, Z. Wu, C.N. Bedbrook, F.H. Arnold, Learned protein embeddings for machine learning, Bioinformatics 34(15) (2018) 2642-2648.
[129] M. Schuster, K.K. Paliwal, Bidirectional recurrent neural networks, IEEE Trans. Signal Process. 45(11) (1997) 2673-2681.
[130] D. Bahdanau, K.H. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate, 3rd Int. Conf. Learn. Represent. ICLR 2015-Conf. Track Proc. (2015) 1-15. http://arxiv.org/abs/1409.0473.
[131] S. Zheng, X. Yan, Y. Yang, J. Xu, Identifying structure-property relationships through SMILES syntax analysis with self-attention mechanism, J. Chem. Inf. Model. 59(2) (2019) 914-923.
[132] Z. Xiong, D. Wang, X. Liu, F. Zhong, X. Wan, X. Li, Z. Li, X. Luo, K. Chen, H. Jiang, M. Zheng, Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism, J. Med. Chem. 63(16) (2020) 8749-8760.
[133] Z. Wu, B. Ramsundar, E.N. Feinberg, J. Gomes, C. Geniesse, A.S. Pappu, K. Leswing, V. Pande, MoleculeNet: A benchmark for molecular machine learning, Chem. Sci. 9(2) (2018) 513-530.
[134] A. Klamt, The COSMO and COSMO-RS solvation models, Wiley Interdiscip. Rev. Comput. Mol. Sci. 8(1) (2018) e1338.
[135] H. Hosseinkhani, P. Da Hong, D.S. Yu, Self-assembled proteins and peptides for regenerative medicine, Chem. Rev. 113(7) (2013) 4837-4861.
[136] S.H. Kim, J.R. Parquette, A model for the controlled assembly of semiconductor peptides, Nanoscale 4(22) (2012) 6940-6947.
[137] B.D. Wall, A.E. Zacca, A.M. Sanders, W.L. Wilson, A.L. Ferguson, J.D. Tovar, Supramolecular polymorphism: Tunable electronic interactions within pconjugated peptide nanostructures dictated by primary amino acid sequence, Langmuir 30(20) (2014) 5946-5956.
[138] H.A.M. Ardoña, K. Besar, M. Togninalli, H.E. Katz, J.D. Tovar, Sequencedependent mechanical, photophysical and electrical properties of piconjugated peptide hydrogelators, J. Mater. Chem. C 3(25) (2015) 6505-6514.
[139] X. Guo, M. Baumgarten, K. Müllen, Designing p-conjugated polymers for organic electronics, Prog. Polym. Sci. 38(12) (2013) 1832-1908.
[140] K. Besar, H.A.M. Ardoña, J.D. Tovar, H.E. Katz, Demonstration of hole transport and voltage equilibration in self-assembled p-conjugated peptide nanostructures using field-effect transistor architectures, ACS Nano 9(12) (2015) 12401-12409.
[141] B.A. Thurston, J.D. Tovar, A.L. Ferguson, Thermodynamics, morphology, and kinetics of early-stage self-assembly of p-conjugated oligopeptides, Mol. Simul. 42(12) (2016) 955-975.
[142] B.A. Thurston, E.P. Shapera, J.D. Tovar, A. Schleife, A.L. Ferguson, Revealing the sequence-structure-electronic property relation of self-assembling pconjugated oligopeptides by molecular and quantum mechanical modeling, Langmuir 35(47) (2019) 15221-15231.
[143] B.A. Thurston, A.L. Ferguson, Machine learning and molecular design of selfassembling-conjugated oligopeptides, Mol. Simul. 44(11) (2018) 930-945.
[144] C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32(7) (2011) 1466-1474.
[145] K. Shmilovich, R.A. Mansbach, H. Sidky, O.E. Dunne, S.S. Panda, J.D. Tovar, A.L. Ferguson, Discovery of self-assembling p-conjugated peptides by active learning-directed coarse-grained molecular simulation, J. Phys. Chem. B 124(19) (2020) 3873-3891.
[146] C. Kim, A. Chandrasekaran, A. Jha, R. Ramprasad, Active-learning and materials design: the example of high glass transition temperature polymers, MRS Commun. 9(3) (2019) 860-866.
[147] R. Calandra, J. Peters, C.E. Rasmussen, M.P. Deisenroth, Manifold Gaussian Processes for regression, in: 2016 Int. Jt. Conf. Neural Networks, IEEE, 3338-3345(2016).
[148] N.M. O’Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, G.R. Hutchison, Open Babel: An Open chemical toolbox, J. Cheminform. 3(10) (2011) 33.
[149] H. Chan, M.J. Cherukara, B. Narayanan, T.D. Loeffler, C. Benmore, S.K. Gray, S. K.R.S. Sankaranarayanan, Machine learning coarse grained models for water, Nat. Commun. 10(1) (2019) 379.
[150] L. Zhang, J. Han, H. Wang, R. Car, W.E. Weinan, P.C.G. Dee, Constructing coarse-grained models via deep neural networks, J. Chem. Phys. 149(3) (2018), 034101.
[151] J.S. Smith, O. Isayev, A.E. Roitberg, ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost, Chem. Sci. 8(4) (2017) 3192-3203.
[152] J.S. Smith, B. Nebgen, N. Lubbers, O. Isayev, A.E. Roitberg, Less is more: Sampling chemical space with active learning, J. Chem. Phys. 148(24) (2018) 241733.
[153] A. Singraber, J. Behler, C. Dellago, Library-based LAMMPS implementation of high-dimensional neural network potentials, J. Chem. Theory Comput. 15(3) (2019) 1827-1840.
[154] Y. Zuo, C. Chen, X. Li, Z. Deng, Y. Chen, J. Behler, G. Csányi, A.V. Shapeev, A.P. Thompson, M.A. Wood, S.P. Ong, Performance and cost assessment of machine learning interatomic potentials, J. Phys. Chem. A 124(4) (2020) 731-745.
[155] N. Xu, Y. Shi, Y. He, Q. Shao, A deep-learning potential for crystalline and amorphous Li-Si alloys, J. Phys. Chem. C 124(30) (2020) 16278-16288.
[156] V. Molinero, E.B. Moore, Water modeled as an intermediate element between carbon and silicon, J. Phys. Chem. B 113(13) (2009) 4008-4016.
[157] E.B. Moore, V. Molinero, Structural transformation in supercooled water controls the crystallization rate of ice, Nature 479(7374) (2011) 506-508.

Machine learning for molecular thermodynamics

Machine learning for molecular thermodynamics

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