Site icon automotivemogul

Large language model guided automated reaction pathway exploration

Large language model guided automated reaction pathway exploration
  • Foscato, M. & Jensen, V. R. Automated in silico design of homogeneous catalysts. ACS Catal. 10, 2354–2377 (2020).

    Article 
    CAS 

    Google Scholar 

  • Houk, K. N. & Cheong, P. H. Y. Computational prediction of small-molecule catalysts. Nature 455, 309–313 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Grimme, S. & Schreiner, P. R. Computational chemistry: the fate of current methods and future challenges. Angew. Chem. Int. Ed. 57, 4170–4176 (2018).

    Article 
    CAS 

    Google Scholar 

  • Berne, B. J., Borkovec, M. & Straub, J. E. Classical and modern methods in reaction rate theory. J. Phys. Chem. 92, 3711–3725 (2002).

    Article 

    Google Scholar 

  • Cukier, R. I. A theory for the rate constant of a dissociative proton-coupled electron-transfer reaction. J. Phys. Chem. A 103, 5989–5995 (1999).

    Article 
    CAS 

    Google Scholar 

  • Puliyanda, A., Srinivasan, K., Sivaramakrishnan, K. & Prasad, V. A review of automated and data-driven approaches for pathway determination and reaction monitoring in complex chemical systems.Digit. Chem. Eng. 2, 100009 (2022).

    Article 

    Google Scholar 

  • Simm, G. N., Vaucher, A. C. & Reiher, M. Exploration of reaction pathways and chemical transformation networks. J. Phys. Chem. A 123, 385–399 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Unsleber, J. P. & Reiher, M. The exploration of chemical reaction networks. Annu. Rev. Phys. Chem. 71, 121–142 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Maeda, S., Ohno, K. & Morokuma, K. Systematic exploration of the mechanism of chemical reactions: the global reaction route mapping (GRRM) strategy using the ADDF and AFIR methods. Phys. Chem. Chem. Phys. 15, 3683–3701 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wang, L. P. et al. Discovering chemistry with an ab initio nanoreactor. Nat. Chem. 6, 1044–1048 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yang, M., Zou, J., Wang, G. & Li, S. Automatic reaction pathway search via combined molecular dynamics and coordinate driving method. J. Phys. Chem. A 121, 1351–1361 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yang, M. et al. Combined molecular dynamics and coordinate driving method for automatic reaction pathway search of reactions in solution. J. Chem. Theory Comput. 14, 5787–5796 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wang, L. P., McGibbon, R. T., Pande, V. S. & Martinez, T. J. Automated discovery and refinement of reactive molecular dynamics pathways. J. Chem. Theory Comput. 12, 638–649 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Rappoport, D., Galvin, C. J., Zubarev, D. Y. & Aspuru-Guzik, A. Complex chemical reaction networks from heuristics-aided quantum chemistry. J. Chem. Theory Comput. 10, 897–907 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Van de Vijver, R. & Zádor, J. KinBot: automated stationary point search on potential energy surfaces. Comput. Phys. Commun. 248, 106947 (2020).

    Article 

    Google Scholar 

  • Elliott, S. N. et al. Automated theoretical chemical kinetics: predicting the kinetics for the initial stages of pyrolysis. Proc. Combust. Inst. 38, 375–384 (2021).

    Article 
    CAS 

    Google Scholar 

  • Liu, M. et al. Reaction mechanism generator v3.0: advances in automatic mechanism generation. J. Chem. Inf. Model. 61, 2686–2696 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Rangarajan, S., Kaminski, T., Van Wyk, E., Bhan, A. & Daoutidis, P. Language-oriented rule-based reaction network generation and analysis: algorithms of RING. Comput. Chem. Eng. 64, 124–137 (2014).

    Article 
    CAS 

    Google Scholar 

  • Simm, G. N. & Reiher, M. Context-driven exploration of complex chemical reaction networks. J. Chem. Theory Comput. 13, 6108–6119 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. Computer-assisted retrosynthesis based on molecular similarity. ACS Cent. Sci. 3, 1237–1245 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A. & Smit, B. Leveraging large language models for predictive chemistry. Nat. Mach. Intell. 6, 161–169 (2024).

    Article 

    Google Scholar 

  • Su, Y. et al. Automation and machine learning augmented by large language models in a catalysis study. Chem. Sci. 15, 12200–12233 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • White, A. D. et al. Assessment of chemistry knowledge in large language models that generate code. Digit Discov. 2, 368–376 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wu, Z. et al. Leveraging language model for advanced multiproperty molecular optimization via prompt engineering. Nat. Mach. Intell. 6, 1359–1369 (2024).

    Article 

    Google Scholar 

  • Zubatiuk, T. & Isayev, O. Development of multimodal machine learning potentials: toward a physics-aware artificial intelligence. Acc. Chem. Res. 54, 1575–1585 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Noé, F., Tkatchenko, A., Müller, K.-R. & Clementi, C. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71, 361–390 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Grisafi, A., Nigam, J. & Ceriotti, M. Multi-scale approach for the prediction of atomic scale properties. Chem. Sci. 12, 2078–2090 (2021).

    Article 
    CAS 

    Google Scholar 

  • Friederich, P., Häse, F., Proppe, J. & Aspuru-Guzik, A. Machine-learned potentials for next-generation matter simulations. Nat. Mater. 20, 750–761 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dral, P. O. Quantum chemistry in the age of machine learning. J. Phys. Chem. Lett. 11, 2336–2347 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Behler, J. Four generations of high-dimensional neural network potentials. Chem. Rev. 121, 10037–10072 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J. General-purpose machine learning potentials capturing nonlocal charge transfer. Acc. Chem. Res. 54, 808–817 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nat. Commun. 12, 398 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Shang, C. & Liu, Z. P. Stochastic surface walking method for structure prediction and pathway searching. J. Chem. Theory Comput. 9, 1838–1845 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Zhang, X. J., Shang, C. & Liu, Z. P. From atoms to fullerene: stochastic surface walking solution for automated structure prediction of complex material. J. Chem. Theory Comput. 9, 3252–3260 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Shang, C., Zhang, X. J. & Liu, Z. P. Stochastic surface walking method for crystal structure and phase transition pathway prediction. Phys. Chem. Chem. Phys. 16, 17845–17856 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dral, P. O. et al. MLatom 3: a platform for machine learning-enhanced computational chemistry simulations and workflows. J. Chem. Theory Comput. 20, 1193–1213 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dral, P. O. et al. MLatom: a package for atomistic simulations with machine learning, version 3.6.0, Xiamen University, Xiamen, China, 2013–2024.

  • Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O. & Roitberg, A. E. Less is more: sampling chemical space with active learning. J. Chem. Phys. 148, 241733 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Lin, Q., Zhang, L., Zhang, Y. & Jiang, B. Searching configurations in uncertainty space: active learning of high-dimensional neural network reactive potentials. J. Chem. Theory Comput. 17, 2691–2701 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kocer, E., Ko, T. W. & Behler, J. Neural network potentials: a concise overview of methods. Annu. Rev. Phys. Chem. 73, 163–186 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Frisch, M. J. et al. Gaussian 09 Rev. D.01 (Gaussian, Inc., 2009).

  • Grimme, S., Bannwarth, C. & Shushkov, P. A robust and accurate tight-binding quantum chemical method for structures, vibrational frequencies, and noncovalent interactions of large molecular systems parametrized for all spd-block elements (Z = 1–86). J. Chem. Theory Comput. 13, 1989–2009 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Young, T. A., Silcock, J. J., Sterling, A. J. & Duarte, F. autodE: automated calculation of reaction energy profiles— application to organic and organometallic reactions. Angew. Chem. Int. Ed. 60, 4266–4274 (2020).

    Article 

    Google Scholar 

  • Sáez, J. A., Arnó, M. & Domingo, L. R. Lewis acid-catalyzed [4 + 3] cycloaddition of 2-(Trimethyl Silyloxy)acrolein with furan. insight on the nature of the mechanism from a DFT analysis. O. Rg. Lett. 5, 4117–4120 (2003).

    Google Scholar 

  • Roca-López, D., Polo, V., Tejero, T. & Merino, P. Mechanism switch in Mannich-type reactions: ELF and NCI topological analyses of the reaction between nitrones and lithium enolates. Eur. J. Org. Chem. 2015, 4143–4152 (2015).

    Article 

    Google Scholar 

  • Patra, S. R., Sangma, S. W., Padhy, A. K. & Bhunia, S. Oxidative addition to the N–C Bond vs formation of the Zwitterionic intermediate in platinum(II)–catalyzed intramolecular annulation of alkynes to form indoles: mechanistic studies and reaction scope. J. Org. Chem. 87, 9714–9722 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Neese, F. The ORCA program system Wiley interdiscip. Rev. Comput. Mol. Sci. 2, 73–78 (2012).

    Article 
    CAS 

    Google Scholar 

  • Becke, A. D. Density‐functional thermochemistry. III. Role Exact. Exch. J. Chem. Phys. 98, 5648–5652 (1993).

    Article 
    CAS 

    Google Scholar 

  • Chengteh, L., Weitao, Y. & Parr, R. G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 37, 785–789 (1988).

    Article 

    Google Scholar 

  • Hay, P. J. & Wadt, W. R. Ab initio effective core potentials for molecular calculations. Potentials for K to Au including the outermost core orbitals. J. Chem. Phys. 82, 299–310 (1985). 1985.

    Article 
    CAS 

    Google Scholar 

  • Chiodo, S., Russo, N. & Sicilia, E. LANL2DZ basis sets recontracted in the framework of density functional theory. J. Chem. Phys. 125, 104107 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • link

    Exit mobile version