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• Braams, B.J., Bowman, J.M. Permutationally invariant potential energy surfaces in high dimensionality.Int. Rev. Phys. Chem. 2009, 28, 577–606.
• Qu, C., Yu, Q., Bowman, J.M. Permutationally invariant potential energy surfaces.Annu. Rev. Phys. Chem. 2018, 69, 6.1–6.25.
• Behler, J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations.Phys. Chem. Chem. Phys. 2011, 13, 17930–17955.
• Behler, J. Constructing high-dimensional neural network potentials: A tutorial review.Int. J. Quantum Chem. 2015, 115, 1032–1050.
• Bartók, A.P., Csányi, G. Gaussian approximation potentials: A brief tutorial introduction.Int. J. Quantum Chem. 2015, 115, 1051–1057.
• Cui, J., Krems, R.V. Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes.J. Phys. B: At. Mol. Opt. Phys. 2016, 49.
• Jiang, B., Guo, H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces.J. Chem. Phys. 2013, 139, 054112.
• Jiang, B., Li, J., Guo, H. Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach.Int. Rev. Phys. Chem. 2016, 35, 479–506.
• Qu, C., Yu, Q., Van Hoozen, B.L., Bowman, J.M., Vargas-Hernández, R.A. Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches To Represent High-Dimensional Potential Energy Surfaces.J. Chem. Theory Comput. 2018, 14, 3381–3396.
• Dral, P.O., Owens, A., Dral, A., Csányi, G. Hierarchical machine learning of potential energy surfaces.J. Chem. Phys. 2020, 152, 204110.
• Hou, Y.-F., Ge, F., Dral, P.O. Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces.J. Chem. Theory Comput. 2023, 19, 2369.
• Crawford, T.D., Schaefer III, H.F. An Introduction to Coupled Cluster Theory for Computational Chemists.Rev. Comp. Chem. 1999, 14, 33.
• Shavitt, I., Bartlett, R.J. Many-Body Methods in Chemistry and Physics: MBPT and Coupled-Cluster Theory; Cambridge Molecular Science; Cambridge University Press, 2009.
• Bartlett, R.J., Musiał, M. Coupled-cluster theory in quantum chemistry.Rev. Mod. Phys. 2007, 79, 291.
• Raghavachari, K., Trucks, G.W., Pople, J.A., Head-Gordon, M. A fifth-order perturbation comparison of electron correlation theories.Chem. Phys. Lett. 1989, 157, 479.
• Deegan, M.J.O., Knowles, P.J. Perturbative corrections to account for triple excitations in closed and open shell coupled cluster theories.Chem. Phys. Lett. 1994, 227, 321.
• Kobayashi, R., Rendell, A.P. A direct coupled cluster algorithm for massively parallel computers.Chem. Phys. Lett. 1997, 265, 1.
• Pitoňák, M., Aquilante, F., Hobza, P., Neogrády, P., Noga, J., Urban, M. Parallelized implementation of the CCSD(T) method in MOLCAS using optimized virtual orbitals space and Cholesky decomposed two-electron integrals.Collect. Czech. Chem. Commun. 2011, 76, 713.
• Asadchev, A., Gordon, M.S. Fast and Flexible Coupled Cluster Implementation.J. Chem. Theory Comput. 2013, 9, 3385.
• Deumens, E., Lotrich, V.F., Perera, A., Ponton, M.J., Sanders, B.A., Bartlett, R.J. Software design of ACES III with the super instruction architecture.Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2011, 1, 895.
• Kaliman, I.A., Krylov, A.I. New algorithm for tensor contractions on multi-core CPUs, GPUs, and accelerators enables CCSD and EOM-CCSD calculations with over 1000 basis functions on a single compute node.J. Comput. Chem. 2017, 38, 842.
• Janowski, T., Pulay, P. Efficient Parallel Implementation of the CCSD External Exchange Operator and the Perturbative Triples (T) Energy Calculation.J. Chem. Theory Comput. 2008, 4, 1585.
• Solomonik, E., Matthews, D., Hammond, J.R., Stanton, J.F., Demmel, J. A massively parallel tensor contraction framework for coupled-cluster computations.J. Parallel Distr. Comput. 2014, 74, 3176.
• Peng, C., Calvin, J.A., Valeev, E.F. Coupled-cluster singles, doubles and perturbative triples with density fitting approximation for massively parallel heterogeneous platforms.Int. J. Quantum Chem. 2019, 119, e25894.
• Shen, T., Zhu, Z., Zhang, I.Y., Scheffler, M. Massive-parallel Implementation of the Resolution-of-Identity Coupled-cluster Approaches in the Numeric Atom-centered Orbital Framework for Molecular Systems.J. Chem. Theory Comput. 2019, 15, 4721.
• Gyevi-Nagy, L., Kállay, M., Nagy, P.R. Integral-direct and parallel implementation of the CCSD(T) method: Algorithmic developments and large-scale applications.J. Chem. Theory Comput. 2020, 16, 366.
• Kutzelnigg, W., Klopper, W. Wave functions with terms linear in the interelectronic coordinates to take care of the correlation cusp. I. General theory.J. Chem. Phys. 1991, 94, 1985.
• Klopper, W., Manby, F.R., S. Ten-no, Valeev E.F., R12 methods in explicitly correlated molecular electronic structure theory.Int. Rev. Phys. Chem. 2006, 25, 427.
• Hättig, C., Klopper, W., Köhn, A., Tew, D.P. Explicitly Correlated Electrons in Molecules.Chem. Rev. 2012, 112, 4.
• Hättig, C., Tew, D.P., Köhn, A. Accurate and efficient approximations to explicitly correlated coupled-cluster singles and doubles, CCSD-F12.J. Chem. Phys. 2010, 132, 231102.
• Kállay, M., Horváth, R.A., Gyevi-Nagy, L., Nagy, P.R. Size-consistent explicitly correlated triple excitation correction.J. Chem. Phys. 2021, 155, 034107.
• Kállay, M., Horváth, R.A., Gyevi-Nagy, L., Nagy, P.R. Basis set limit CCSD(T) energies for extended molecules via a reduced-cost explicitly correlated approach.J. Chem. Theory Comput. 2023, 19, 174.
• Löwdin, P.-O. Quantum theory of many-particle systems. I. Physical interpretations by means of density matrices, natural spin-orbitals, and convergence problems in the method of configurational interaction.Phys. Rev. 1955, 97, 1474.
• Taube, A.G., Bartlett, R.J. Frozen natural orbitals: Systematic basis set truncation for coupled-cluster theory.Collect. Czech. Chem. Commun. 2005, 70, 837.
• DePrince, A.E., Sherrill, C.D. Accurate Noncovalent Interaction Energies Using Truncated Basis Sets Based on Frozen Natural Orbitals.J. Chem. Theory Comput. 2013, 9, 293.
• Gyevi-Nagy, L., Kállay, M., Nagy, P.R. Accurate reduced-cost CCSD(T) energies: parallel implementation, benchmarks, and large-scale applications.J. Chem. Theory Comput. 2021, 17, 860.
• Nagy, P.R., Gyevi-Nagy, L., Kállay, M. Basis set truncation corrections for improved frozen natural orbital CCSD(T) energies.Mol. Phys. 2021, 119, e1963495.
• Chmiela, S., Sauceda, H.E., Müller, K.-R., Tkatchenko, A. Towards exact molecular dynamics simulations with machine-learned force fields.Nat. Commun. 2018, 9, 3887.
• Sauceda, H.E., Vassilev-Galindo, V., Chmiela, S., Müller, K.-R., Tkatchenko, A. Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature.Nat. Commun. 2021, 12, 442.
• Smith, J.S., Nebgen, B.T., Zubatyuk, R., Lubbers, N., Devereux, C., Barros, K., Tretiak, S., Isayev, O., Roitberg, A.E. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.Nat. Commun. 2019, 10, 2903.
• Käser, S., Unke, O.T., Meuwly, M. Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces.New J. Phys. 2020, 22, 055002.
• Young, T.A., Johnston-Wood, T., Deringer, V.L., Duarte, F. A transferable active-learning strategy for reactive molecular force fields.Chem. Sci. 2021, 12, 10944.
• Qu, C., Houston, P.L., Conte, R., Nandi, A., Bowman, J.M. Breaking the Coupled Cluster Barrier for Machine-Learned Potentials of Large Molecules: The Case of 15-Atom Acetylacetone.J. Phys. Chem. Lett. 2021, 12, 4902.
• Khire, S.S., Gurav, N.D., Nandi, A., Gadre, S.R., Enabling Rapid and Accurate Construction of CCSD(T)-Level Potential Energy Surface of Large Molecules Using Molecular Tailoring Approach.J. Phys. Chem. A 2022, 126, 1458–1464.
• Jensen, A.B., Kubečka, J., Schmitz, G., Christiansen, O., Elm, J. Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters.Journal of Chemical Theory and Computation 2022, 18, 7373–7383.
• Daru, J., Forbert, H., Behler, J., Marx, D. Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark.Phys. Rev. Lett. 2022, 129, 226001.
• Yu, Q., Qu, C., Houston, P.L., Conte, R., Nandi, A., Bowman, J.M. q-AQUA: AMany-Body CCSD(T) Water Potential, Including Four-Body Interactions, Demonstrates the Quantum Nature of Water from Clusters to the Liquid Phase.J. Phys. Chem. Lett. 2022, 13, 5068–5074.
• Nandi, A., Qu, C., Houston, P.L., Conte, R., Yu, Q., Bowman, J.M. A CCSD(T)-Based 4-Body Potential for Water.J. Phys. Chem. Lett. 2021, 12, 10318–10324.
• Fu, Y.-L., Lu, X., Han, Y.-C., Fu, B., Zhang, D.H., Bowman, J.M. Collision-induced and complex-mediated roaming dynamics in the H + C2H4 → H2 + C2H3 reaction.Chem. Sci. 2020, 11, 2148–2154.
• Lu, D., Behler, J., Li, J. Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance.J. Phys. Chem. A 2020, 124, 5737–5745.
• Papp, D., Czakó, G. Rotational Mode-Specificity in the Cl + C2H6 → HCl + C2H5 Reaction.J. Phys. Chem. A 2022, 126, 2551–2560.
• Gruber, B., Tajti, V., Czakó, G. Full-dimensional automated potential energy surface development and dynamics for the OH + C2H6 reaction.J. Chem. Phys. 2022, 157, 074307.
• Qu, C., Bowman, J.M. An ab initio potential energy surface for the formic acid dimer: zero-point energy, selected anharmonic fundamental energies, and ground-state tunneling splitting calculated in relaxed 1–4-mode subspaces.Phys. Chem. Chem. Phys. 2016, 18, 24835–24840.
• Guo, Y., Riplinger, C., Becker, U., Liakos, D.G., Minenkov, Y., Cavallo, L., Neese, F. Communication: An improved linear scaling perturbative triples correction for the domain based local pair-natural orbital based singles and doubles coupled cluster method [DLPNO-CCSD(T)].J. Chem. Phys. 2018, 148, 011101.
• Ma, Q., Werner, H.-J. Explicitly correlated local coupled-cluster methods using pair natural orbitals.Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2018, 8, e1371.
• Nagy, P.R., Samu, G., Kállay, M. Optimization of the linear-scaling local natural orbital CCSD(T) method: Improved algorithm and benchmark applications.J. Chem. Theory Comput. 2018, 14, 4193.
• Nagy, P.R., Kállay, M. Approaching the basis set limit of CCSD(T) energies for large molecules with local natural orbital coupled-cluster methods.J. Chem. Theory Comput. 2019, 15, 5275.
• Nandi, A., Laude, G., Khire, S.S., Gurav, N.D., Qu, C., Conte, R., Yu, Q., Li, S., Houston, P.L., Gadre, S.R., Richardson, J.O., Evangelista, F.A., Bowman, J.M. Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands.J. Am. Chem. Soc. 2023, 145, 9655–9664.
• Kállay, M., Nagy, P.R., Mester, D., Rolik, Z., Samu, G., Csontos, J., Csóka, J., Szabó, P.B., Gyevi-Nagy, L., Hégely, B., Ladjánszki, I., Szegedy, L., Ladóczki, B., Petrov, K., Farkas, M., Mezei, P.D., Ganyecz, Á. The MRCC program system: Accurate quantum chemistry from water to proteins.J. Chem. Phys. 2020, 152, 074107.
• Kállay, M., Nagy, P.R., Mester, D., Gyevi-Nagy, L., Csóka, J., Szabó, P.B., Rolik, Z., Samu, G., Csontos, J., Hégely, B., Ganyecz, Á., Ladjánszki, I., Szegedy, L., Ladóczki, B., Petrov, K., Farkas, M., Mezei, P.D., Horváth, R.A. Mrcc, a quantum chemical program suite. See Accessed August 1, 2023.2023.
• Nagy, P.R., Gyevi-Nagy, L., Lőrincz, B.D., Kállay, M. Pursuing the basis set limit of CCSD(T) non-covalent interaction energies for medium-sized complexes: case study on the S66 compilation.Mol. Phys. 2023, 121, e2109526.
• DePrince, A.E., Sherrill, C.D. Accuracy and Efficiency of Coupled-Cluster Theory Using Density Fitting/Cholesky Decomposition, Frozen Natural Orbitals, and a t1-Transformed Hamiltonian.J. Chem. Theory Comput. 2013, 9, 2687.
• Nagy, P.R., Kállay, M. Optimization of the linear-scaling local natural orbital CCSD(T) method: Redundancy-free triples correction using Laplace transform.J. Chem. Phys. 2017, 146, 214106.
• Lee, T.J., Rendell, A.P., Taylor, P.R. Comparison of the quadratic configuration interaction and coupled-cluster approaches to electron correlation including the effect of triple excitations.J. Phys. Chem. 1990, 94, 5463.
• Kállay, M. A systematic way for the cost reduction of density fitting methods.J. Chem. Phys. 2014, 141, 244113.
• Liu, Y., Li, J. Permutation-invariant-polynomial neural-network-based Δ-machine learning approach: A case for the HO2 self-reaction and its dynamics study.J. Phys. Chem. Letts. 2022, 13, 4729–4738.
• Song, K., Li, J. The neural network based Δ-machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH3OH reaction.Phys. Chem. Chem. Phys. 2023, 25, 11192–11204.
• Li, J., Li, J. An accurate full-dimensional interaction potential energy surface of CO2 + N2 incorporating Δ-machine learning approach via permutation invariant polynomial-neural network.Artificial Intelligence Chemistry 2023, 1, 100019.
• Nandi, A., Qu, C., Houston, P.L., Conte, R., Bowman, J.M. Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory.J. Chem. Phys. 2021, 154, 051102.
• Bowman, J.M., Qu, C., Conte, R., Nandi, A., Houston, P.L., Yu, Q. Δ-Machine Learned Potential Energy Surfaces and Force Fields.J. Chem. Theory and Comput. 2023, 19, 1–17.
• Bowman, J.M., Braams, B.J., Carter, S., Chen, C., Czak´o, G., Fu, B., Huang, X., Kamarchik, E., Sharma, A.R., Shepler, B.C., Wang, Y., Xie, Z. Ab-initio-based potential energy surfaces for complex molecules and molecular complexes.J. Phys. Chem. Lett. 2010, 1, 1866–1874.
• Xie, Z., Bowman, J.M. Permutationally Invariant Polynomial Basis for Molecular Energy Surface Fitting via Monomial Symmetrization.J. Chem. Theory Comput. 2010, 6, 26–34.
• Bowman, J.M., Czakó, G., Fu, B. High-dimensional ab initio potential energy surfaces for reaction dynamics calculations.Phys. Chem. Chem. Phys. 2011, 13, 8094–8111.
• Qu, C., Conte, R., Houston, P.L., Bowman, J.M. Full-dimensional potential energy surface for acetylacetone and tunneling splittings.Phys. Chem. Chem. Phys. 2021, 23, 7758–7767.
• Nandi, A., Qu, C., Bowman, J.M. Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH4 Using as Few as 100 Configurations.J. Chem. Theory Comput. 2019, 15, 2826–2835.
• MSA Software with Gradients., 2019; Accessed: 2019–01-20.
• Dunning Jr., T.H. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen.J. Chem. Phys. 1989, 90, 1007.
• Kendall, R.A., Dunning Jr., T.H., Harrison, R.J. Electron affinities of the first-row atoms revisited. Systematic basis sets and wave functions.J. Chem. Phys. 1992, 96, 6796.
• Weigend, F. Hartree–Fock Exchange Fitting Basis Sets for H to Rn.J. Comput. Chem. 2008, 29, 167.
• Weigend, F., Köhn, A., Hättig, C. Efficient use of the correlation consistent basis sets in resolution of the identity MP2 calculations.J. Chem. Phys. 2002, 116, 3175.
• Peterson, K.A., Adler, T.B., Werner, H.-J. Systematically convergent basis sets for explicitly correlated wavefunctions: The atoms H, He, B-Ne, and Al-Ar.J. Chem. Phys. 2008, 128, 084102.
• Yousaf, K.E., Peterson, K.A. Optimized auxiliary basis sets for explicitly correlated methods.J. Chem. Phys. 2008, 129, 184108.
• Yousaf, K.E., Peterson, K.A. Optimized complementary auxiliary basis sets for explicitly correlated methods: aug-cc-pVnZ orbital basis sets.Chem. Phys. Lett. 2009, 476, 303.
• Karton, A., Martin, J.M.L. Comment on: “Estimating the Hartree–Fock limit from finite basis set calculations. Theor. Chem. Acc. 2006, 115, 330.
• Helgaker, T., Klopper, W., Koch, H., Noga, J. Basis-set convergence of correlated calculations on water.J. Chem. Phys. 1997, 106, 9639.
• Wang, Y., Bowman, J.M. One-dimensional tunneling calculations in the imaginary-frequency, rectilinear saddle-point normal mode.J. Chem. Phys. 2008, 129, 121103.
• Colbert, D.T., Miller, W.H. A novel discrete variable representation for quantum mechanical reactive scattering via the S-matrix Kohn method.J. Chem. Phys. 1992, 96, 1982–1991.