Three decades of quantum science: how quantum chemistry transformed thermochemical database generation for benchmarking DFT and machine learning
Amir Karton
A
Abstract
In celebration of the United Nations’ declaration of 2025 as the International Year of Quantum Science and Technology, marking 100 years since the development of quantum mechanics, this review highlights how accurate quantum mechanical calculations have transformed gas-phase thermochemistry. In particular, the developments of high-level composite ab initio methods over the past 30 years enable the calculations of thermochemical properties with confident chemical accuracy (i.e. with 95% confidence intervals ≤1 kcal mol−1) for molecules with up to 12 non-hydrogen atoms. Lower-level composite ab initio methods can be applied to molecules containing up to ~50 non-hydrogen atoms; however, they cannot achieve confident chemical accuracy in terms of 95% confidence intervals. Over the past three decades, hundreds of composite ab initio methods have been developed, covering different theoretical frameworks, levels of accuracy and computational costs. To guide users in selecting an appropriate composite ab initio method for a given system size and level of accuracy, we present a general approach for categorising the accuracy of these methods. This approach places composite ab initio methods on four rungs of Jacob’s Ladder. Lower rungs offer less accuracy but are applicable to larger systems, and higher rungs offer greater accuracy but are applicable to smaller systems. Each consecutive rung of this ladder represents an improvement in the treatment of the one-particle space, n-particle space, or both, leading toward the exact solution of the relativistic Schrödinger equation. The Jacob’s Ladder of composite ab initio methods can be considered as an extension to the Jacob’s Ladder of density functional theory (DFT), which leads from ‘Hartree Hell’ to the ‘Heaven’ of double-hybrid DFT methods.
Keywords: CCSD(T), CCSDTQ, chemical databases, composite ab initio methods, density functional theory, machine-learning, quantum chemistry.
References
1 Afeefy HY, Liebman JF, Stein SE. NIST Chemistry WebBook, SRD 69. Linstrom PJ, Mallard WG, editors. Gaithersburg, MD, USA: National Institute of Standards and Technology. Available at http://webbook.nist.gov
2 Chase MW, Davies CA, Downey JR, Frurip DJ, McDonald RA, Syverud AN. JANAF thermochemical tables. J Phys Chem Ref Data 1985; 14(Suppl. 1):.
| Google Scholar |
3 Chase MW. NIST-JANAF thermochemical tables. J Phys Chem Ref Data 1998; 9:.
| Google Scholar |
4 Lias SG, Bartmess JE, Liebman JF, Holmes JL, Levin RD, Mallard WG. Gas-phase ion and neutral thermochemistry. J Phys Chem Ref Data 1988; 17(Suppl. 1): 861.
| Google Scholar |
6 Cox JD, Wagman DD, Medvedev VA. CODATA Key Values for Thermodynamics. New York, NY, USA: Hemisphere Publishing Corp.; 1989. Available at http://www.codata.org/resources/databases/key1.html
11 Ruscic B, Pinzon RE, Morton ML, von Laszewski G, Bittner SJ, Nijsure SG, Amin KA, Minkoff M, Wagner AF. Introduction to active thermochemical tables: several “key” enthalpies of formation revisited. J Phys Chem A 2004; 108: 9979-9997.
| Crossref | Google Scholar |
12 Ruscic B, Pinzon RE, von Laszewski G, Kodeboyina D, Burcat A, Leahy D, Montoya D, Wagner AF. Active thermochemical tables: thermochemistry for the 21st Century. J Phys Conf Ser 2005; 16: 561.
| Crossref | Google Scholar |
13 Narayanan B, Redfern PC, Assary RS, Curtiss LA. Accurate quantum chemical energies for 133 000 organic molecules. Chem Sci 2019; 10: 7449-7455.
| Crossref | Google Scholar | PubMed |
14 Pople JA, Luke BT, Frisch MJ, Binkley JS. Theoretical thermochemistry. 1. Heats of formation of neutral AHn molecules (A = Li to Cl). J Phys Chem 1985; 89: 2198-2203.
| Crossref | Google Scholar |
15 Pople JA, Curtiss LA. Theoretical thermochemistry. 2. Ionization energies and proton affinities of AHn species (A = C to F and Si to Cl)—heats of formation of their cations. J Phys Chem 1987; 91: 155-162.
| Crossref | Google Scholar |
16 Pople JA, Head-Gordon M, Fox DJ, Raghavachari K, Curtiss LA. Gaussian-1 theory: a general procedure for prediction of molecular energies. J Chem Phys 1989; 90: 5622-5629.
| Crossref | Google Scholar |
17 Curtiss LA, Jones C, Trucks GW, Raghavachari K, Pople JA. Gaussian-1 theory of molecular-energies for 2nd-row compounds. J Chem Phys 1990; 93: 2537-2545.
| Crossref | Google Scholar |
18 Curtiss LA, Raghavachari K, Trucks GW, Pople JA. Gaussian-2 theory for molecular-energies of 1st-row and 2nd-row compounds. J Chem Phys 1991; 94: 7221-7230.
| Crossref | Google Scholar |
20 Curtiss LA, Raghavachari K, Redfern PC, Rassolov V, Pople JA. Gaussian-3 (G3) theory for molecules containing first and second-row atoms. J Chem Phys 1998; 109: 7764-7776.
| Crossref | Google Scholar |
21 Raghavachari K. Autobiography of Krishnan Raghavachari. J Phys Chem A 2024; 128: 2526-2533.
| Crossref | Google Scholar | PubMed |
22 Curtiss LA, Redfern PC, Raghavachari K. Gn theory. WIREs Comput Mol Sci 2011; 1: 810-825.
| Crossref | Google Scholar |
23 Curtiss LA, Raghavachari K, Redfern PC, Pople JA. Investigation of the use of B3LYP zero-point energies and geometries in the calculation of enthalpies of formation. Chem Phys Lett 1997; 270: 419-426.
| Crossref | Google Scholar |
24 Curtiss LA, Raghavachari K, Redfern PC, Baboul AG, Pople JA. Gaussian-3 theory using coupled cluster energies. Chem Phys Lett 1999; 314: 101-107.
| Crossref | Google Scholar |
25 Curtiss LA, Redfern PC, Raghavachari K. Gaussian-4 theory. J Chem Phys 2007; 126: 084108.
| Crossref | Google Scholar | PubMed |
26 Chan B, Karton A, Raghavachari K. G4(MP2)-XK: a variant of the G4(MP2)-6X composite method with expanded applicability for main group elements up to radon. J Chem Theory Comput 2019; 15: 4478-4484.
| Crossref | Google Scholar | PubMed |
27 Wan W, Karton A. Heat of formation for C60 by means of the G4(MP2) thermochemical protocol through reactions in which C60 is broken down into corannulene and sumanene. Chem Phys Lett 2016; 643: 34-38.
| Crossref | Google Scholar |
28 Karton A, Waite SL, Page AJ. Performance of DFT for C60 isomerization energies: a noticeable exception to Jacob’s Ladder. J Phys Chem A 2019; 123: 257-266.
| Crossref | Google Scholar | PubMed |
29 Henry DJ, Sullivan MB, Radom L. G3-RAD and G3X-RAD: modified Gaussian-3 (G3) and Gaussian-3X (G3X) procedures for radical thermochemistry. J Chem Phys 2003; 118: 4849-4860.
| Crossref | Google Scholar |
30 Chan B, Coote ML, Radom L. G4-SP, G4(MP2)-SP, G4-sc, and G4(MP2)-sc: modifications to G4 and G4(MP2) for the treatment of medium-sized radicals. J Chem Theory Comput 2010; 6: 2647-2653.
| Crossref | Google Scholar | PubMed |
31 Chan B, Deng J, Radom L. G4(MP2)-6X: a cost-effective improvement to G4(MP2). J Chem Theory Comput 2011; 7: 112-120.
| Crossref | Google Scholar | PubMed |
32 Karton A, O’Reilly RJ, Chan B, Radom L. Determination of barrier heights for proton exchange in small water, ammonia, and hydrogen fluoride clusters with G4(MP2)-type, MPn, and SCS-MPn procedures–a caveat. J Chem Theory Comput 2012; 8: 3128-3136.
| Crossref | Google Scholar | PubMed |
33 da Silva G. G3X-K theory: a composite theoretical method for thermochemical kinetics. Chem Phys Lett 2013; 558: 109-113.
| Crossref | Google Scholar |
34 Chan B, Karton A, Raghavachari K, Radom L. Restricted open-shell G4(MP2)-type procedures. J Phys Chem A 2016; 120: 9299-9304.
| Crossref | Google Scholar | PubMed |
35 Semidalas E, Martin JML. Canonical and DLPNO-based G4(MP2)XK-inspired composite wave function methods parametrized against large and chemically diverse training sets: are they more accurate and/or robust than double-hybrid DFT? J Chem Theory Comput 2020; 16: 4238-4255.
| Crossref | Google Scholar | PubMed |
36 Petersson GA, Bennett A, Tensfeldt TG, Al-Laham MA, Shirley WA, Mantzaris J. A complete basis set model chemistry. I. The total energies of closed-shell atoms and hydrides of the first-row elements. J Chem Phys 1988; 89: 2193-2218.
| Crossref | Google Scholar |
37 Petersson GA, Al-Laham MA. A complete basis set model chemistry. II. Open-shell systems and the total energies of the first-row atoms. J Chem Phys 1991; 94: 6081-6090.
| Crossref | Google Scholar |
38 Petersson GA, Bennett A, Tensfeldt TG. A complete basis set model chemistry. III. The complete basis set-quadratic configuration interaction family of methods. J Chem Phys 1991; 94: 6091-6101.
| Crossref | Google Scholar |
39 Montgomery JA, Ochterski JW, Petersson GA. A complete basis set model chemistry. IV. An improved atomic pair natural orbital method. J Chem Phys 1994; 101: 5900-5909.
| Crossref | Google Scholar |
40 Ochterski JW, Petersson GA, Montgomery JA. A complete basis set model chemistry. V. Extensions to six or more heavy atoms. J Chem Phys 1996; 104: 2598-2619.
| Crossref | Google Scholar |
41 Montgomery JA, Frisch MJ, Ochterski JW, Petersson GA. A complete basis set model chemistry. VI. Use of density functional geometries and frequencies. J Chem Phys 1999; 110: 2822-2827.
| Crossref | Google Scholar |
42 Wood GPF, Radom L, Petersson GA, Barnes EC, Frisch MJ, Montgomery JA. A restricted-open-shell complete-basis-set model chemistry. J Chem Phys 2006; 125: 094106.
| Crossref | Google Scholar | PubMed |
44 East ALL, Allen WD. The heat of formation of NCO. J Chem Phys 1993; 99: 4638-4650.
| Crossref | Google Scholar |
45 Klippenstein SJ, East ALL, Allen WD. A high level ab initio map and direct statistical treatment of the fragmentation of singlet ketene. J Chem Phys 1996; 105: 118-140.
| Crossref | Google Scholar |
46 Császár AG, Allen WD, Schaefer HF. In pursuit of the ab initio limit for conformational energy prototypes. J Chem Phys 1998; 108: 9751-9764.
| Crossref | Google Scholar |
47 Schuurman MS, Muir SR, Allen WD, Schaefer HF. Toward subchemical accuracy in computational thermochemistry: Focal point analysis of the heat of formation of NCO and [H,N,C,O] isomers. J Chem Phys 2004; 120: 11586-11599.
| Crossref | Google Scholar | PubMed |
48 Martin JML, de Oliveira G. Towards standard methods for benchmark quality ab initio thermochemistry—W1 and W2 theory. J Chem Phys 1999; 111: 1843-1856.
| Crossref | Google Scholar |
49 Boese AD, Oren M, Atasoylu O, Martin JML, Kallay M, Gauss J. W3 theory: robust computational thermochemistry in the kJ/mol accuracy range. J Chem Phys 2004; 120: 4129-4141.
| Crossref | Google Scholar | PubMed |
50 Karton A, Rabinovich E, Martin JML, Ruscic B. W4 theory for computational thermochemistry: in pursuit of confident sub-kJ/mol predictions. J Chem Phys 2006; 125: 144108.
| Crossref | Google Scholar | PubMed |
51 Karton A, Taylor PR, Martin JML. Basis set convergence of post-CCSD contributions to molecular atomization energies. J Chem Phys 2007; 127: 064104.
| Crossref | Google Scholar | PubMed |
52 Karton A, Martin JML. Explicitly correlated Wn theory: W1-F12 and W2-F12. J Chem Phys 2012; 136: 124114.
| Crossref | Google Scholar | PubMed |
53 Sylvetsky N, Peterson KA, Karton A, Martin JML. Toward a W4-F12 approach: Can explicitly correlated and orbital-based ab initio CCSD(T) limits be reconciled? J Chem Phys 2016; 144: 214101.
| Crossref | Google Scholar | PubMed |
54 Chan B, Radom L. W1X-1 and W1X-2: W1-quality accuracy with an order of magnitude reduction in computational cost. J Chem Theory Comput 2012; 8: 4259-4269.
| Crossref | Google Scholar | PubMed |
55 Chan B, Radom L. W3X: a cost-effective post-CCSD (T) composite procedure. J Chem Theory Comput 2013; 9: 4769-4778.
| Crossref | Google Scholar | PubMed |
56 Chan B, Radom L. W2X and W3X-L: cost-effective approximations to W2 and W4 with kJ mol–1 accuracy. J Chem Theory Comput 2015; 11: 2109-2119.
| Crossref | Google Scholar | PubMed |
57 Fast PL, Sanchez ML, Truhlar DG. Multi-coefficient Gaussian-3 method for calculating potential energy surfaces. Chem Phys Lett 1999; 306: 407-410.
| Crossref | Google Scholar |
58 Fast PL, Corchado JC, Sanchez ML, Truhlar DG. Multi-coefficient correlation method for quantum chemistry. J Phys Chem 1999; 103: 5129-5136.
| Crossref | Google Scholar |
59 Fast PL, Truhlar DG. MC-QCISD: multi-coefficient correlation method based on quadratic configuration interaction with single and double excitations. J Phys Chem A 2000; 104: 6111-6116.
| Crossref | Google Scholar |
60 Lynch BJ, Truhlar DG. Robust and affordable multicoefficient methods for thermochemistry and thermochemical kinetics: the MCCM/3 suite and SAC/3. J Phys Chem A 2003; 107: 3898-3906.
| Crossref | Google Scholar |
61 Lynch BJ, Zhao Y, Truhlar DG. The 6-31B(d) basis set and the BMC-QCISD and BMC-CCSD multicoefficient correlation methods. J Phys Chem A 2005; 109: 1643-1649.
| Crossref | Google Scholar | PubMed |
62 Zhang W, Kong X, Liu S, Zhao Y. Multi-coefficients correlation methods. WIREs Comput Mol Sci 2020; 10: e1474.
| Crossref | Google Scholar |
63 Tajti A, Szalay PG, Császár AG, Kállay M, Gauss J, Valeev EF, Flowers BA, Vázquez J, Stanton JF. HEAT: high accuracy extrapolated ab initio thermochemistry. J Chem Phys 2004; 121: 11599-11613.
| Crossref | Google Scholar | PubMed |
64 Bomble YJ, Vázquez J, Kállay M, Michauk C, Szalay PG, Császár AG, Gauss J, Stanton JF. High-accuracy extrapolated ab initio thermochemistry. II. Minor improvements to the protocol and a vital simplification. J Chem Phys 2006; 125: 064108.
| Crossref | Google Scholar | PubMed |
65 Harding ME, Vázquez J, Ruscic B, Wilson AK, Gauss J, Stanton JF. High-accuracy extrapolated ab initio thermochemistry. III. Additional improvements and overview. J Chem Phys 2008; 128: 114111.
| Crossref | Google Scholar | PubMed |
66 Thorpe JH, Lopez CA, Nguyen TL, Baraban JH, Bross DH, Ruscic B, Stanton JF. High-accuracy extrapolated ab initio thermochemistry. IV. A modified recipe for computational efficiency. J Chem Phys 2019; 150: 224102.
| Crossref | Google Scholar | PubMed |
67 Thorpe JH, Kilburn JL, Feller D, Changala PB, Bross DH, Ruscic B, Stanton JF. Elaborated thermochemical treatment of HF, CO, N2, and H2O: Insight into HEAT and its extensions. J Chem Phys 2021; 155: 184109.
| Crossref | Google Scholar | PubMed |
68 DeYonker NJ, Cundari TR, Wilson AK. The correlation consistent composite approach (ccCA): an alternative to the Gaussian-n methods. J Chem Phys 2006; 124: 114104.
| Crossref | Google Scholar | PubMed |
69 DeYonker NJ, Peterson KA, Steyl G, Wilson AK, Cundari TR. Quantitative computational thermochemistry of transition metal species. J Phys Chem A 2007; 111: 11269-11277.
| Crossref | Google Scholar | PubMed |
70 DeYonker NJ, Williams TG, Imel AE, Cundari TR, Wilson AK. Accurate thermochemistry for transition metal complexes from first-principles calculations. J Chem Phys 2009; 131: 024106.
| Crossref | Google Scholar | PubMed |
71 Mintz B, Williams TG, Howard L, Wilson AK. Computation of potential energy surfaces with the multireference correlation consistent composite approach. J Chem Phys 2009; 130: 234104.
| Crossref | Google Scholar | PubMed |
72 DeYonker NJ, Wilson BR, Pierpont AW, Cundari TR, Wilson AK. Towards the intrinsic error of the correlation consistent composite approach (ccCA). Mol Phys 2009; 107: 1107-1121.
| Crossref | Google Scholar |
73 Prascher BP, Lai JD, Wilson AK. The resolution of the identity approximation applied to the correlation consistent composite approach. J Chem Phys 2009; 131: 044130.
| Crossref | Google Scholar | PubMed |
74 Laury ML, DeYonker NJ, Jiang W, Wilson AK. A pseudopotential-based composite method: the relativistic pseudopotential correlation consistent composite approach for molecules containing 4d transition metals (Y–Cd). J Chem Phys 2011; 135: 214103.
| Crossref | Google Scholar | PubMed |
75 Laury ML, Wilson AK. Examining the heavy p-block with a pseudopotential-based composite method: atomic and molecular applications of rp-ccCA. J Chem Phys 2012; 137: 214111.
| Crossref | Google Scholar | PubMed |
76 Mahler A, Wilson AK. Explicitly correlated methods within the ccCA methodology. J Chem Theory Comput 2013; 9: 1402-1407.
| Crossref | Google Scholar | PubMed |
77 Welch BK, Almeida NMS, Wilson AK. Super ccCA (s-ccCA): an approach for accurate transition metal thermochemistry. Mol Phys 2021; 119: e1963001.
| Crossref | Google Scholar |
80 Feller D, Peterson KA, Dixon DA. Refined theoretical estimates of the atomization energies and molecular structures of selected small oxygen fluorides. J Phys Chem A 2010; 114: 613-623.
| Crossref | Google Scholar | PubMed |
81 Feller D, Peterson KA, Dixon DA. Ab initio coupled cluster determination of the heats of formation of C2H2F2, C2F2, and C2F4. J Phys Chem A 2011; 115: 1440-1451.
| Crossref | Google Scholar | PubMed |
82 Feller D, Peterson KA, Dixon DA. Further benchmarks of a composite, convergent, statistically calibrated coupled cluster-based approach for thermochemical and spectroscopic studies. Mol Phys 2012; 110: 2381-2399.
| Crossref | Google Scholar |
83 Feller D, Peterson KA, Ruscic B. Improved accuracy benchmarks of small molecules using correlation consistent basis sets. Theor Chem Acc 2014; 133: 1407.
| Crossref | Google Scholar |
84 Feller D. Estimating the intrinsic limit of the Feller–Peterson–Dixon composite approach when applied to adiabatic ionization potentials in atoms and small molecules. J Chem Phys 2017; 147: 034103.
| Crossref | Google Scholar | PubMed |
85 Bakowies D. Ab initio thermochemistry using optimal-balance models with isodesmic corrections: the ATOMIC protocol. J Chem Phys 2009; 130: 144113.
| Crossref | Google Scholar | PubMed |
86 Bakowies D. Estimating systematic error and uncertainty in ab initio thermochemistry. I. Atomization energies of hydrocarbons in the ATOMIC(HC) protocol. J Chem Theory Comput 2019; 15: 5230-5251.
| Crossref | Google Scholar | PubMed |
87 Bakowies D. Estimating systematic error and uncertainty in ab initio thermochemistry: II. ATOMIC(HC) enthalpies of formation for a large set of hydrocarbons. J Chem Theory Comput 2020; 16: 399-426.
| Crossref | Google Scholar | PubMed |
88 Vogiatzis KD, Haunschild R, Klopper W. Accurate atomization energies from combining coupled-cluster computations with interference-corrected explicitly correlated second-order perturbation theory. Theor Chem Acc 2014; 133: 1446.
| Crossref | Google Scholar |
89 Alessandrini S, Barone V, Puzzarini C. Extension of the “cheap” composite approach to noncovalent interactions: the jun-ChS scheme. J Chem Theory Comput 2020; 16: 988-1006.
| Crossref | Google Scholar | PubMed |
90 Lupi J, Alessandrini S, Puzzarini C, Barone V. junChS and junChS-F12 models: parameter-free efficient yet accurate composite schemes for energies and structures of noncovalent complexes. J Chem Theory Comput 2021; 17: 6974-6992.
| Crossref | Google Scholar | PubMed |
92 Karton A. A computational chemist’s guide to accurate thermochemistry for organic molecules. WIREs Comput Mol Sci 2016; 6: 292-310.
| Crossref | Google Scholar |
94 Chan B. How to computationally calculate thermochemical properties objectively, accurately, and as economically as possible. Pure Appl Chem 2017; 89: 699-713.
| Crossref | Google Scholar |
96 Feller D, Peterson KA, Dixon DA. A survey of factors contributing to accurate theoretical predictions of atomization energies and molecular structures. J Chem Phys 2008; 129: 204105.
| Crossref | Google Scholar | PubMed |
97 Peterson KA, Feller D, Dixon DA. Chemical accuracy in ab initio thermochemistry and spectroscopy: current strategies and future challenges. Theor Chem Acc 2012; 131: 1079.
| Crossref | Google Scholar |
99 Klopper W, Bachorz RA, Hättig C, Tew DP. Accurate computational thermochemistry from explicitly correlated coupled-cluster theory. Theor Chem Acc 2010; 126: 289-304.
| Crossref | Google Scholar |
100 DeYonker N, Cundari TR, Wilson AK. The correlation consistent composite approach (ccCA): efficient and pan-periodic kinetics and thermodynamics. In: Piecuch P, Maruani J, Delgado-Barrio G, Wilson S, editors. Advances in the Theory of Atomic and Molecular Systems (Progress in Theoretical Chemistry and Physics). Vol. 19. Dordrecht, Netherlands: Springer; 2009. pp. 197–224.
101 Helgaker T, Klopper W, Tew DP. Quantitative quantum chemistry. Mol Phys 2008; 106: 2107.
| Crossref | Google Scholar |
102 Helgaker T, Klopper W, Bak KL, Halkier A, Jørgensen P, Olsen J. Highly accurate ab initio computation of thermochemical data. In: Cioslowski J, editor. Understanding Chemical Reactivity, Vol. 22: Quantum–Mechanical Prediction of Thermochemical Data. Dordrecht, Netherlands: Kluwer; 2001. pp. 1–30.
105 Karton A, Goerigk L. Accurate reaction barrier heights of pericyclic reactions: surprisingly large deviations for the CBS-QB3 composite method and their consequences in DFT benchmark studies. J Comput Chem 2015; 36: 622-632.
| Crossref | Google Scholar | PubMed |
106 Yu LJ, Sarrami F, O’Reilly RJ, Karton A. Reaction barrier heights for cycloreversion of heterocyclic rings: an Achilles’ heel for DFT and standard ab initio procedures. Chem Phys 2015; 458: 1-8.
| Crossref | Google Scholar |
107 Karton A, Martin JML. Prototypical π–π dimers re-examined by means of high-level CCSDT(Q) composite ab initio methods. J Chem Phys 2021; 154: 124117.
| Crossref | Google Scholar | PubMed |
108 Curtiss LA, Redfern PC, Raghavachari K. Gaussian-4 theory using reduced order perturbation theory. J Chem Phys 2007; 127: 124105.
| Crossref | Google Scholar | PubMed |
109 Karton A. Fullerenes pose a strain on hybrid density functional theory. J Phys Chem A 2022; 126: 4709-4720.
| Crossref | Google Scholar | PubMed |
110 Karton A, Chan B. Performance of local G4(MP2) composite ab initio procedures for fullerene isomerization energies. Comput Theor Chem 2022; 1217: 113874.
| Crossref | Google Scholar |
111 Karton A. Big data benchmarking: how do DFT methods across the rungs of Jacob’s Ladder perform for a dataset of 122k CCSD(T) total atomization energies? Phys Chem Chem Phys 2024; 26: 14594-14606.
| Crossref | Google Scholar | PubMed |
112 Dunning TH. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J Chem Phys 1989; 90: 1007-1023.
| Crossref | Google Scholar |
113 Kendall RA, Dunning TH, Harrison RJ. Electron affinities of the first-row atoms revisited. Systematic basis sets and wave functions. J Chem Phys 1992; 96: 6796-6806.
| Crossref | Google Scholar |
114 Dunning TH, Peterson KA, Wilson AK. Gaussian basis sets for use in correlated molecular calculations. X. The atoms aluminum through argon revisited. J Chem Phys 2001; 114: 9244-9253.
| Crossref | Google Scholar |
115 Manaa MR, Fried LE, Kuo I-FW. Determination of enthalpies of formation of energetic molecules with composite quantum chemical methods. Chem Phys Lett 2016; 648: 31-35.
| Crossref | Google Scholar |
116 Fogueri UR, Kozuch S, Karton A, Martin JML. The melatonin conformer space: benchmark and assessment of wavefunction and DFT methods for a paradigmatic biological and pharmacological molecule. J Phys Chem A 2013; 117: 2269-2277.
| Crossref | Google Scholar | PubMed |
117 Jorgensen KR, Oyedepo GA, Wilson AK. Highly energetic nitrogen species: reliable energetics via the correlation consistent composite approach (ccCA). J Hazard Mater 2011; 186: 583-589.
| Crossref | Google Scholar | PubMed |
118 Barnes EC, Petersson GA, Montgomery JA, Jr, Frisch MJ, Martin JML. Unrestricted coupled cluster and brueckner doubles variations of W1 theory. J Chem Theory Comput 2009; 5: 2687-2693.
| Crossref | Google Scholar | PubMed |
119 Peterson KA, Adler TB, 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.
| Crossref | Google Scholar | PubMed |
120 Papajak E, Truhlar DG. Convergent partially augmented basis sets for post-Hartree–Fock calculations of molecular properties and reaction barrier heights. J Chem Theory Comput 2011; 7: 10-18.
| Crossref | Google Scholar | PubMed |
121 Karton A, Chan B, Raghavachari K, Radom L. Evaluation of the heats of formation of corannulene and C60 by means of high-level theoretical procedures. J Phys Chem A 2013; 117: 1834-1842.
| Crossref | Google Scholar | PubMed |
122 Karton A, Schreiner PR, Martin JML. Heats of formation of platonic hydrocarbon cages by means of high-level thermochemical procedures. J Comput Chem 2016; 37: 49-58.
| Crossref | Google Scholar | PubMed |
123 Karton A, Chan B. Accurate heats of formation for polycyclic aromatic hydrocarbons: a high-level ab initio perspective. J Chem Eng Data 2021; 66: 3453-3462.
| Crossref | Google Scholar |
124 Karton A, Yu L-J, Kesharwani MK, Martin JML. Heats of formation of the amino acids re-examined by means of W1-F12 and W2-F12 theories. Theor Chem Acc 2014; 133: 1483.
| Crossref | Google Scholar |
125 Karton A, Kaminker I, Martin JML. Economical post-CCSD(T) computational thermochemistry protocol and applications to some aromatic compounds. J Phys Chem A 2009; 113: 7610-7620.
| Crossref | Google Scholar | PubMed |
126 Karton A. Cope rearrangements in shapeshifting molecules re-examined by means of high-level CCSDT(Q) composite ab initio methods. Chem Phys Lett 2020; 759: 138018.
| Crossref | Google Scholar |
127 Karton A. High-level thermochemistry for the octasulfur ring: a converged coupled cluster perspective for a challenging second-row system. Chem Phys Impact 2021; 3: 100047.
| Crossref | Google Scholar |
128 Kroeger AA, Karton A. Thermochemistry of phosphorus sulfide cages: an extreme challenge for high-level ab initio methods. Struct Chem 2019; 30: 1665-1675.
| Crossref | Google Scholar |
129 Karton A, Sylvetsky N, Martin JML. W4-17: a diverse and high-confidence dataset of atomization energies for benchmarking high-level electronic structure methods. J Comput Chem 2017; 38: 2063-2075.
| Crossref | Google Scholar | PubMed |
130 Karton A, Daon S, Martin JML. W4-11: a high-confidence benchmark dataset for computational thermochemistry derived from first-principles W4 data. Chem Phys Lett 2011; 510: 165-178.
| Crossref | Google Scholar |
131 Martin JML. Electron correlation: nature’s weird and wonderful chemical glue. Isr J Chem 2022; 62: e202100111.
| Crossref | Google Scholar |
132 Boese AD, Klopper W, Martin JML. Anharmonic force fields and thermodynamic functions using density functional theory. Mol Phys 2005; 103: 863-876.
| Crossref | Google Scholar |
133 Boese AD, Martin JML. Vibrational spectra of the azabenzenes revisited: anharmonic force fields. J Phys Chem A 2004; 108: 3085-3096.
| Crossref | Google Scholar |
134 Karton A, Martin JML. The lowest singlet-triplet excitation energy of BN: a converged coupled cluster perspective. J Chem Phys 2006; 125: 144313.
| Crossref | Google Scholar | PubMed |
135 Karton A, Tarnopolsky A, Martin JML. Atomization energies of the carbon clusters Cn (n = 2–10) revisited by means of W4 theory as well as density functional, Gn, and CBS methods. Mol Phys 2009; 107: 977-990.
| Crossref | Google Scholar |
136 Karton A. Basis set convergence of high-order coupled cluster methods up to CCSDTQ567 for a highly multireference molecule. Chem Phys Lett 2019; 737: 136810.
| Crossref | Google Scholar |
137 Karton A. Post-CCSD(T) contributions to total atomization energies in multireference systems. J Chem Phys 2018; 149: 034102.
| Crossref | Google Scholar | PubMed |
138 Ruscic B. Uncertainty quantification in thermochemistry, benchmarking electronic structure computations, and active thermochemical tables. Int J Quantum Chem 2014; 114: 1097-1101.
| Crossref | Google Scholar |
140 Wheeler SE. Homodesmotic reactions for thermochemistry. WIREs Comput Mol Sci 2012; 2: 204-220.
| Crossref | Google Scholar |
141 Chan B, Collins E, Raghavachari K. Applications of isodesmic-type reactions for computational thermochemistry. WIREs Comput Mol Sci 2021; 11: e1501.
| Crossref | Google Scholar |
142 Wheeler SE, Houk KN, Schleyer PvR, Allen WD. A hierarchy of homodesmotic reactions for thermochemistry. J Am Chem Soc 2009; 131: 2547-2560.
| Crossref | Google Scholar | PubMed |
143 Yu L-J, Karton A. Assessment of theoretical procedures for a diverse set of isomerization reactions involving double-bond migration in conjugated dienes. Chem Phys 2014; 441: 166-177.
| Crossref | Google Scholar |
144 Karton A. How reliable is DFT in predicting relative energies of polycyclic aromatic hydrocarbon isomers? Comparison of functionals from different rungs of Jacob’s Ladder. J Comput Chem 2017; 38: 370-382.
| Crossref | Google Scholar | PubMed |
145 Karton A, Chan B. PAH335 – a diverse database of highly accurate CCSD(T) isomerization energies of 335 polycyclic aromatic hydrocarbons. Chem Phys Lett 2023; 824: 140544.
| Crossref | Google Scholar |
146 Ramabhadran RO, Raghavachari K. Theoretical thermochemistry for organic molecules: development of the generalized connectivity-based hierarchy. J Chem Theory Comput 2011; 7: 2094-2103.
| Crossref | Google Scholar | PubMed |
147 Ramabhadran RO, Raghavachari K. Connectivity-based hierarchy for theoretical thermochemistry: assessment using wave function-based methods. J Phys Chem A 2012; 116: 7531-7537.
| Crossref | Google Scholar | PubMed |
148 Ramabhadran RO, Raghavachari K. The successful merger of theoretical thermochemistry with fragment-based methods in quantum chemistry. Acc Chem Res 2014; 47: 3596-3604.
| Crossref | Google Scholar | PubMed |
149 Boese AD, Martin JML. Vibrational spectra of the azabenzenes revisited: anharmonic force fields. J Phys Chem A 2004; 108: 3085-3096.
| Crossref | Google Scholar |
150 Heckert M, Kállay M, Tew DP, Klopper W, Gauss J. Basis-set extrapolation techniques for the accurate calculation of molecular equilibrium geometries using coupled-cluster theory. J Chem Phys 2006; 125: 44108.
| Crossref | Google Scholar | PubMed |
151 Tew DP, Klopper W, Heckert M, Gauss J. Basis set limit CCSD(T) harmonic vibrational frequencies. J Phys Chem A 2007; 111: 11242-11248.
| Crossref | Google Scholar | PubMed |
152 Puzzarini C, Heckert M, Gauss J. The accuracy of rotational constants predicted by high-level quantum-chemical calculations. I. Molecules containing first-row atoms. J Chem Phys 2008; 128: 194108.
| Crossref | Google Scholar | PubMed |
153 Puzzarini C. Extrapolation to the complete basis set limit of structural parameters: comparison of different approaches. J Phys Chem A 2009; 113: 14530-14535.
| Crossref | Google Scholar | PubMed |
154 Karton A, Martin JML. Performance of W4 theory for spectroscopic constants and electrical properties of small molecules. J Chem Phys 2010; 133: 144102.
| Crossref | Google Scholar | PubMed |
155 Puzzarini C, Barone V. Extending the molecular size in accurate quantum-chemical calculations: the equilibrium structure and spectroscopic properties of uracil. Phys Chem Chem Phys 2011; 13: 7189-7197.
| Crossref | Google Scholar | PubMed |
156 Puzzarini C, Stanton JF. Connections between the accuracy of rotational constants and equilibrium molecular structures. Phys Chem Chem Phys 2023; 25: 1421-1429.
| Crossref | Google Scholar | PubMed |
157 Franke PR, Stanton JF. Rotamers of methanediol: composite ab initio predictions of structures, frequencies, and rovibrational constants. J Phys Chem A 2023; 127: 924-937.
| Crossref | Google Scholar | PubMed |
158 Spiegel M, Semidalas E, Martin JML, Bentley MR, Stanton JF. Post-CCSD(T) corrections to bond distances and vibrational frequencies: the power of Λ. Mol Phys 2023; 122: e2252114.
| Crossref | Google Scholar |
159 Christiansen O, Coriani S, Gauss J, Hattig C, Jorgensen P, Pawlowski F, Rizzo A. Accurate nonlinear optical properties for small molecules. In: Papadopoulos MG, Sadlej AJ, Leszczynski J, editors. Non-linear optical properties of matter: from molecules to condensed phases. Dordrecht, Netherlands: Springer; 2006. pp. 51–99. 10.1007/1-4020-4850-5_2
160 Curtiss LA, Raghavachari K, Redfern PC, Pople JA. Assessment of Gaussian-2 and density functional theories for the computation of enthalpies of formation. J Chem Phys 1997; 106: 1063-1079.
| Crossref | Google Scholar |
161 Curtiss LA, Raghavachari K, Redfern PC, Pople JA. Assessment of Gaussian-3 and density functional theories for a larger experimental test set. J Chem Phys 2000; 112: 7374-7383.
| Crossref | Google Scholar |
162 Curtiss LA, Redfern PC, Raghavachari K. Assessment of Gaussian-3 and density-functional theories on the G3/05 test set of experimental energies. J Chem Phys 2005; 123: 124107.
| Crossref | Google Scholar | PubMed |
163 Karton A, de Oliveira MT. Good practices in database generation for benchmarking DFT. WIREs Comput Mol Sci 2024; 15: e1737.
| Crossref | Google Scholar |
164 Karton A, Gruzman D, Martin JML. Benchmark thermochemistry of the CnH2n + 2 alkane isomers (n = 2–8) and performance of DFT and composite ab initio methods for dispersion-driven isomeric equilibria. J Phys Chem A 2009; 113: 8434-8447.
| Crossref | Google Scholar | PubMed |
165 Karton A, Martin JML. Explicitly correlated benchmark calculations on C8H8 isomer energy separations: how accurate are DFT, double-hybrid and composite ab initio procedures? Mol Phys 2012; 110: 2477-2491.
| Crossref | Google Scholar |
166 Gruzman D, Karton A, Martin JML. Performance of ab initio and density functional methods for conformational equilibria of CnH2n+2 alkane isomers (n = 4–8). J Phys Chem A 2009; 113: 11974-11983.
| Crossref | Google Scholar | PubMed |
167 Fogueri UR, Kozuch S, Karton A, Martin JML. The melatonin conformer space: benchmark and assessment of wave function and DFT methods for a paradigmatic biological and pharmacological molecule. J Phys Chem A 2013; 117: 2269-2277.
| Crossref | Google Scholar | PubMed |
168 Reha D, Valdés H, Vondrásek J, Hobza P, Abu-Riziq A, Crews B, de Vries MS. Structure and IR spectrum of phenylalanyl-glycyl-glycine tripetide in the gas-phase: IR/UV experiments, ab initio quantum chemical calculations, and molecular dynamic simulations. Chem Eur J 2005; 11: 6803-6817.
| Crossref | Google Scholar | PubMed |
169 Csonka GI, French AD, Johnson GP, Stortz CA. Evaluation of density functionals and basis sets for carbohydrates. J Chem Theory Comput 2009; 5: 679-692.
| Crossref | Google Scholar | PubMed |
170 Wilke JJ, Lind MC, Schaefer HF, Császár AG, Allen WD. Conformers of gaseous cysteine. J Chem Theory Comput 2009; 5: 1511-1523.
| Crossref | Google Scholar | PubMed |
171 Johnson ER, Mori-Sánchez P, Cohen AJ, Yang W. Delocalization errors in density functionals and implications for main-group thermochemistry. J Chem Phys 2008; 129: 204112.
| Crossref | Google Scholar | PubMed |
172 Krieg H, Grimme S. Thermochemical benchmarking of hydrocarbon bond separation reaction energies: Jacob’s Ladder is not reversed! Mol Phys 2010; 108: 2655-2666.
| Crossref | Google Scholar |
173 Korth M, Grimme S. “Mindless” DFT benchmarking. J Chem Theory Comput 2009; 5: 993-1003.
| Crossref | Google Scholar | PubMed |
174 Karton A. Highly accurate CCSDT(Q)/CBS reaction barrier heights for a diverse set of transition structures: basis set convergence and cost-effective approaches for estimating post-CCSD(T) contributions. J Phys Chem A 2019; 123: 6720-6732.
| Crossref | Google Scholar | PubMed |
175 Grimme S, Ehrlich S, Goerigk L. Effect of the damping function in dispersion corrected density functional theory. J Comput Chem 2011; 32: 1456-1465.
| Crossref | Google Scholar | PubMed |
176 Guner V, Khuong KS, Leach AG, Lee PS, Bartberger MD, Houk KN. A standard set of pericyclic reactions of hydrocarbons for the benchmarking of computational methods: the performance of ab initio, density functional, CASSCF, CASPT2, and CBS-QB3 methods for the prediction of activation barriers, reaction energetics, and transition state geometries. J Phys Chem A 2003; 107: 11445-11459.
| Crossref | Google Scholar |
177 Ess DH, Houk KN. Activation energies of pericyclic reactions: performance of DFT, MP2, and CBS-QB3 methods for the prediction of activation barriers and reaction energetics of 1,3-dipolar cycloadditions, and revised activation enthalpies for a standard set of hydrocarbon pericyclic reactions. J Phys Chem A 2005; 109: 9542-9553.
| Crossref | Google Scholar | PubMed |
178 Grimme S, Mück-Lichtenfeld C, Würthwein E-U, Ehlers AW, Goumans TPM, Lammertsma K. Consistent theoretical description of 1,3-dipolar cycloaddition reactions. J Phys Chem A 2006; 110: 2583-2586.
| Crossref | Google Scholar | PubMed |
179 Dinadayalane TC, Vijaya R, Smitha A, Sastry GN. Diels–Alder reactivity of butadiene and cyclic five-membered dienes ((CH)4X, X = CH2, SiH2, O, NH, PH, and S) with ethylene: a benchmark study. J Phys Chem A 2002; 106: 1627-1633.
| Crossref | Google Scholar |
180 Zhao Y, Lynch BJ, Truhlar DG. Development and assessment of a new hybrid density functional model for thermochemical kinetics. J Phys Chem A 2004; 108: 2715-2719.
| Crossref | Google Scholar |
181 Zhao Y, González-García N, Truhlar DG. Benchmark database of barrier heights for heavy atom transfer, nucleophilic substitution, association, and unimolecular reactions and its use to test theoretical methods. J Phys Chem A 2005; 109: 2012-2018.
| Crossref | Google Scholar | PubMed |
182 Goerigk L, Grimme S. A general database for main group thermochemistry, kinetics, and noncovalent interactions – assessment of common and reparameterized (meta-)GGA density functionals. J Chem Theory Comput 2010; 6: 107-126.
| Crossref | Google Scholar | PubMed |
183 Neese F, Schwabe T, Kossmann S, Schirmer B, Grimme S. Assessment of orbital-optimized, spin-component scaled second-order many-body perturbation theory for thermochemistry and kinetics. J Chem Theory Comput 2009; 5: 3060-3073.
| Crossref | Google Scholar | PubMed |
184 Izgorodina EI, Coote ML, Radom L. Trends in R–X bond dissociation energies (R = Me, Et, i-Pr, t-Bu; X = H, CH3, OCH3, OH, F): a surprising shortcoming of density functional. J Phys Chem A 2005; 109: 7558-7566.
| Crossref | Google Scholar | PubMed |
185 Coote ML, Lin CY, Beckwith ALJ, Zavitsas AA. A comparison of methods for measuring relative radical stabilities of carbon-centred radicals. Phys Chem Chem Phys 2010; 12: 9597-9610.
| Crossref | Google Scholar | PubMed |
186 Goerigk L, Grimme S. A thorough benchmark of density functional methods for general main group thermochemistry, kinetics, and noncovalent interactions. Phys Chem Chem Phys 2011; 13: 6670-6688.
| Crossref | Google Scholar | PubMed |
187 Goerigk L, Hansen A, Bauer C, Ehrlich S, Najibi A, Grimme S. A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions. Phys Chem Chem Phys 2017; 19: 32184-32215.
| Crossref | Google Scholar | PubMed |
188 Mardirossian N, Head-Gordon M. Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals. Mol Phys 2017; 115: 2315-2372.
| Crossref | Google Scholar |
190 Ramakrishnan R, Dral PO, Rupp M, von Lilienfeld OA. Quantum chemistry structures and properties of 134 kilo molecules. Sci Data 2014; 1: 140022.
| Crossref | Google Scholar | PubMed |
191 Huang B, von Lilienfeld OA. Ab initio machine learning in chemical compound space. Chem Rev 2021; 121: 10001-10036.
| Crossref | Google Scholar | PubMed |
192 Manna D, Martin JML. What are the ground state structures of C20 and C24? An explicitly correlated ab initio approach. J Phys Chem A 2016; 120: 153-160.
| Crossref | Google Scholar | PubMed |
193 Feyereisen MW, Fitzgerald G, Kormornicki A. Use of approximate integrals in ab initio theory. An application in MP2 energy calculations. Chem Phys Lett 1993; 208: 359-363.
| Crossref | Google Scholar |
194 Vahtras O, Almlof J, Feyereisen MW. Integral approximations for LCAO-SCF calculations. Chem Phys Lett 1993; 213: 514-518.
| Crossref | Google Scholar |
195 Kendall R, Fruchtl HA. The impact of the resolution of the identity approximate integral method on modern ab initio algorithm development. Theor Chim Acta 1997; 97: 158-163.
| Crossref | Google Scholar |
196 Weigend F, Haser M, Patzelt H, Ahlrichs R. RI-MP2: optimized auxiliary basis sets and demonstration of efficiency. Chem Phys Lett 1998; 294: 143-152.
| Crossref | Google Scholar |
197 Klopper W. Highly accurate coupled-cluster singlet and triplet pair energies from explicitly correlated calculations in comparison with extrapolation techniques. Mol Phys 2001; 99: 481-507.
| Crossref | Google Scholar |
198 Ten-no S. Initiation of explicitly correlated Slater-type geminal theory. Chem Phys Lett 2004; 398: 56-61.
| Crossref | Google Scholar |
199 Klopper W, Manby FR, Ten-no S, Valeev EF. R12 methods in explicitly correlated molecular electronic structure theory. Int Rev Phys Chem 2006; 25: 427-468.
| Crossref | Google Scholar |
200 Werner H-J, Adler TB, Manby FR. General orbital invariant MP2-F12 theory. J Chem Phys 2007; 126: 164102.
| Crossref | Google Scholar | PubMed |
201 Ten-no S, Noga J. Explicitly correlated electronic structure theory from R12/F12 ansätze. WIREs Comput Mol Sci 2012; 2: 114-125.
| Crossref | Google Scholar |
202 Ma Q, Werner H-J. Explicitly correlated local coupled-cluster methods using pair natural orbitals. WIREs Comput Mol Sci 2018; 8: e1371.
| Crossref | Google Scholar |
203 Riplinger C, Neese F. An efficient and near linear scaling pair natural orbital based local coupled cluster method. J Chem Phys 2013; 138: 034106.
| Crossref | Google Scholar | PubMed |
204 Riplinger C, Sandhoefer B, Hansen A, Neese F. Natural triple excitations in local coupled cluster calculations with pair natural orbitals. J Chem Phys 2013; 139: 134101.
| Crossref | Google Scholar | PubMed |
205 Nagy PR, 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.
| Crossref | Google Scholar | PubMed |
206 Nagy PR, 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: 4193-4215.
| Crossref | Google Scholar | PubMed |
207 Nagy PR, 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: 5275-5298.
| Crossref | Google Scholar | PubMed |
208 Liakos DG, Sparta M, Kesharwani MK, Martin JML, Neese F. Exploring the accuracy limits of local pair natural orbital coupled cluster theory. J Chem Theory Comput 2015; 11: 1525-1539.
| Crossref | Google Scholar | PubMed |
209 Chan B, Karton A. Assessment of DLPNO-CCSD(T)-F12 and its use for the formulation of the low-cost and reliable L-W1X composite method. J Comput Chem 2022; 43: 1394-1402.
| Crossref | Google Scholar | PubMed |
210 Semidalas E, Martin JML. Canonical and DLPNO-based composite wavefunction methods parametrized against large and chemically diverse training sets. 2: Correlation-consistent basis sets, core−valence correlation, and F12 alternatives. J Chem Theory Comput 2020; 16: 7507-7524.
| Crossref | Google Scholar | PubMed |
211 Bursch M, Mewes J-M, Hansen A, Grimme S. Best-practice DFT protocols for basic molecular computational chemistry. Angew Chem Int Ed 2022; 61: e202205735.
| Crossref | Google Scholar | PubMed |
212 Karton A, Ruscic B, Martin JML. Benchmark atomization energy of ethane: importance of accurate zero-point vibrational energies and diagonal Born–Oppenheimer corrections for a ‘simple’ organic molecule. J Mol Struct Theochem 2007; 811: 345-353.
| Crossref | Google Scholar |
213 Jiang J, Ke L, Chen L, Dou B, Zhu Y, Liu J, Zhang B, Zhou T, Wei G-W. Transformer technology in molecular science. WIREs Comput Mol Sci 2024; 14: e1725.
| Crossref | Google Scholar |
214 Abraham BM, Jyothirmai MV, Sinha P, Viñes F, Singh JK, Illas F. Catalysis in the digital age: unlocking the power of data with machine learning. WIREs Comput Mol Sci 2024; 14: e1730.
| Crossref | Google Scholar |
215 Xue H, Cheng G, Yin W-J. Computational design of energy-related materials: from first-principles calculations to machine learning. WIREs Comput Mol Sci 2024; 14: e1732.
| Crossref | Google Scholar |
216 Dalmau D, Alegre-Requena JV. ROBERT: bridging the gap between machine learning and chemistry. WIREs Comput Mol Sci 2024; 14: e1733.
| Crossref | Google Scholar |
217 Back S, Aspuru-Guzik A, Ceriotti M, Grynova G, Grzybowski B, Gu GH, Hein J, Hippalgaonkar K, Hormázabal R, Jung Y, Kim S, Kim WY, Moosavi SM, Noh J, Park C, Schrier J, Schwaller P, Tsuda K, Vegge T, von Lilienfeld OA, Walsh A. Accelerated chemical science with AI. Digit Discov 2024; 3: 23-33.
| Crossref | Google Scholar | PubMed |
218 Bender A, Schneider N, Segler M, Patrick Walters W, Engkvist O, Rodrigues T. Evaluation guidelines for machine learning tools in the chemical sciences. Nat Rev Chem 2022; 6: 428-442.
| Crossref | Google Scholar | PubMed |
219 Meuwly M. Machine learning for chemical reactions. Chem Rev 2021; 121: 10218-10239.
| Crossref | Google Scholar | PubMed |
220 Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller K-R, Tkatchenko A. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem Rev 2021; 121: 9816-9872.
| Crossref | Google Scholar | PubMed |
221 Westermayr J, Gastegger M, Schütt KT, Maurer RJ. Perspective on integrating machine learning into computational chemistry and materials science. J Chem Phys 2021; 154: 230903.
| Crossref | Google Scholar | PubMed |
222 Tkatchenko A. Machine learning for chemical discovery. Nat Commun 2020; 11: 4125.
| Crossref | Google Scholar | PubMed |
223 von Lilienfeld OA, Müller K-R, Tkatchenko A. Exploring chemical compound space with quantum-based machine learning. Nat Rev Chem 2020; 4: 347-358.
| Crossref | Google Scholar | PubMed |
224 Strieth-Kalthoff F, Sandfort F, Segler MHS, Glorius F. Machine learning the ropes: principles, applications and directions in synthetic chemistry. Chem Soc Rev 2020; 49: 6154-6168.
| Crossref | Google Scholar | PubMed |
225 Yu HS, He X, Li SL, Truhlar DG. MN15: a Kohn–Sham global-hybrid exchange-correlation density functional with broad accuracy for multi-reference and single-reference systems and noncovalent interactions. Chem Sci 2016; 7: 5032-5051.
| Crossref | Google Scholar | PubMed |
226 Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM 2013; 65: 1501-1509.
| Crossref | Google Scholar |
227 Kirklin S, Saal EJ, Meredig B, Thompson A, Doak JW, Aykol M, Rühl S, Wolverton C. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. Npj Comput Mater 2015; 1: 15010.
| Crossref | Google Scholar |
228 Gubernatis JE, Lookman T. Machine learning in materials design and discovery: examples from the present and suggestions for the future. Phys Rev Mater 2018; 2: 120301.
| Crossref | Google Scholar |
229 Bhattacharjee H, Vlachos DG. Thermochemical data fusion using graph representation learning. J Chem Inf Model 2020; 60: 4673-4683.
| Crossref | Google Scholar | PubMed |
230 Li Q, Wittreich G, Wang Y, Bhattacharjee H, Gupta U, Vlachos DG. Accurate thermochemistry of complex lignin structures via density functional theory, group additivity, and machine learning. ACS Sustain Chem Eng 2021; 9: 3043-3049.
| Crossref | Google Scholar |
231 Bhattacharjee H, Anesiadis N, Vlachos DG. Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies. Sci Rep 2021; 11: 14372.
| Crossref | Google Scholar | PubMed |
232 Formalik FS, Kaihang J, Faramarz W, Xijun S, Randall Q. Exploring the structural, dynamic, and functional properties of metal–organic frameworks through molecular modeling. Adv Funct Mater 2024; 34: 2308130.
| Crossref | Google Scholar |
233 Rowe P, Deringer VL, Gasparotto P, Csányi G, Michaelides A. An accurate and transferable machine learning potential for carbon. J Chem Phys 2020; 153: 034702.
| Crossref | Google Scholar | PubMed |
234 Deringer VL, Caro MA, Csányi G. A general-purpose machine-learning force field for bulk and nanostructured phosphorus. Nat Commun 2020; 11: 5461.
| Crossref | Google Scholar | PubMed |
235 Milardovich D, Waldhoer D, Jech M, El-Sayed A-MB, Grasser T. Building robust machine learning force fields by composite Gaussian approximation potentials. Solid-State Electron 2023; 200: 108529.
| Crossref | Google Scholar |
236 Klawohn S, Darby JP, Kermode JR, Csányi G, Caro MA, Bartók AP. Gaussian approximation potentials: theory, software implementation and application examples. J Chem Phys 2023; 159: 174108.
| Crossref | Google Scholar | PubMed |
237 Nagai R, Akashi R, Sugino O. Completing density functional theory by machine learning hidden messages from molecules. npj Comput Mater 2020; 6: 43.
| Crossref | Google Scholar |
238 Kirkpatrick J, McMorrow B, Turban DHP, Gaunt AL, Spencer JS, Matthews AGDG, Obika A, Thiry L, Fortunato M, Pfau D, Castellanos LR, Petersen S, Nelson AWR, Kohli P, Mori-Sánchez P, Hassabis D, Cohen AJ. Pushing the frontiers of density functionals by solving the fractional electron problem. Science 2021; 374: 1385-1389.
| Crossref | Google Scholar | PubMed |
239 del Rio BG, Phan B, Ramprasad R. A deep learning framework to emulate density functional theory. npj Comput Mater 2023; 9: 158.
| Crossref | Google Scholar |
240 Xiao J, Chen Y, Zhang L, Wang H, Zhu T. A machine learning-based high-precision density functional method for drug-like molecules. Artificial Intelligence Chemistry 2024; 2(1): 100037.
| Crossref | Google Scholar |