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Efficient Estimation of Formation Enthalpies for Closed-Shell Organic Compounds with Local Coupled-Cluster Methods
Eugene Paulechka1, Andrei Kazakov1
1Thermodynamics Research Center, Applied Chemicals and Materials Division , National Institute of Standards and Technology , 325 Broadway , Boulder , Colorado 80305-3337 , United States.
This study introduces an efficient computational method for estimating formation enthalpies in organic compounds. The new approach achieves accuracy comparable to experiments, offering practical advantages for data validation and estimation.
Area of Science:
- Computational chemistry
- Thermochemistry
Background:
- Accurate determination of enthalpies of formation is crucial for understanding chemical reactions and properties.
- Existing computational methods often require significant resources or lack experimental accuracy.
Purpose of the Study:
- To investigate efficient computational schemes for estimating enthalpies of formation of closed-shell organic compounds.
- To assess the accuracy and uncertainty of local coupled-cluster with single, double, and perturbative triple excitation (CCSD(T)) approximations.
Main Methods:
- Utilized atom-equivalent-type computational schemes.
- Employed various local coupled-cluster approximations, including CCSD(T).
- Analyzed sources of uncertainty, such as frozen-core contributions and zero-point energy anharmonicity.
Main Results:
- The proposed estimation framework has a lower limit of expanded uncertainty of approximately 2 kJ·mol⁻¹.
- The best-performing computational schemes achieved an expanded uncertainty of about 2.5 kJ·mol⁻¹ when validated against 44 experimental values.
- Demonstrated computational efficiency and accuracy comparable to experimental measurements.
Conclusions:
- The developed computational methodology offers significant advantages for practical applications in chemistry.
- Enables prompt validation of experimental measurements, estimation of missing data, and resolution of conflicting experimental results.
- The approach eliminates the need for auxiliary reactions and additional experimental data, streamlining the process.
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