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The Road to Affordable Accuracy beyond Small Molecules: From Energetics toward Molecular Structures
Vincenzo Barone1, Luigi Crisci2, Federico Lazzari2
1INSTM, via G. Giusti 9, 50121 Firenze, Italy.
Accounts of Chemical Research
|December 26, 2025
Summary
This study introduces a new framework for accurate molecular geometry prediction, combining quantum chemistry and machine learning to achieve spectroscopic accuracy for large molecules affordably. This approach enables precise molecular design and the development of sustainable materials.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Molecular Modeling
Background:
- Accurate molecular geometries are crucial for spectroscopy, thermochemistry, and molecular design.
- High-level quantum chemistry methods provide accuracy but are computationally expensive for large systems.
- Existing methods struggle to balance accuracy and computational feasibility for complex molecules.
Purpose of the Study:
- To develop an integrated framework for accurate and cost-effective prediction of molecular geometries.
- To bridge the gap between predictive accuracy and computational feasibility in computational chemistry.
- To enable rational molecular design and the development of new materials.
Main Methods:
- Combining composite quantum-chemical methods with data-driven corrections and fragment-based modeling.
- Utilizing explicitly correlated composite schemes for high accuracy on smaller systems (up to 20 atoms).
- Developing a benchmark geometry library (LCB25) and using it to train more affordable functionals (hybrid and double-hybrid) via linear regression and machine learning.
- Employing the Nano-LEGO platform for automated assembly of large molecular geometries from fragments.
Main Results:
- Achieved near-spectroscopic accuracy for molecular geometries of medium to large molecules (50-100 atoms) at a fraction of the cost of high-level methods.
- Demonstrated the transferability of accuracy from high-level methods to more affordable computational schemes.
- Successfully applied fragment-based modeling and machine learning corrections for accurate structural predictions.
Conclusions:
- The integrated framework provides a hierarchical, data-enriched ecosystem for accurate, transferable, and cost-effective molecular geometry prediction.
- This approach facilitates predictive spectroscopy, structure-based design, and the development of functional and sustainable materials.
- Openly available components promote transparency, accessibility, and community-driven development in computational chemistry.
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