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Updated: May 16, 2026

Interactive Molecular Model Assembly with 3D Printing

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

A General Accuracy Ladder for Molecular Structures: From Bonds to Interaction-Driven, Transferable Building Blocks.

Luigi Crisci¹, Lina Uribe^1,2, Federico Lazzari¹

¹Scuola Superiore Meridionale, Largo San Marcellino 10, 80138 Napoli, Italy.

The Journal of Physical Chemistry Letters

|May 14, 2026

Summary

This summary is machine-generated.

Accurate molecular structures are achieved by matching computational error scales to building blocks, not just increasing theory levels. This method improves predictions for complex systems like radicals and hydrogen bonds.

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Area of Science:

Computational chemistry
Quantum chemistry
Molecular modeling

Background:

Accurate structural interpretation is vital but challenging for systems with nonlocal effects.
Standard density functional methods struggle with delocalized open-shell and hydrogen-bonded systems due to inappropriate error scales.

Purpose of the Study:

To develop a new approach for accurate structural predictions in complex molecular systems.
To establish a direct link between error origin scale and computational theory level.

Main Methods:

Formulating transferable corrections based on interaction-driven building blocks instead of individual bonds.
Automatically identifying and assigning these blocks to an accuracy ladder.
Consistent treatment of vibrational averaging.

Main Results:

Quantitative agreement with experimental data for radicals, nonplanar aromatics, and hydrogen-bonded networks.
Reproducing experimental rotational constants within 0.1-0.2%.
Achieving root-mean-square atomic position deviations of 1-2 × 10^-3 Å.

Conclusions:

Matching the scale of error transferability with the appropriate level of electronic structure theory is key for accurate structures.
Multilevel computational strategies are effective for large systems with varying complexity.
This approach provides accurate structural predictions at an affordable computational cost.