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A Reliable and Inexpensive Flexible Molecule Crystal Structure Prediction Protocol Based on First Principles.
Rahul Nikhar1, Krzysztof Szalewicz1
1Department of Physics and Astronomy, University of Delaware, Newark, Delaware 19716, United States.
Journal of Chemical Theory and Computation
|October 10, 2025
Summary
Crystal structure prediction (CSP) for flexible molecules is unreliable with empirical force fields. A new method using ab initio force fields (aiFFs) accurately predicts crystal structures efficiently, with implications for materials science and biomolecular simulations.
Area of Science:
- Materials Science
- Computational Chemistry
- Crystallography
Background:
- Crystal structure prediction (CSP) is crucial for designing new materials.
- Current CSP methods often use empirical force fields (empFFs) or periodic density functional theory (pDFT+D).
- EmpFFs are unreliable for crystals with flexible monomers.
Purpose of the Study:
- To develop a reliable CSP method for flexible molecular crystals.
- To design and implement novel intramonomer force fields (intra-aiFFs) fitted to ab initio calculations.
- To improve the efficiency and accuracy of CSP protocols.
Main Methods:
- Developed intra-aiFFs by fitting to ab initio calculations for monomers.
- Combined intra-aiFFs with intermonomer aiFFs (inter-aiFFs) for full-dimensional CSP.
- Screened 1000 low-energy polymorphs using aiFFs, followed by pDFT+D calculations.
- Optimized top 100 polymorphs using pDFT+D.
Main Results:
- The novel aiFF-based CSP method accurately predicted the experimental crystal structure of 2-acetamido-4,5-dinitrotoluene as the second-ranked structure.
- This protocol achieved high accuracy at significantly lower computational cost compared to other reliable methods.
- The developed intra-aiFFs show promise for biomolecular simulations.
Conclusions:
- A new, reliable, and cost-effective CSP method for flexible molecular crystals has been established.
- The developed intra-aiFFs are crucial for accurate CSP of flexible systems.
- This approach has broad implications for materials discovery and computational drug design.

