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Machine Learned Model for Solid Form Volume Estimation Based on Packing-Accessible Surface and Molecular Topological

Imanuel Bier1, Noa Marom1,2,3

  • 1Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

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We developed a machine learning model to predict homomolecular crystal volume using single-molecule structure. The PyMoVE package uses packing-accessible surface and topological fragments for accurate volume prediction.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Crystallography

Background:

  • Accurate prediction of crystal volume is crucial for materials design.
  • Existing methods for volume calculation have limitations in accuracy and computational efficiency.

Purpose of the Study:

  • To develop a novel machine learning model for predicting homomolecular crystal volume from single-molecule structures.
  • To implement this model in an open-source Python package, PyMoVE.

Main Methods:

  • Developed a new "projected marching cubes" algorithm for calculating packing-accessible surface volume.
  • Utilized molecular topological fragments to represent atomic bonding environments.
  • Employed feature selection to identify relevant fragments for the model.
  • Trained the model on data from the Cambridge Structural Database.

Main Results:

  • The "projected marching cubes" algorithm demonstrated higher accuracy and efficiency compared to traditional methods.
  • The machine learning model effectively integrates geometric and chemical features for robust predictions.
  • The model accurately predicts crystal volume, accounting for voids, steric hindrance, and intermolecular interactions.

Conclusions:

  • The PyMoVE model provides an accurate and efficient method for predicting homomolecular crystal volume.
  • The integration of geometric and chemical descriptors enhances predictive power.
  • The developed algorithm and model show excellent performance on validation datasets.