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Peer-reviewedCC BYJournal of Cheminformatics

P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial

  • Methun Kamruzzaman1
  • Alexander Landera2
  • Nalini Menon3
  • Mark D. Allendorf4
  • Kunal Poorey5
5 affiliations
  1. 1(Former) Applied Biosciences and Engineering, Sandia National Laboratories
  2. 2Applied Biosciences and Engineering, Sandia National Laboratories
  3. 3Material Characterization and Performance, Sandia National Laboratories
  4. 4(Former) Materials Research, Sandia National Laboratories
  5. 5Material Lifecycle management, Sandia National Laboratories
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Abstract

Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown limited success due to the complex relationship between molecular structure and melting point. In this study, we present a data-driven machine learning (ML) approach to predict the melting points of organic compounds, leveraging both 2D and 3D molecular descriptors. Our results indicate that ML models can significantly improve melting-point predictions, providing a robust tool for the scientific community. Scientific contribution Our detailed analysis on melting point prediction, along with SHAP explainability, reveals the top influencing features for the prediction. The P2MAT application we developed as part of this study can predict both melting and boiling points from a SMILES string. P2MAT is available as an easy-to-install, user-friendly GUI for maximum outreach to the scientific community. Our benchmark analysis demonstrates the excellence of our method for predicting melting points.

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Cite

BibTeXRIS
BibTeX
@article{kamruzzaman2026p2mat,
  title = {{P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial}},
  author = {Methun Kamruzzaman and Alexander Landera and Nalini Menon and Mark D. Allendorf and Kunal Poorey},
  journal = {Journal of Cheminformatics},
  year = {2026},
  month = jul,
  doi = {10.1186/s13321-026-01247-w},
  url = {https://doi.org/10.1186/s13321-026-01247-w},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial
AU  - Methun Kamruzzaman
AU  - Alexander Landera
AU  - Nalini Menon
AU  - Mark D. Allendorf
AU  - Kunal Poorey
PY  - 2026
DA  - 2026/07/01
JO  - Journal of Cheminformatics
DO  - 10.1186/s13321-026-01247-w
UR  - https://doi.org/10.1186/s13321-026-01247-w
AB  - Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown limited success due to the complex relationship between molecular structure and melting point. In this study, we present a data-driven machine learning (ML) approach to predict the melting points of organic compounds, leveraging both 2D and 3D molecular descriptors. Our results indicate that ML models can significantly improve melting-point predictions, providing a robust tool for the scientific community. Scientific contribution Our detailed analysis on melting point prediction, along with SHAP explainability, reveals the top influencing features for the prediction. The P2MAT application we developed as part of this study can predict both melting and boiling points from a SMILES string. P2MAT is available as an easy-to-install, user-friendly GUI for maximum outreach to the scientific community. Our benchmark analysis demonstrates the excellence of our method for predicting melting points.
SN  - 1758-2946
ER  -