ArticleChemical science2023
A data-driven interpretation of the stability of organic molecular crystals.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Identifying the impact of chemical functional groups on ionic liquid conductivity.Chemical science · 2026Article
- How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric Systems.Journal of chemical theory and computation · 2026Article
- Fast Molecular Crystal Structure Prediction Using Sampling by Analogy to Previously Predicted Landscapes.Journal of chemical theory and computation · 2026Article
- Thermodynamics-Informed Machine Learning of Organic Electrode Material Solubility in Nonaqueous Electrolytes.The journal of physical chemistry. B · 2026Article
- PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.Nature communications · 2025Article
- Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes.Faraday discussions · 2025Article
- Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.Molecular pharmaceutics · 2024Article
- Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials.Chemical science · 2023Review
- Robustness of Local Predictions in Atomistic Machine Learning Models.Journal of chemical theory and computation · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning of their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals, exploiting its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.