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Low-data machine learning models for predicting thermodynamic properties of solid-solid phase transformations in

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This summary is machine-generated.

Researchers developed a machine learning model to predict the transformation entropy of plastic crystals, crucial for colossal barocaloric effect materials. This advance aids in discovering new materials by identifying key molecular descriptors.

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Area of Science:

  • Materials Science
  • Thermodynamics
  • Computational Chemistry

Background:

  • Plastic crystals are vital for colossal barocaloric effect materials but are sparsely documented, hindering predictive model development.
  • Understanding the rotational ordering and disordering transitions in plastic crystals is key to their application.
  • A lack of comprehensive data on plastic crystal structures challenges the creation of new thermodynamic models.

Purpose of the Study:

  • To develop a machine learning model for predicting the transformation entropy of plastic crystals.
  • To identify key molecular features that govern the plastic crystal transformation.
  • To establish a strategy for predicting thermodynamic properties of new molecular structures.

Main Methods:

  • Compiled a database of tetrahedral plastic crystal molecules (neopentane analogs).
  • Utilized features such as chemical functional groups, molecular symmetry, DFT-calculated vibrational entropy, and energy decomposition analysis.
  • Employed correlation matrices and sure independence screening and sparsifying operator (SISSO) regression for feature selection.
  • Trained machine learning models using a dataset of 49 plastic and 37 non-plastic crystals.

Main Results:

  • Successfully demonstrated the effectiveness of the machine learning strategy on a dataset of tetrahedral crystals.
  • Developed regression models capable of predicting transition entropy and enthalpy.
  • Identified top-performing descriptors that elucidate the mechanisms of plastic crystal transformation.
  • SISSO regression explored combinatorial feature spaces to establish structure-property relationships.

Conclusions:

  • The developed machine learning approach effectively predicts plastic crystal transformation entropy and enthalpy.
  • Feature selection via SISSO regression is crucial for identifying predictive descriptors.
  • This strategy aids in understanding and discovering new colossal barocaloric effect materials.
  • The findings provide insights into the underlying mechanisms driving plastic crystal phase transitions.