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Updated: Jul 4, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Thermodynamic assessment of machine learning models for solid-state synthesis prediction.

Jane Schlesinger1, Simon Hjaltason1, Nathan J Szymanski1

  • 1Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA. cbartel@umn.edu.

Materials Horizons
|July 3, 2026
PubMed
Summary

Machine learning models for materials synthesis prediction often overestimate results. This study introduces a thermodynamic approach to better assess model accuracy for novel solid-state materials.

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Last Updated: Jul 4, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Machine learning models are increasingly used to predict the synthesizability of solid-state materials.
  • These models learn from databases of successful syntheses, aiming to bypass computationally intensive first-principles calculations.
  • However, the thermodynamic validity of these predictions remains largely unassessed.

Purpose of the Study:

  • To evaluate the alignment of existing machine learning synthesis prediction models with fundamental material and reaction thermodynamics.
  • To establish bounds for thermodynamic quantities (energy relative to convex hull, reaction selectivity) beyond which synthesis is unlikely.
  • To introduce a novel method for assessing machine learning model quality using thermodynamic heuristics.

Main Methods:

  • Computed thermodynamic quantities (energy relative to convex hull, reaction selectivity) for hypothetical materials using the CHGNet potential.
  • Generated novel hypothetical materials using the Chemeleon generative model.
  • Assessed four machine learning synthesizability prediction models against computed thermodynamics and established bounds.

Main Results:

  • Machine learning models generally overpredict the likelihood of material synthesis.
  • Some model scores correlate with thermodynamic heuristics, assigning lower scores to less stable materials or those lacking thermodynamically selective synthesis routes.
  • Established bounds for thermodynamic quantities to identify potentially unsynthesizable materials.

Conclusions:

  • Existing machine learning models for materials synthesis prediction have limitations and tend to overestimate synthesizability.
  • Thermodynamic analysis provides a valuable framework for assessing and improving these predictive models.
  • This work highlights gaps in current models and offers a new approach for quality assessment, particularly crucial in the absence of negative synthesis data.