Accelerated predictions of the sublimation enthalpy of organic materials with machine learning

Abstract

The sublimation enthalpy, ΔH_sub, is a key thermodynamic parameter governing the phase transformation of a substance between its solid and gas phases. This transformation is at the core of many important materials' purification, deposition, and etching processes. While ΔH_sub can be measured experimentally and estimated computationally, these approaches have their own different challenges. Here, we develop a machine learning (ML) approach to rapidly predict ΔH_sub from data generated using density functional theory (DFT). We further demonstrate how combining ML and DFT methods with active learning can be efficient in exploring the materials space, expanding the coverage of the computed dataset, and systematically improving the ML predictive model of ΔHsub. With an error of ~15 kJ/mol in instantaneous predictions of ΔH_sub, the ML model developed in this work will be useful for the community.

Keywords

organic material / machine learning / density functional theory (DFT)

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.