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

The sublimation enthalpy, $\Delta H_{\text{sub}}$, 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 $\Delta H_{\text{sub}}$ 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 $\Delta H_{\text{sub}}$ 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 $\Delta H_{\text{sub}}$. With an error of $\sim 15$ kJ/mol in instantaneous predictions of $\Delta H_{\text{sub}}$, the ML model developed in this work will be useful for the community.