@article {49532,
	title = {MH-PCTpro: a machine learning model for rapid prediction of pressure-composition-temperature (PCT) isotherms},
	journal = {Iscience},
	volume = {28},
	year = {2025},
	month = {APR },
	pages = {112251},
	type = {Article},
	abstract = {<p>
	We present a machine-learning powered Metal Hydride{\textquoteright}s Pressure-Composition-Temperature isotherm Predictor (MH-PCTpro) for metal compositions. To train the MH-PCTpro, an experimental database of PCT isotherms is built from published literature. The database comprises over 14,000 data points extracted from 237 PCT isotherms representing 138 distinct compositions. The dataset encompasses more than 25 elements and spans a broad spectrum of absorption temperatures (263-653 K) and hydrogen pressures (0.001-40 MPa). The model is validated on a wide range of alloy families and its predictions are consistent with experimental results. The model also captures temperature-dependent variations in plateau pressure, enabling determination of enthalpy and entropy of hydride formation through Van{\textquoteright}t Hoff plots. Hence, MH-PCTpro can be used as an ML tool for guiding PCT experiments, offering PCT isotherm predictions and valuable thermodynamic insights into materials suitable for solid-state hydrogen storage.</p>
},
	doi = {10.1016/j.isci.2025.112251},
	author = {Verma, Ashwini and Joshi, Kavita}
}
