@article {48775,
	title = {MeGen-generation of gallium metal clusters using reinforcement learning},
	journal = {Machine Learning-Science and Technology},
	volume = {4},
	year = {2023},
	month = {JUN },
	pages = {025032},
	type = {Article},
	abstract = {<p>
	The generation of low-energy 3D structures of metal clusters depends on the efficiency of the search algorithm and the accuracy of inter-atomic interaction description. In this work, we formulate the search algorithm as a reinforcement learning (RL) problem. Concisely, we propose a novel actor-critic architecture that generates low-lying isomers of metal clusters at a fraction of computational cost than conventional methods. Our RL-based search algorithm uses a previously developed DART model as a reward function to describe the inter-atomic interactions to validate predicted structures. Using the DART model as a reward function incentivizes the RL model to generate low-energy structures and helps generate valid structures. We demonstrate the advantages of our approach over conventional methods for scanning local minima on potential energy surface. Our approach not only generates isomer of gallium clusters at a minimal computational cost but also predicts isomer families that were not discovered through previous density-functional theory (DFT)-based approaches.</p>
},
	keywords = {gallium clusters, reinforcement learning, structure generation},
	doi = {10.1088/2632-2153/acdc03},
	author = {Modee, Rohit and Verma, Ashwini and Joshi, Kavita and Priyakumar, U. Deva}
}
@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}
}
