@article { ISI:000363068400047, title = {Multifaceted thermodynamics of Pb-n (n=16-24) clusters: a case study}, journal = {Journal of Physical Chemistry C}, volume = {119}, number = {41}, year = {2015}, month = {OCT}, pages = {23698-23707}, publisher = {AMER CHEMICAL SOC}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, abstract = {

Thermodynamic response of small clusters is a challenging area of exploration, both experimentally and theoretically. In this article, we study the thermodynamic behavior of small Pb clusters (size 16-24) using Born-Oppenheimer molecular dynamics. A new ground state structure is reported for Pb-20. Except for Pb-21, all clusters fragment at temperatures above T-m[bulk] and show no signs of melting. Characteristic behavior like restricted diffusion and solid solid transition is discussed in detail. Variation in the isomerization temperature of these clusters is explained using the bond length analysis. Root mean square bond length fluctuations (delta(rms)) along with distribution of atoms about center of mass of the cluster as a function of time and distance-energy (DE) plots are used to bring out the essential features of Pb cluster thermodynamics. Analysis carried out using these parameters, and their interpretation regarding state of the system, are discussed in detail. We highlight that it is not possible to define {\textquoteleft}{\textquoteleft}liquid state{\textquoteright}{\textquoteright} for these small clusters, in the conventional frame of understanding.

}, issn = {1932-7447}, doi = {10.1021/acs.jpcc.5b05250}, author = {Susan, Anju and Kaware, Vaibhav and Joshi, Kavita} } @article {46595, title = {Mixed metal oxide: a new class of catalyst for methanol activation}, journal = {Applied Surface Science}, volume = {534}, year = {2020}, month = {DEC}, pages = {147449}, type = {Article}, abstract = {

In this work, we propose a mixed metal oxide as a catalyst and demonstrate it{\textquoteright}s ability to not only activate the MeOH molecule upon adsorption but also dissociate O-H and one of it{\textquoteright}s C-H bonds. MeOH activation is compared on two prominent facets of ZnAl(2)O(4 )viz. (2 2 0) and (31 1). While spontaneous O-H bond dissociation is observed on both facets, C-H bond dissociates only on the (3 1 1) surface. Multiple factors like atomic arrangement and steps on the surface, coordination of surface atoms, and their effective charges have a combined effect on MeOH activation. The (3 1 1) surface offers higher catalytic activity in comparison with (2 2 0) surface. Having a stepped surface, availability of multiple sites, and variation in the charge distribution are some of the reasons for better catalytic performance of (3 1 1) facet. Effect of orientation of MeOH with respect to the surface adds both, information and complexity to the problem. Observations pertinent to understanding this effect are also reported. A detailed analysis of atomic arrangement on the two surfaces provides a rationale as to why MeOH gets dissociated spontaneously on the mixed metal oxide. The promising results reported here opens up a new class of catalyst for research.

}, keywords = {DFT, MeOH, Spontaneous dissociation, ZnAl2O4}, issn = {0169-4332}, doi = {10.1016/j.apsusc.2020.147449}, author = {Mehta, Shweta and Agarwal, Sheena and Kenge, Nivedita and Mekala, Siva Prasad and Patil, Vipul and Raja, T. and Joshi, Kavita} } @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 = {

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.

}, 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 = {

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.

}, doi = {10.1016/j.isci.2025.112251}, author = {Verma, Ashwini and Joshi, Kavita} }