01747nas a2200169 4500008004100000022001400041245007300055210006700128260007100195300001600266490000800282520113200290100001601422700002001438700001801458856010101476 2015 eng d a1932-744700aMultifaceted thermodynamics of Pb-n (n=16-24) clusters: a case study0 aMultifaceted thermodynamics of Pbn n1624 clusters a case study a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcOCT a23698-237070 v1193 a

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 ``liquid state'' for these small clusters, in the conventional frame of understanding.

1 aSusan, Anju1 aKaware, Vaibhav1 aJoshi, Kavita uhttps://library.ncl.res.in/content/multifaceted-thermodynamics-pb-n-n16-24-clusters-case-study-102043nas a2200265 4500008004100000022001400041245007100055210006900126260000800195300001100203490000800214520127000222653000801492653000901500653002901509653001201538100001801550700002001568700002001588700002501608700001701633700001301650700001801663856009601681 2020 eng d a0169-433200aMixed metal oxide: a new class of catalyst for methanol activation0 aMixed metal oxide a new class of catalyst for methanol activatio cDEC a1474490 v5343 a

In this work, we propose a mixed metal oxide as a catalyst and demonstrate it's ability to not only activate the MeOH molecule upon adsorption but also dissociate O-H and one of it'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.

10aDFT10aMeOH10aSpontaneous dissociation10aZnAl2O41 aMehta, Shweta1 aAgarwal, Sheena1 aKenge, Nivedita1 aMekala, Siva, Prasad1 aPatil, Vipul1 aRaja, T.1 aJoshi, Kavita uhttps://library.ncl.res.in/content/mixed-metal-oxide-new-class-catalyst-methanol-activation01768nas a2200205 4500008004100000245007600041210006900117260000900186300001100195490000600206520109400212653002101306653002701327653002501354100001701379700001901396700001801415700002101433856010801454 2023 eng d00aMeGen-generation of gallium metal clusters using reinforcement learning0 aMeGengeneration of gallium metal clusters using reinforcement le cJUN  a0250320 v43 a

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.

10agallium clusters10areinforcement learning10astructure generation1 aModee, Rohit1 aVerma, Ashwini1 aJoshi, Kavita1 aPriyakumar, Deva uhttps://library.ncl.res.in/content/megen-generation-gallium-metal-clusters-using-reinforcement-learning01581nas a2200145 4500008004100000245011300041210006900154260000900223300001100232490000700243520102200250100001901272700001801291856012601309 2025 eng d00aMH-PCTpro: a machine learning model for rapid prediction of pressure-composition-temperature (PCT) isotherms0 aMHPCTpro a machine learning model for rapid prediction of pressu cAPR  a1122510 v283 a

We present a machine-learning powered Metal Hydride'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'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.

1 aVerma, Ashwini1 aJoshi, Kavita uhttps://library.ncl.res.in/content/mh-pctpro-machine-learning-model-rapid-prediction-pressure-composition-temperature-pct