02619nas a2200193 4500008004100000022001400041245016300055210006900218260000900287300001600296490000700312520188200319100001702201700002002218700001902238700001802257700002102275856012902296 2021 eng d a1463-907600aDART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers0 aDART deep learning enabled topological interaction model for ene cOCT  a21995-220030 v233 aRecently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol(-1) (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.1 aModee, Rohit1 aAgarwal, Sheena1 aVerma, Ashwini1 aJoshi, Kavita1 aPriyakumar, Deva uhttps://library.ncl.res.in/content/dart-deep-learning-enabled-topological-interaction-model-energy-prediction-metal-clusters01768nas 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-learning02066nas a2200241 4500008004100000022001400041245010000055210006900155260000900224300001200233490000700245520125000252653001401502653002101516653001901537653003301556100001901589700001901608700002901627700002601656700001801682856012401700 2024 eng d a0360-319900aHyStor: an experimental database of hydrogen storage properties for various metal alloy classes0 aHyStor an experimental database of hydrogen storage properties f cNOV  a460-4690 v903 a

In this work, we introduce the HyStor database, consisting of 1282 metal alloys along with their maximum hydrogen storage capacity (H2wt%) at a given absorption temperature. The curated HydPark database consist of 831 entries. We sourced compositions from research articles and various patent documents, resulting in addition of 451 compositions to the HydPark database. The addition is reflected in the data across all existing classes of alloys. Further, low entropy alloys (LEA), medium entropy alloys (MEA) and high entropy alloys (HEA) have been newly included classes. This has broadened the scope of the database to encompass the latest materials of interest for hydrogen storage. HyStor contains representation of 54 elements, with a temperature range of 200-800 K, and H2wt% ranging from 0.1 to 7.19. We conducted thorough checks for duplicate entries, erroneous data, and conflicting compositions within the database to ensure data quality. Furthermore, we conducted multiple tests to identify potential outlier compositions. The data curation and updation reflects into slight improved error metrics of the HYST model, reducing the Mean Absolute Error (MAE) from 0.31 to 0.29 and increasing the R2 score from 0.77 to 0.79.

10aDatabases10amachine learning10aMetal hydrides10aSolid-state hydrogen storage1 aWilson, Nikhil1 aVerma, Ashwini1 aMaharana, Piyush, Ranjan1 aSahoo, Ameeya, Bhusan1 aJoshi, Kavita uhttps://library.ncl.res.in/content/hystor-experimental-database-hydrogen-storage-properties-various-metal-alloy-classes02676nas a2200229 4500008004100000022001400041245007700055210006900132260000800201300001400209490000700223520189800230653003402128653003002162653001902192653003902211653003302250100001902283700001902302700001802321856010702339 2024 eng d a0360-319900aSolid state hydrogen storage: Decoding the path through machine learning0 aSolid state hydrogen storage Decoding the path through machine l cJAN a1518-15280 v503 a

We present a machine learning (ML) framework HEART (HydrogEn storAge propeRty predicTor) for identifying suitable families of metal alloys for hydrogen storage under ambient conditions. Our framework includes two ML models that predict the hydrogen storage capacity (HYST) and the enthalpy of hydride formation (THOR) of multi-component metal alloys. We demonstrate that a chemically diverse set of features effectively describes the hydrogen storage properties of the alloys. In HYST, we use absorption temperature as a feature which improved H2wt% prediction significantly. For out-of-the-bag samples, HYST predicted H2wt% with R2 score of 0.81 and mean absolute error (MAE) of 0.45 wt% whereas R2 score is 0.89 and MAE is 4.53 kJ/molH2 for THOR. These models are further employed to predict H2wt% and Delta H for similar to 6.4 million multi-component metal alloys. We have identified 6480 compositions with superior storage properties (H2wt% > 2.5 at room temperature and Delta H < 60 kJ/molH2). We have also discussed in detail the interesting trends picked up by these models like temperature dependent variation in the rate of hydrogenation and alloying effect on H2wt% and Delta H in different families of alloys. Importantly certain elements like Al, Si, Sc, Cr, and Mn when mixed in small fractions with hydriding elements like Mg, Ti, V etc. systematically reduce Delta H without significantly compromising the storage capacity. Further upon increasing the number of elements in the alloy i.e from binary to ternary to quaternary, the number of compositions with lower enthalpies also increases. From the 6.4 million compositions, we have reported new alloy families having potential for hydrogen storage at room temperature. Finally, we demonstrate that HEART has the potential to scan vast chemical spaces by narrowing down potential materials for hydrogen storage.

10aEnthalpy of hydride formation10aHydrogen storage capacity10aMetal hydrides10aPredictive machine learning models10aSolid-state hydrogen storage1 aVerma, Ashwini1 aWilson, Nikhil1 aJoshi, Kavita uhttps://library.ncl.res.in/content/solid-state-hydrogen-storage-decoding-path-through-machine-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-pct01877nas a2200217 4500008004100000245008400041210006900125260000800194300001100202490000600213520119200219653002101411653002101432653000801453653001401461653000801475100002901483700001901512700001801531856011001549 2025 eng d00aRetrieval augmented generation for building datasets from scientific literature0 aRetrieval augmented generation for building datasets from scient cJUL a0350060 v83 a

In this work, we show that employing retrieval augmented generation (RAG) with a large language model (LLM) enables us to extract accurate data from scientific literature and construct datasets. The rapid growth in publications necessitates the automation of extraction of structured data as it is crucial for training machine learning(ML) models. The pipeline developed is simple and can be adjusted accordingly with natural language as input. Quantization enables us to run LLMs on consumer hardware and remove the reliance on closed-source models. Both Llama3-8B and Gemma2-9B with RAG give structured output consistently and with high accuracy as compared to direct prompting. Using the newly developed protocol, we created a data set of metal hydrides for solid-state hydrogen storage from paper abstracts. The accuracy of the generated dataset was >88% in the cases tested. Further, we demonstrate that the generated dataset is ready-to-use for ML models by testing it with HYST to predict the H(2)wt\textbackslash% at a given temperature. Thus, we demonstrate a pipeline to create datasets from scientific literature at minimal computational cost and high accuracy.

10adataset building10aHydrogen storage10aLLM10amaterials10aRAG1 aMaharana, Piyush, Ranjan1 aVerma, Ashwini1 aJoshi, Kavita uhttps://library.ncl.res.in/content/retrieval-augmented-generation-building-datasets-scientific-literature01780nas a2200205 4500008004100000022001400041245007800055210006900133260000900202300001100211490000800222520111900230653002201349653002101371653001901392653001801411100001901429700001801448856010801466 2026 eng d a0360-319900aPathfinder: adaptive learning for hydrogen storage material optimizationa0 aPathfinder adaptive learning for hydrogen storage material optim cMAY  a1551240 v2363 a

Progress in solid-state hydrogen storage is constrained by time-consuming experiments and scarce high-quality data, limiting effective use of machine learning. To address this, we present an adaptive learning (AL) framework that integrates uncertainty quantification within a closed-loop workflow for targeted material optimization. Unlike static models, it adaptively selects compositions to maximize information gain and improve predictive performance. As a proof of concept, we predict pressure-composition-temperature (PCT) isotherms of Mg-Ni-La systems using literature data for Mg-Ni. The framework identifies informative compositions across Mg fractions (92%-4%) and temperatures (300-633 K), demonstrating effective exploration of the chemical space. For ten unseen compositions evaluated sequentially, accuracy reaches 80% within five cycles, with predictions aligning well with experiments. This establishes a family-specific predictive tool for Mg-Ni-based systems, while the underlying AL framework is broadly applicable to other chemical families with modest initial experimental datasets.

10aAdaptive learning10aHydrogen storage10aMetal hydrides10aPCT isotherms1 aVerma, Ashwini1 aJoshi, Kavita uhttps://library.ncl.res.in/content/pathfinder-adaptive-learning-hydrogen-storage-material-optimizationa02403nas a2200229 4500008004100000022001400041245009400055210006900149260000900218300001100227490000800238520163600246653002201882653002401904653002101928653002101949653001901970653002401989100001902013700001802032856012302050 2026 eng d a0360-319900aWhat drives property prediction for solid-state hydrogen storage? data or smart features?0 aWhat drives property prediction for solidstate hydrogen storage  cAPR  a1544990 v2263 a

Metal hydrides play a pivotal role in a wide range of applications, including hydrogen storage, compression, heat management, and catalysis, making them a central focus of interdisciplinary research spanning chemistry, materials science, and engineering. The performance of the metal hydride-based systems is strongly governed by the thermodynamics of metal-hydrogen interactions. Among key thermodynamic properties, the equilibrium plateau pressure (P-eq) is particularly critical, as it defines operating conditions for hydrogen absorption and desorption. Traditionally, determining P-eq requires extensive experimental measurements, which limits the pace of materials discovery. On the other hand, predicting it through ML-based models is constrained by the availability of limited data. In this work, we demonstrate that smart features can be a way to overcome this limitation. EquiP, an ML model trained to predict ln(P-eq) as a function of temperature, generates Van't Hoff plots (P-eq vs. 1/T), enabling rapid determination of enthalpy and entropy of hydride formation. We demonstrate that incorporating structural descriptors derived from X-ray diffraction (XRD) data improves the performance of the model, particularly with sparse training datasets. A model trained using only compositional descriptors yields a validation mean absolute error (MAE) of 0.21 bar, whereas incorporating XRD features reduces the MAE substantially to 0.07 bar. This work demonstrates that with limited data, intelligent feature design grounded in domain knowledge is the key to improving predictions of complex material properties.

10aExperimental data10aFeature engineering10aHydrogen storage10amachine learning10aMetal hydrides10aProperty prediction1 aVerma, Ashwini1 aJoshi, Kavita uhttps://library.ncl.res.in/content/what-drives-property-prediction-solid-state-hydrogen-storage-data-or-smart-features