A competent professional with over ten years of experience in advanced material characterization, data analysis, and performing machine learning algorithms to develop next-generation advanced material.

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Honors & Awards

  • Best Poster Winner Award, Denver X-ray Conference, Denver X-ray Conference (2017)
  • International Grad Student Scholarship,, University of Nevada, Reno (2016)
  • International Grad Student Scholarship, University of Nevada, Reno (2015)
  • Hon. Marilyn J. Ph.D. Scholarship, University of Nevada, Reno (2013)
  • Ned R. Morehouse Engineering Endowed Scholarship, University of Nevada (2013)

Professional Education

  • Bachelor of Engineering, Bengal Engineering College (2009)
  • Doctor of Philosophy, University of Nevada Reno (2017)

Research Interests

  • Data Sciences
  • Research Methods
  • Teachers and Teaching

Current Research and Scholarly Interests

My research is focused on accelerating materials discovery for advanced manufacturing and renewable energy storage devices by different high-throughput processing (thin-film, bulk, and 3D printing) and characterization techniques guided by Machine Learning (ML).


  • Accelerated discovery of compositionally complex alloys for direct thermal energy conversion, Stanford University



  • Discovery of new metallic glasses by machine learning and high throughput x-ray diffraction, Stanford University



  • Behavior of Ni-Nb-Zr Alloy Gas Permeation Membrane Ribbons at Extreme Pressure Condition, University of Nevada, Reno



  • Micro structural and mechanical properties of cryogenically treated and case carburised SAE 8620 steel, Bengali Engineering & Science University



All Publications

  • On-the-fly closed-loop materials discovery via Bayesian active learning. Nature communications Kusne, A. G., Yu, H., Wu, C., Zhang, H., Hattrick-Simpers, J., DeCost, B., Sarker, S., Oses, C., Toher, C., Curtarolo, S., Davydov, A. V., Agarwal, R., Bendersky, L. A., Li, M., Mehta, A., Takeuchi, I. 2020; 11 (1): 5966


    Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

    View details for DOI 10.1038/s41467-020-19597-w

    View details for PubMedID 33235197

  • High-Throughput Characterization of (FexCo1-x)3O4 Thin-Film Composition Spreads. ACS combinatorial science Piotrowiak, T. H., Wang, X., Banko, L., Kumari, S., Sarker, S., Mehta, A., Ludwig, A. 2020


    Thin-film continuous composition spreads of Fe-Co-O were fabricated by reactive cosputtering from elemental Fe and Co targets in reactive Ar/O2 atmosphere using deposition temperatures ranging from 300 to 700 °C. Fused silica and platinized Si/SiO2 strips were used as substrates. Ti and Ta were investigated as adhesion layer for Pt and the fabrication of the Fe-Co-O films. The thin-film composition spreads were characterized by high-throughput electron-dispersive X-ray spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, atomic force microscopy, scanning electron microscopy, and optical transmission spectroscopy. The Fe-content ranged from 28 to 72 at. %. The spinel phases Fe2CoO4 and FeCo2O4 could be synthesized and stabilized at all deposition temperatures with a continuous variation in spinel composition in between. The dependence of the film surface microstructure on the deposition temperature and the composition was mapped. Moreover, the band gap values, ranging from 2.41 eV for FeCo2O4 to 2.74 eV for Fe2CoO4, show a continuous variation with the composition.

    View details for DOI 10.1021/acscombsci.0c00126

    View details for PubMedID 33152234

  • Exploring the First High-Entropy Thin Film Libraries: Composition Spread-Controlled Crystalline Structure. ACS combinatorial science Nguyen, T. X., Su, Y., Hattrick-Simpers, J., Joress, H., Nagata, T., Chang, K., Sarker, S., Mehta, A., Ting, J. 2020


    Thin films of two types of high-entropy oxides (HEOs) have been deposited on 76.2 mm Si wafers using combinatorial sputter deposition. In one type of the oxides, (MgZnMnCoNi)Ox, all the metals have a stable divalent oxidation state and similar cationic radii. In the second type of oxides, (CrFeMnCoNi)Ox, the metals are more diverse in the atomic radius and valence state, and have good solubility in their sub-binary and ternary oxide systems. The resulting HEO thin films were characterized using several high-throughput analytical techniques. The microstructure, composition, and electrical conductivity obtained on defined grid maps were obtained for the first time across large compositional ranges. The crystalline structure of the films was observed as a function of the metallic elements in the composition spreads, that is, the Mn and Zn in (MgZnMnCoNi)Ox and Mn and Ni in (CrFeMnCoNi)Ox. The (MgZnMnCoNi)Ox sample was observed to form two-phase structures, except single spinel structure was found in (MgZnMnCoNi)Ox over a range of Mn > 12 at. % and Zn < 44 at. %, while (CrFeMnCoNi)Ox was always observed to form two-phase structures. Composition-controlled crystalline structure is not only experimentally demonstrated but also supported by density function theory calculation.

    View details for DOI 10.1021/acscombsci.0c00159

    View details for PubMedID 33146510

  • Combinatorial Exploration and Mapping of Phase Transformation in a Ni-Ti-Co Thin Film Library. ACS combinatorial science Al Hasan, N. M., Hou, H., Gao, T., Counsell, J., Sarker, S., Thienhaus, S., Walton, E., Decker, P., Mehta, A., Ludwig, A., Takeuchi, I. 2020


    Combinatorial synthesis and high-throughput characterization of a Ni-Ti-Co thin film materials library are reported for exploration of reversible martensitic transformation. The library was prepared by magnetron co-sputtering, annealed in vacuum at 500 °C without atmospheric exposure, and evaluated for shape memory behavior as an indicator of transformation. Composition, structure, and transformation behavior of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and four-point probe temperature-dependent resistance (R(T)) measurements. A new, expanded composition space having phase transformation with low thermal hysteresis and Co > 10 at. % is found. Unsupervised machine learning methods of hierarchical clustering were employed to streamline data processing of the large XRD and XPS data sets. Through cluster analysis of XRD data, we identified and mapped the constituent structural phases. Composition-structure-property maps for the ternary system are made to correlate the functional properties to the local microstructure and composition of the Ni-Ti-Co thin film library.

    View details for DOI 10.1021/acscombsci.0c00097

    View details for PubMedID 32786322

  • A High-Throughput Structural and Electrochemical Study of Metallic Glass Formation in Ni-Ti-Al ACS COMBINATORIAL SCIENCE Joress, H., DeCost, B. L., Sarker, S., Braun, T. M., Jilani, S., Smith, R., Ward, L., Laws, K. J., Mehta, A., Hattrick-Simpers, J. R. 2020; 22 (7): 330–38


    On the basis of a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high-throughput structural and electrochemical characterization. Using this dual-modality approach, we are able to better classify the amorphous portion of the library, which we found to be the portion with a full width at half maximum (fwhm) of >0.42 Å-1 for the first sharp X-ray diffraction peak. Proper phase labeling is important for future machine learning efforts. We demonstrate that the fwhm and corrosion resistance are correlated but that, while chemistry still plays a role in corrosion resistance, a large fwhm, attributed to a glassy phase, is necessary for the highest corrosion resistance.

    View details for DOI 10.1021/acscombsci.9b00215

    View details for Web of Science ID 000607536400002

    View details for PubMedID 32496755

  • Structural and photoelectrochemical properties in the thin film system Cu-Fe-V-O and its ternary subsystems Fe-V-O and Cu-V-O. The Journal of chemical physics Kumari, S., Junqueira, J. R., Sarker, S., Mehta, A., Schuhmann, W., Ludwig, A. 2020; 153 (1): 014707


    Thin-film material libraries in the ternary and quaternary metal oxide systems Fe-V-O, Cu-V-O, and Cu-Fe-V-O were synthesized using combinatorial reactive co-sputtering with subsequent annealing in air. Their compositional, structural, and functional properties were assessed using high-throughput characterization methods. Prior to the investigation of the quaternary system Cu-Fe-V-O, the compositions (Fe61V39)Ox and (Cu52V48)Ox with promising photoactivity were identified from their ternary subsystems Fe-V-O and Cu-V-O, respectively. Two Cu-Fe-V-O material libraries with (Cu29-72Fe4-27V22-57)Ox and (Cu11-55Fe27-73V12-34)Ox composition spread were investigated. Seven mixed ternary and quaternary phase regions were identified: I (alpha-Cu3FeV6O26/FeVO4), II (Cu5V2O10/FeVO4/alpha-Cu3Fe4V6O26), III (Cu5V2O10), IV (Cu5V2O10/FeVO4, V (FeVO4/gamma-Cu2V2O7/alpha-Cu3Fe4V6O26), VI (beta-Cu2V2O7/alpha-Cu3Fe4V6O26/FeVO4), and VII (beta-Cu3Fe4V6O26/FeVO4). In the investigated composition range, two photoactive regions, (Cu53Fe7V40)Ox and (Cu45Fe21V34)Ox, were identified, exhibiting 103 muA/cm2 and 108 muA/cm2 photocurrent density for the oxygen evolution reaction at 1.63 V vs reversible hydrogen electrode, respectively. The highest photoactive region (Cu45Fe21V34)Ox comprises the dominant alpha-Cu3Fe4V6O24 phase and minor FeVO4 phase. This photoactive region corresponds to having an indirect bandgap of 1.87 eV and a direct bandgap of 2.58 eV with an incident photon-to-current efficiency of 30% at a wavelength of 310 nm.

    View details for DOI 10.1063/5.0009512

    View details for PubMedID 32640827

  • High-throughput characterization of Ag-V-O nanostructured thin-film materials libraries for photoelectrochemical solar water splitting INTERNATIONAL JOURNAL OF HYDROGEN ENERGY Kumari, S., Helt, L., Junqueira, J. C., Kostka, A., Zhang, S., Sarker, S., Mehta, A., Scheu, C., Schuhmann, W., Ludwig, A. 2020; 45 (21): 12037–47