arXiv subjects

Apurva Mehta

Publications and source records attributed to Apurva Mehta.

29 records · Page 2Linked to original sources

arXiv2022

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system's composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO's autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO's search when used as a prior.

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arXiv2020

On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning

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 [1]. 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 autonomous research methodology (i.e. autonomous hypothesis definition and evaluation) that can place complex, advanced materials in reach, allowing scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. Additionally, this robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. We used the real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) at the synchrotron beamline to accelerate the fundamentally interconnected tasks of rapid phase mapping and property optimization, with each cycle taking seconds to minutes, resulting in the discovery of a novel epitaxial nanocomposite phase-change memory material.

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arXiv2019

A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al

Based on 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-half-maximum (FWHM) of 0.42 A$^{-1}$ for the first sharp x-ray diffraction peak. We demonstrate that the FWHM and corrosion resistance are correlated but that, while chemistry still plays a role, a large FWHM is necessary for the best corrosion resistance.

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arXiv2019

Control of dopant crystallinity in electrochemically treated cuprate thin films

We present a methodology based on \textit{ex-situ} (post-growth) electrochemistry to control the oxygen concentration in thin films of the superconducting oxide La$_2$CuO$_{4+y}$ grown epitaxially on substrates of isostructural LaSrAlO$_4$. The superconducting transition temperature, which depends on the oxygen concentration, can be tuned by adjusting the pH level of the base solution used for the electrochemical reaction. As our main finding, we demonstrate that the dopant oxygens can either occupy the interstitial layer in an orientationally disordered state or organize into a crystalline phase via a mechanism in which dopant oxygens are inserted into the substrate, changing the lattice symmetry of both the substrate and the epitaxial film. We discuss this mechanism, and describe the resulting methodology as a platform to be explored in thin films of other transition metal oxides.

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arXiv2018

Ionic Tuning of Cobaltites at the Nanoscale

Control of materials through custom design of ionic distributions represents a powerful new approach to develop future technologies ranging from spintronic logic and memory devices to energy storage. Perovskites have shown particular promise for ionic devices due to their high ion mobility and sensitivity to chemical stoichiometry. In this work, we demonstrate a solid-state approach to control of ionic distributions in (La,Sr)CoO$_{3}$ thin films. Depositing a Gd capping layer on the perovskite film, oxygen is controllably extracted from the structure, up-to 0.5 O/u.c. throughout the entire 36 nm thickness. Commensurate with the oxygen extraction, the Co valence state and saturation magnetization show a smooth continuous variation. In contrast, magnetoresistance measurements show no-change in the magnetic anisotropy and a rapid increase in the resistivity over the same range of oxygen stoichiometry. These results suggest significant phase separation, with metallic ferromagnetic regions and oxygen-deficient, insulating, non-ferromagnetic regions, forming percolated networks. Indeed, X-ray diffraction identifies oxygen-vacancy ordering, including transformation to a brownmillerite crystal structure. The unexpected transformation to the brownmillerite phase at ambient temperature is further confirmed by high-resolution scanning transmission electron microscopy which shows significant structural - and correspondingly chemical - phase separation. This work demonstrates room-temperature ionic control of magnetism, electrical resistivity, and crystalline structure in a 36 nm thick film, presenting new opportunities for ionic devices that leverage multiple material functionalities.

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arXiv2017

On-the-fly Data Assessment for High Throughput X-ray Diffraction Measurement

Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through development of an approach that makes routine data assessment automatic and instantaneous. Through extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large datasets is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize usage of expensive characterization resources by prioritizing measurements of highest scientific impact. We anticipate our approach to become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.

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arXiv2017

On-the-fly Segmentation Approaches for X-ray Diffraction Datasets for Metallic Glasses

Investment in brighter sources and larger detectors has resulted in an explosive rise in the data collected at synchrotron facilities. Currently, human experts extract scientific information from these data, but they cannot keep pace with the rate of data collection. Here, we present three on-the-fly approaches - attribute extraction, nearest-neighbor distance, and cluster analysis - to quickly segment x-ray diffraction (XRD) data into groups with similar XRD profiles. An expert can then analyze representative spectra from each group in detail with much reduced time, but without loss of scientific insights. On-the-fly segmentation would, therefore, result in accelerated scientific productivity.

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arXiv2017

Crystal truncation rods from miscut surfaces

Crystal truncation rods are used to study surface and interface structure. Since real surfaces are always somewhat miscut from a low index plane, it is important to study the effect of miscut on crystal truncation rods. We develop a model that describes the truncation rod scattering from miscut surfaces that have steps and terraces. We show that non-uniform terrace widths and jagged step edges are both forms of roughness that decrease the intensity of the rods. Non-uniform terrace widths also result in a broad peak that overlaps the rods. We use our model to characterize the terrace width distribution and step edge jaggedness on three SrTiO$_3$ (001) samples, showing excellent agreement between the model and the data, confirmed by atomic force micrographs of the surface morphology. We expect our description of terrace roughness will apply to many surfaces, even those without obvious terracing.

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arXiv2016

Voltage Controlled Interfacial Layering in an Ionic Liquid on SrTiO$_3$

One prominent structural feature of ionic liquids near surfaces is formation of alternating layers of anions and cations. However, how this layering responds to applied potential is poorly understood. We focus on the structure of 1-butyl-1-methylpyrrolidinium tris(pentafluoroethyl) trifluorophosphate (BMPY-FAP) near the surface of a strontium titanate (SrTiO$_3$) electric double-layer transistor. Using x-ray reflectivity, we show that at positive bias, the individual layers in the ionic liquid double layer thicken and the layering persists further away from the interface. We model the reflectivity using a modified distorted crystal model with alternating cation and anion layers, which allows us to extract the charge density and the potential near the surface. We find that the charge density is strongly oscillatory with and without applied potential, and that with applied gate bias of 4.5 V the first two layers become significantly more cation rich than at zero bias, accumulating about $2.5 \times 10^{13}$ cm$^{-2}$ excess charge density.

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arXiv2016

Mechanism for the Large Conductance Modulation in Electrolyte-gated Thin Gold Films

Electrolyte gating using ionic liquid electrolytes has recently generated considerable interest as a method to achieve large carrier density modulations in a variety of materials. In noble metal thin films, electrolyte gating results in large changes in sheet resistance. The widely accepted mechanism for these changes is the formation of an electric double layer with a charged layer of ions in the liquid and accumulation or depletion of carriers in the thin film. We report here a different mechanism. In particular, we show using x-ray absorption near edge structure (XANES) that the previously reported large conductance modulation in gold films is due to reversible oxidation and reduction of the surface rather than the charging of an electric double layer. We show that the double layer capacitance accounts for less than 10\% of the observed change in transport properties. These results represent a significant step towards understanding the mechanisms involved in electrolyte gating.

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arXiv2010

Visualizing Three-Dimensional Micromechanical Response in Nanomaterials

Understanding mechanical properties of materials requires not only complete determination of the three-dimensional response at a local scale, but also knowledge of the mode or the mechanism by which deformation takes place. Probing mechanical response at such a depth can be only achieved through a diffraction based method. In spite of this, diffraction based methods still are not commonly employed for strain measurements because they are perceived as very time intensive and non-intuitive. Herein we introduce the concept of a diffraction strain ellipsoid, and show how its shape, thickness, and orientation represent the complete deformation state in a powerfully visual and intuitive way. We also show how the geometry of the ellipsoid can be very quickly determined from x-ray diffraction data obtained using a large area detector, and how it can be used to understand micromechanical deformation of nanocrystalline materials.

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