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Piyush Wanchoo

Publications and source records attributed to Piyush Wanchoo.

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AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments

Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-throughput and autonomous laboratories. To date most demonstrations of such laboratories have focused on functional materials, with less attention paid to structural materials. We present here the Artificial Intelligence in Materials Design Laboratory (AIMD-L), an automated, high-throughput facility for characterizing the microstructure and properties of structural metals and ceramics, with an emphasis on materials in extreme environments. AIMD-L has two custom instruments for characterization of structural materials: HELIX for shock studies of materials, and MAXIMA for X-ray diffraction and X-ray fluorescence spectroscopy. Specifically designed for high-throughput studies, HELIX and MAXIMA are each capable of collecting data at rates two to three orders of magnitude faster than conventional systems. A third experimental station, SPHINX, is a commercial nanoindenter modified for integration into the automated workflow of AIMD-L. A user (which may be human or an AI agent) directs the experiments to be carried out by means of a centralized control program. The experimental stations are linked by a conveyance that moves samples around the lab, with a robot at each station for sample transfer in/out of the instrument. The experimental stations also communicate with a common data layer that streams data autonomously from each instrument to a data portal, where their arrival triggers automated workflows for data reduction and analysis. The processed data are immediately available to the human operator or agentic AI, forming a closed loop for rapid decision-making and experimental control.

cond-mat.mtrl-sci

A large deformation model for quasi-static to high strain rate response of a rate-stiffening soft polymer

Polyborosiloxane (PBS) is an important rate-stiffening soft polymer with dynamic, reversible crosslinks used in applications ranging from self-healing sensing and actuation to body and structural protection. Its highly rate-dependent response, especially for impact-mitigating structures, is important. However, the large strain response of PBS has not been characterized over quasi-static to high strain rates. Currently, there are no constitutive models that can predict the strongly rate-dependent large-deformation elastic-viscoplastic response of PBS. To address this gap, we have developed a microstructural physics motivated constitutive model for PBS and similar soft polymers and polymer gels with dynamic crosslinks to predict their large strain, non-linear loading-unloading, and significantly rate-dependent response. We have conducted compression experiments on PBS up to true strains of $\sim$125$\%$ over a wide strain rate range of 10$^{-3}$ s$^{-1}$ to 10$^{3}$ s$^{-1}$. The model reasonably accurately captures the response of PBS over six decades of strain rates. We propose boron-oxygen coordinate-bond dynamic crosslinks with macroscopic relaxation timescale $τ\approx 3$ s and temporary entanglement lockups at high strain rates acting as crosslinks with $τ\approx 0.0005$ s as the two types of crosslink mechanisms in PBS. We have outlined a numerical update procedure to evaluate the convolution-like time integrals arising from dynamic crosslink kinetics. Experiments involving three-dimensional inhomogeneous deformations were used to verify the predictive capabilities of our model and its finite element implementation. The modeling framework can be adopted for other dynamically crosslinked rate-stiffening soft polymers and polymer gels that are microstructurally similar to PBS.

cond-mat.soft