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Sparks, Taylor D.

Publications and source records attributed to Sparks, Taylor D..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept

This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical experiments. Frugal twins range from simple toy examples to low-cost surrogates of high-cost research systems. For example, a color-mixing self-driving laboratory (SDL) can serve as a low-cost version of a costly multi-step chemical discovery SDL. Frugal twins already provide hands-on experience for SDLs with low costs and low risks. They can also offer as test beds for software prototyping (e.g., optimization, data infrastructure), and a low barrier to entry for democratizing SDLs. However, there is room for improvement. The true value of frugal twins can be realized in three core areas. Firstly, hardware and software modularity; secondly, purpose-built design (human-inspired vs. hardware-centric vs. human-in-the-loop); and thirdly state-of-the-art (SOTA) software (e.g., multi-fidelity optimization). We also describe the ethical benefits and risks that come with the democratization of science through frugal twins. For future work, we suggest ideas for new frugal twins, SDL educational course outcomes, and a classification scheme for autonomy levels.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reversible Electrochemical Lithium Cycling in a Vanadium(IV)- and Niobium(V)-Based Wadsley–Roth Phase

Fast charging remains one of the greatest safety challenges in Li-ion batteries due to Li-dendrite growth occurring on graphite anodes if they are lithiated too quickly. The search for high-rate anodes has highlighted materials in the Wadsley–Roth (WR) shear phase family. The relative abundance of V compared with traditional WR compositions of Nb and W makes V-based phases attractive. However, the high voltage and poor reversibility typically associated with V redox have made V-rich WR phases less studied than Nb- and W-rich phases. Here, we show that a new V-rich Wadsley–Roth phase, V 7 Nb 6 O 29 , achieves excellent rate capability and 80% capacity retention after 228 cycles with a relatively low average voltage of 1.76 V vs Li/Li + compared with other V-rich WR phases. Single-crystal X-ray diffraction reveals a P4/m space group with repeating 2 × 2 × ∞ and 3 × 3 × ∞ blocks of V 4+ and Nb 5+ octahedra. Finally, combined neutron pair distribution function analysis, X-ray absorption spectroscopy, and density functional theory calculations show that V redox is the primary source of capacity and that cycling stability is provided by the stable octahedral coordination adopted by V 4+ in the material.

25 ENERGY STORAGE↗
DOE OSTI2022

Real-space visualization of short-range antiferromagnetic correlations in a magnetically enhanced thermoelectric

Short-range magnetic correlations can significantly increase the thermopower of magnetic semiconductors, representing a noteworthy development in the decades-long effort to develop high-performance thermoelectric materials. Here, we reveal the nature of the thermopower-enhancing magnetic correlations in the antiferromagnetic semiconductor MnTe. Using magnetic pair distribution function analysis of neutron scattering data, we obtain a detailed, real-space view of robust, nanometer-scale, antiferromagnetic correlations that persist into the paramagnetic phase above the Neel temperature $T_N$ = 307 K. In this work, the magnetic correlation length in the paramagnetic state is significantly longer along the crystallographic c axis than within the ab plane, pointing to anisotropic magnetic interactions. Ab initio calculations of the spin-spin correlations using density functional theory in the disordered local moment approach reproduce this result with quantitative accuracy. These findings constitute the first real-space picture of short-range spin correlations in a magnetically enhanced thermoelectric and inform future efforts to optimize thermoelectric performance by magnetic means.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.

36 MATERIALS SCIENCE↗