Vecchio Lab Research

Professor Vecchio's Research Group focuses on advanced materials discovery and their translation to industrial and DoD applications. The research philosophy employs a 'closing-the-loop' methodology wherein computational approaches lead the materials discovery effort, followed by materials synthesis using a wide array of methods, followed by state-of-the-art materials characterization techniques, the results of which feedback into the computational front end. The materials space includes metallics (both crystalline and amorphous), metallic-intermetallic laminate composites, and high entropy ceramics. For the computational tools, the research uses CALPHAD methodologies, along with collaborations in the DFT and AIMD spaces to search for novel material composition spaces appropriate for development, and combine these approaches with machine learning methods to accelerate un-intuitive material discovery. For materials synthesis we employ a wide array of approaches, including: arc-melting, induction melting, splat-quenching, hot-press sintering, Spark Plasma Sintering (SPS), Flash SPS, and a unique metal 3-D printing machine equipped with the first of its kind, Nuburu 450nm blue laser and a Rapid Experimental Development powder feeder system. One critical aspect of the materials characterization approach employed, in addition to using state-of-the-art instrumentation, is the development of machine-learning augmented decision making, that enables autonomous characterization of the novel materials being developed, significantly reducing the reliance on individual user biases. The results from this autonomous characterization are then fed back into the computational tools used for the materials discovery process to enhance the next loop through the development process.

Research Highlights

Dr. Kenneth Vecchio

Spotlight

Professor of Materials Engineering

NanoEngineering Department, UC San Diego

Recent Publications from the Vecchio Group:
Crystal symmetry determination in electron diffraction using machine learning, K Kaufmann, et al., Science 367 (6477), 564-568, 2020.
Discovery of high-entropy ceramics via machine learning, K Kaufmann, et al., Npj Computational Materials 6 (1), 1-9, 3, 2020.
Cold-workable refractory complex concentrated alloys with tunable microstructure and good room-temperature tensile behavior, C Zhang, et al., Scripta Materialia 188, 16-20, 2020.
Searching for high entropy alloys: A machine learning approach, K Kaufmann, KS Vecchio, Acta Materialia, 2020.
High-Entropy Monoborides: Towards Superhard Materials, M Qin, Q Yan, H Wang, KS Vecchio, J Luo, arXiv preprint arXiv:2007.15454, 2020, to appear in Scripta Materialia.
Deep Neural Network Enabled Space Group Identification in EBSD, K Kaufmann, C Zhu, AS Rosengarten, KS Vecchio, Microscopy and Microanalysis 26 (3), 447-457, 2, 2020.
Phase Mapping in EBSD Using Convolutional Neural Networks, K Kaufmann, et al., Microscopy and Microanalysis 26 (3), 458-468, 2020.
Dissolving and stabilizing soft WB2 and MoB2 phases into high-entropy borides via boron-metals reactive sintering to attain higher hardness, M Qin, J Gild, H Wang, T Harrington, KS Vecchio, J Luo, Journal of the European Ceramic Society, 2020.
Dual-Phase High-Entropy Ultrahigh Temperature Ceramics, M Qin, et al., Journal of the European Ceramic Society, 2020.
Deformation and Fracture Evolution of FeAl-based Metallic-Intermetallic Laminate (MIL) Composites, H Wang, C Zhu, KS Vecchio, Acta Materialia, 2020
Electromigration effect in Fe-Al diffusion couples with field-assisted sintering, H Wang, R Kou, T Harrington, KS Vecchio, Acta Materialia 186, 631-643, 2, 2020.
A computer vision approach to study surface deformation of materials, C Zhu, H Wang, K Kaufmann, KS Vecchio, Measurement Science and Technology 31 (5), 055602, 4, 2020

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