Bryce Meredig, Ph.D.

Affiliations: 
2007-2012 Materials Science and Engineering Northwestern University, Evanston, IL 
 2014- Citrine Informatics 
Area:
computational materials science, energy storage
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"Bryce Meredig"
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Parents

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Chris Wolverton grad student 2012 Northwestern
 (Data-Driven Computational Methods for Materials Characterization, Classification, and Discovery.)
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Publications

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Saal JE, Oliynyk AO, Meredig B. (2020) Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches Annual Review of Materials Research. 50: 49-69
Meredig B. (2019) Five High-Impact Research Areas in Machine Learning for Materials Science Chemistry of Materials. 31: 9579-9581
Ward L, Aykol M, Blaiszik B, et al. (2018) Strategies for accelerating the adoption of materials informatics Mrs Bulletin. 43: 683-689
Spear AD, Kalidindi SR, Meredig B, et al. (2018) Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior Jom. 70: 1143-1146
Ling J, Hutchinson M, Antono E, et al. (2017) Building data-driven models with microstructural images: Generalization and interpretability Materials Discovery. 10: 19-28
Hart GL, Nelson LJ, Vanfleet RR, et al. (2017) Revisiting the revised Ag-Pt phase diagram Acta Materialia. 124: 325-332
Ling J, Hutchinson M, Antono E, et al. (2017) High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates Integrating Materials and Manufacturing Innovation. 6: 207-217
Hill J, Mulholland G, Persson K, et al. (2016) Materials science with large-scale data and informatics: Unlocking new opportunities Mrs Bulletin. 41: 399-409
Michel K, Meredig B. (2016) Beyond bulk single crystals: A data format for all materials structure–property–processing relationships Mrs Bulletin. 41: 617-623
Gaultois MW, Oliynyk AO, Mar A, et al. (2016) Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties Apl Materials. 4
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