Lifetime Sample Tracking (LiST): A data platform for materials science
Authors:
- Anthony RichardellaARAnthony Richardella
- Isaiah A. MosesIMIsaiah A. Moses
- Konrad HilseKHKonrad Hilse
- Kevin DresslerKDKevin Dressler
- Ric WilburnRWRic Wilburn
- Saiyyam KocharSKSaiyyam Kochar
- Wesley F. ReinhartWRWesley F. Reinhart
- Adri C. T. van DuinADAdri C. T. van Duin
- Nitin SamarthNSNitin Samarth
- Vincent H. CrespiVCVincent H. Crespi
- Joan M. RedwingJRJoan M. Redwing
Open Access
- 13 October 2026
- journal article
- Published by AIP Publishing in APL Machine Learning
- Vol. 4 (4)
Abstract
The 2D Crystal Consortium Materials Innovation Platform (2DCC-MIP) is a NSF supported national user facility focused on advancing the synthesis of 2D materials, monolayers, surfaces, and interfaces. The need for the facility to organize and share data with users led to the development of an internal data management and analysis engine, the Lifetime Sample Tracking platform (LiST). This infrastructure allows the automated capture, curation, analysis, and dissemination of data ranging from experimental materials synthesis parameters and characterization to theoretical first-principles and ReaxFF molecular dynamics modeling [Nayir et al., 2D Mater. 10, 032002 (2023)]. The system currently hosts synthesis and property data (accessible via a REST API) on ∼20 000 samples produced by the 2DCC grown using a variety of techniques from bulk crystal growth to metal-organic chemical vapor deposition and molecular beam epitaxy, among others. Data used in publications can easily be grouped by the system into data packages that are given digital object identifiers for inclusion with each publication. The LiST platform is now being used by groups outside of the 2DCC as a solution for data curation in materials science. Data management tools, such as LiST, support the materials development process by allowing a closed loop iteration between synthesis, characterization, theory, and targeted materials design. This also enables machine learning research, artificial intelligence analysis, and the potential for autonomous synthesis in the future.Keywords
Funding Information
- National Science Foundation (DMR-1539916)
- National Science Foundation (DMR-2039351)
- U.S. Department of Energy (DE-SC0021118)
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