Lifetime Sample Tracking (LiST): A data platform for materials science

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.

Authors

Institutions

Publication Details

Journal
APL Machine Learning
Published
2026-10-13
DOI
https://doi.org/10.1063/5.0344984
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lifetime Sample Tracking (LiST): A data platform for materials science

R Richardella Anthony, Joan Marie Redwing, Wesley F. Reinhart, Konrad Hilse et al.
APL Machine Learning
Machine Learning in Materials Science
article

Lifetime Sample Tracking (LiST): A data platform for materials science

R Richardella Anthony, Joan Marie Redwing, Wesley F. Reinhart, Konrad Hilse, Vincent H. Crespi, Adri C. T. van Duin, Nitin Samarth, Isaiah A. Moses, Frank Santaguida, Saiyyam Kochar, Kevin Dressler, Ric Wilburn
article en

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.

APL Machine LearningVol. 4(4)
Argonne National Laboratory (US), Pennsylvania State University (US)
National Science Foundation, U.S. Department of Energy, Office of Science, Division of Materials Research, Energy Frontier Research Centers
Openalex Percentile: Top 68%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.