9 citations · 19 across the 11 of their papers we have counts for
8 papers · 1 filter
Lifetime Sample Tracking (LiST): A Data Platform for Materials Science
Anthony Richardella, Isaiah A Moses, Konrad Hilse +9
The 2D Crystal Consortium Materials Innovation Platform (2DCC-MIP) is an NSF supported national user facility focused on advancing the synthesis of 2D materials, monolayers, surfac…
Multi-modal machine learning analysis of GaSe molecular beam epitaxy growth conditions
Mingyu Yu, Isaiah A. Moses, Wesley F. Reinhart +1
Autonomous synthesis platforms integrating machine learning with in situ diagnostics have the potential to revolutionize thin-film growth by enabling real-time process optimization…
Modeling High Entropy Alloys' Mechanical Property through Natural Language-Derived Descriptors
Li-Cheng Hsiao, Zi-Kui Liu, Wesley Reinhart
Processing treatments of alloys, despite being influential to alloy properties, are often neglected in machine-learning aided alloy designs due to the difficulties in expressing th…
Machine Learning Guided Polymorph Selection in Molecular Beam Epitaxy of In2Se3
Ryan Trice, Mingyu Yu, Eric Welp +3
Indium selenide (In2Se3), a layered chalcogenide with multiple polymorphs, is a promising material for optoelectronic and ferroelectric applications. However, achieving polymorph-p…
Cross-Modal Characterization of Thin Film MoS Using Generative Models
Isaiah A. Moses, Chen Chen, Joan M. Redwing +1
The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary c…
Transfer Learning for Multi-material Classification of Transition Metal Dichalcogenides with Atomic Force Microscopy
Isaiah A. Moses, Wesley F. Reinhart
Deep learning models are widely used for the data-driven design of materials based on atomic force microscopy (AFM) and other scanning probe microscopy. These tools enhance efficie…
Crystal Growth Characterization of WSe Thin Film Using Machine Learning
Isaiah A. Moses, Chengyin Wu, Wesley F. Reinhart
Materials characterization remains a labor-intensive process, with a large amount of expert time required to post-process and analyze micrographs. As a result, machine learning has…
Quantitative analysis of MoS thin film micrographs with machine learning
Isaiah A. Moses, Wesley F. Reinhart
Isolating the features associated with different materials growth conditions is important to facilitate the tuning of these conditions for effective materials growth and characteri…