Location
Havener Center, Miner Lounge / Wiese Atrium, 1:30pm-3:30pm
Start Date
4-1-2026 1:30 PM
End Date
4-1-2026 3:30 PM
Presentation Date
April 1, 2026; 1:30pm-3:30pm
Description
In this project, the disordered oxide structures will be mapped by a machine-learning algorithm (e.g., HDBSCAN) to identify characteristic behaviors of the proton across different material densities and defect types. This should help identify the under-coordinated, highly distorted, weakly-bonded, and dynamically unstable atoms to predict the most probable H locations. This fast and accurate prediction of energetically favorable H distribution will enable a reliable and fast screening of a large number of AOSs with variable cation and/or anion compositions. The approach will help find AOSs with suppressed numbers of M-OH defects (that form deep electron traps, limiting the number of free carriers and contributing to carrier instabilities under illumination) and with enhanced number of M-H-M defects (yielding better mobility and reduced absorption in the visible range).
Biography
Lucas A. Ethington is a Physics and Computer Science student at Missouri University of Science and Technology with a 4.0 GPA and 107 completed credit hours toward a B.S. degree. He combines strong analytical skills with hands-on technical experience in programming, research, and scientific problem-solving. His background includes work in Python, C++, AVL assembly, Git, Docker, and LaTeX, with additional experience in Java and Lua. Lucas has contributed to academic leadership through the Missouri S&T Student Council, serving as Vice President of Academic Affairs and Executive at Large. His research experience includes applying neural networks to predict hydrogen motion in amorphous indium oxide and continuing analysis of semiconductor behavior. He has also developed a regex engine based on nondeterministic finite automata and optimized an extended Towers of Hanoi problem, including implementation on a microcontroller with severe memory constraints. He is multilingual in English, German, and Portuguese.
Meeting Name
2026 - Miners Solving for Tomorrow Research Conference
Department(s)
Physics
Second Department
Computer Science
Document Type
Poster
Document Version
Final Version
File Type
event
Language(s)
English
Rights
© 2026 The Authors, All rights reserved
Included in
Elucidating complex H behavior in Amorphous Oxide Semiconductors by Machine Learning
Havener Center, Miner Lounge / Wiese Atrium, 1:30pm-3:30pm
In this project, the disordered oxide structures will be mapped by a machine-learning algorithm (e.g., HDBSCAN) to identify characteristic behaviors of the proton across different material densities and defect types. This should help identify the under-coordinated, highly distorted, weakly-bonded, and dynamically unstable atoms to predict the most probable H locations. This fast and accurate prediction of energetically favorable H distribution will enable a reliable and fast screening of a large number of AOSs with variable cation and/or anion compositions. The approach will help find AOSs with suppressed numbers of M-OH defects (that form deep electron traps, limiting the number of free carriers and contributing to carrier instabilities under illumination) and with enhanced number of M-H-M defects (yielding better mobility and reduced absorption in the visible range).
Comments
Advisor: Julia E. Medvedeva, juliaem@mst.edu