240 citations · 471 across the 5 of their papers we have counts for
6 papers
Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution
Jewook Park, Hoyeon Jeon, Dongwon Shin +4
Unambiguous identification of Majorana zero modes (MZMs) in topological superconductors (TSCs) remains a challenge due to complex in-gap states that can also produce zero-bias cond…
A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions
Paul Laiu, Ying Yang, Massimiliano Lupo Pasini +2
We present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a…
Data Analytics Approach to Predict High-Temperature Cyclic Oxidation Kinetics of NiCr-based Alloys
Jian Peng, Rishi Pillai, Marie Romedenne +4
Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the i…
Coupling Physics in Machine Learning to Predict Properties of High-temperatures Alloys
Jian Peng, Yukinori Yamamoto, Jeffrey A. Hawk +2
High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into…
Solute-Vacancy Clustering in Aluminum
Jian Peng, Sumit Bahl, Amit Shyam +2
We present an extensive first-principles database of solute-vacancy, homoatomic, heteroatomic solute-solute, and solute-solute-vacancy binding energies of relevant alloying element…
Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys
Dongwon Shin, Yukinori Yamamoto, Michael P. Brady +2
A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We introduce a moder…