340 citations · 551 across the 9 of their papers we have counts for
9 papers
Exploring material compositions for synthesis using oxidation states
Maung Thway, Andy Paul Chen, Haiwen Dai +9
Recent advances in machine learning techniques have made it possible to use high-throughput screening to identify novel materials with specific properties. However, the large numbe…
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films
Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj +13
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downs…
What Information is Necessary and Sufficient to Predict Materials Properties using Machine Learning?
Siyu Isaac Parker Tian, Aron Walsh, Zekun Ren +2
Conventional wisdom of materials modelling stipulates that both chemical composition and crystal structure are integral in the prediction of physical properties. However, recent de…
Machine learning and high-throughput robust design of P3HT-CNT composite thin films for high electrical conductivity
Daniil Bash, Yongqiang Cai, Vijila Chellappan +16
Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that ma…
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh +14
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limite…
Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization
Felipe Oviedo, Zekun Ren, Xue Hansong +12
Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost. So far, solar ce…
Embedding Physics Domain Knowledge into a Bayesian Network Enables Layer-by-Layer Process Innovation for Photovoltaics
Zekun Ren, Felipe Oviedo, Muang Thway +15
Process optimization of photovoltaic devices is a time-intensive, trial and error endeavor, without full transparency of the underlying physics, and with user-imposed constraints t…
Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis
Shijing Sun, Noor T. P. Hartono, Zekun D. Ren +15
Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique h…
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Felipe Oviedo, Zekun Ren, Shijing Sun +9
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-en…