192 citations · 233 across the 7 of their papers we have counts for
7 papers
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)
Alexander E. Siemenn, Zekun Ren, Qianxiao Li +1
Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property op…
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…
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…
Machine learning enables polymer cloud-point engineering via inverse design
Jatin N. Kumar, Qianxiao Li, Karen Y. T. Tang +3
Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We…