340 citations · 609 across the 14 of their papers we have counts for
14 papers
PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories
Zekun Ren, Hongzhao Tan, Jiaen Yee +1
Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (G…
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Zeyu Wang, Shuya Yamazaki, Martin Hoffmann Petersen +11
The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantial…
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 with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing
Zhe Liu, Nicholas Rolston, Austin C. Flick +4
Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commer…
Accelerated automated screening of viscous graphene suspensions with various surfactants for optimal electrical conductivity
Daniil Bash, Frederick Hubert Chenardi, Zekun Ren +5
Functional composite thin films have a wide variety of applications in flexible and/or electronic devices, telecommunications and multifunctional emerging coatings. Rapid screening…
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
Qiaohao Liang, Aldair E. Gongora, Zekun Ren +12
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and hig…
Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning
Armi Tiihonen, Sarah J. Cox-Vazquez, Qiaohao Liang +12
New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug, takes decades. Although progress…
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…