Search Results for author:
Found 9 papers, 8 papers with code
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)
Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization.
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning.
Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing
With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18. 5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling.
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
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 high-throughput experimentation systems.
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization
Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost.
Applied Physics
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.
