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Interpretable and Explainable Machine Learning for Materials Science and Chemistry
Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi +1
While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successf…
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
Rishi E. Kumar, Armi Tiihonen, Shijing Sun +3
While halide perovskites attract significant academic attention, examples of at-scale industrial production are still sparse. In this perspective, we review practical challenges hi…
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…
Discovering Equations that Govern Experimental Materials Stability under Environmental Stress using Scientific Machine Learning
Richa Ramesh Naik, Armi Tiihonen, Janak Thapa +4
While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, inductive reasoning and knowledge extraction remain elusive tasks, in part…
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…
Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop
Alexander E. Siemenn, Matthew Beveridge, Tonio Buonassisi +1
High-performance semiconductor optoelectronics such as perovskites have high-dimensional and vast composition spaces that govern the performance properties of the material. To cost…
A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation
Alexander E. Siemenn, Evyatar Shaulsky, Matthew Beveridge +3
Generating droplets from a continuous stream of fluid requires precise tuning of a device to find optimized control parameter conditions. It is analytically intractable to compute…
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…