most citedBenchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

11 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

physics.app-ph2023★ 1 cited

Autonomous Discovery of Tough Structures

Kelsey L. Snapp, Benjamin Verdier, Aldair Gongora +6

A key feature of mechanical structures ranging from crumple zones in cars to padding in packaging is their ability to provide protection by absorbing mechanical energy. Designing s…

cond-mat.mtrl-sci2023★ 1 cited

What is missing in autonomous discovery: Open challenges for the community

Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird +16

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has give…

physics.med-ph2023

Bayesian Reconstruction of Magnetic Resonance Images using Gaussian Processes

Yihong Xu, Chad W. Farris, Stephan W. Anderson +2

A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations suc…

cond-mat.mtrl-sci2021

Designing Composites with Target Effective Young's Modulus using Reinforcement Learning

Aldair E. Gongora, Siddharth Mysore, Beichen Li +5

Advancements in additive manufacturing have enabled design and fabrication of materials and structures not previously realizable. In particular, the design space of composite mater…

cond-mat.mtrl-sci2021★ 11 cited

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…