An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

1 code implementation • 15 May 2020 • Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G. Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

1 code implementation • 23 May 2021 • Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi

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.

Active Learning Benchmarking +2

Scalable Bayesian Optimization Accelerates Process Optimization of Penicillin Production

1 code implementation • NeurIPS Workshop AI4Scien 2021 • Qiaohao Liang, Lipeng Lai

While Bayesian Optimization (BO) has emerged as sample-efficient optimization method for accelerating drug discovery, it has rarely been applied to the process optimization of pharmaceutical manufacturing, which traditionally has relied on human-intuition, along with trial-and-error and slow cycles of learning.

Bayesian Optimization Drug Discovery