9 papers
Benchmarking Active Learning Strategies for Materials Optimization and Discovery
Alex Wang, Haotong Liang, Austin McDannald +2
Autonomous physical science is revolutionizing materials science. In these systems, machine learning controls experiment design, execution, and analysis in a closed loop. Active le…
A Semi-Supervised Approach for Automatic Crystal Structure Classification
Satvik Lolla, Haotong Liang, A. Gilad Kusne +2
The structural solution problem can be a daunting and time consuming task. Especially in the presence of impurity phases, current methods such as indexing become more unstable. In…
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
A. Gilad Kusne, Heshan Yu, Changming Wu +13
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to gu…
CRYSPNet: Crystal Structure Predictions via Neural Network
Haotong Liang, Valentin Stanev, A. Gilad Kusne +1
Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics. However, predicting crystal structure…
Scientific AI in materials science: a path to a sustainable and scalable paradigm
Brian DeCost, Jason Hattrick-Simpers, Zachary Trautt +3
Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, an…
Designing over uncertain outcomes with stochastic sampling Bayesian optimization
Peter D. Tonner, Daniel V. Samarov, A. Gilad Kusne
Optimization is becoming increasingly common in scientific and engineering domains. Oftentimes, these problems involve various levels of stochasticity or uncertainty in generating…