549 citations · 2.2k across the 23 of their papers we have counts for
23 papers
Autonomous epitaxial atomic-layer synthesis via real-time computer vision of electron diffraction
Haotong Liang, Yunlong Sun, Ryan Paxson +8
Autonomous science platforms which make decisions on the fly are fundamentally changing the outlook for materials development. AI-driven schemes can effectively reduce the total nu…
Quantum Kernel Machine Learning for Autonomous Materials Science
Felix Adams, Daiwei Zhu, David W. Steuerman +2
Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A cruci…
Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization
Tyler B. Martin, Duncan R. Sutherland, Austin McDannald +2
The pace of soft material formulation (re)development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment wh…
Real-time experiment-theory closed-loop interaction for autonomous materials science
Haotong Liang, Chuangye Wang, Heshan Yu +6
Iterative cycles of theoretical prediction and experimental validation are the cornerstone of the modern scientific method. However, the proverbial "closing of the loop" in experim…
Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
Felix Adams, Austin McDannald, Ichiro Takeuchi +1
Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materia…
aflow++: a C++ framework for autonomous materials design
C. Oses, M. Esters, D. Hicks +17
The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades,…
Scalable Multi-Agent Lab Framework for Lab Optimization
A. Gilad Kusne, Austin McDannald
Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and co…
Reproducible Sorbent Materials Foundry for Carbon Capture at Scale
Austin McDannald, Howie Joress, Brian DeCost +9
We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide (CO2), specifically targeting novel metal organi…
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 Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
Logan Saar, Haotong Liang, Alex Wang +4
The next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop. Su…
Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties
Kamal Choudhary, Taner Yildirim, Daniel Siderius +3
The increasing CO2 level is a critical concern and suitable materials are needed to capture such gases from the environment. While experimental and conventional computational metho…
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 Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning
Austin McDannald, Matthias Frontzek, Andrei T. Savici +8
Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and dynamics of materials. However, instruments at neutron scattering fac…
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid +25
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional the…
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…
Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis
Shijing Sun, Noor T. P. Hartono, Zekun D. Ren +15
Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique h…
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Felipe Oviedo, Zekun Ren, Shijing Sun +9
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-en…
Machine-learning guided discovery of a high-performance spin-driven thermoelectric material
Yuma Iwasaki, Ichiro Takeuchi, Valentin Stanev +10
Thermoelectric conversion using Seebeck effect for generation of electricity is becoming an indispensable technology for energy harvesting and smart thermal management. Recently, t…
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne +3
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and a…
Machine learning modeling of superconducting critical temperature
Valentin Stanev, Corey Oses, A. Gilad Kusne +4
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood;…