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Found 12 papers, 4 papers with code
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning
Autonomous materials research labs require the ability to combine and learn from diverse data streams.
Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping.
Scalable Multi-Agent Lab Framework for Lab Optimization
We demonstrate this framework with an autonomous material science lab in mind - where information from diverse research campaigns can be combined to ad-dress the scientific question at hand.
Benchmarking Active Learning Strategies for Materials Optimization and Discovery
In these systems, machine learning controls experiment design, execution, and analysis in a closed loop.
A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
We present the next generation in science education, a kit for building a low-cost autonomous scientist.
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
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 guide his discovery of celestial mechanics [1].
CRYSPNet: Crystal Structure Predictions via Neural Network
Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics.
Designing over uncertain outcomes with stochastic sampling Bayesian optimization
Optimization is becoming increasingly common in scientific and engineering domains.
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.
Machine learning modeling of superconducting critical temperature
Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds.
