.png){kind=icon}
Return
Agents for sequential learning using multiple-fidelity data
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-022-08413-8.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values. The fidelities of data come from the results of DFT calculations as low fidelity and experimental results as high fidelity. We demonstrate performance gains of agents which incorporate multi-fidelity data in two contexts: either using a large body of low fidelity data as a prior knowledge base or acquiring low fidelity data in-tandem with experimental data. This advance provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. This may also serve as a reference point for those who are interested in practical strategies that can be used when multiple data sources are available for active or sequential learning campaigns.
Keywords:
DISCOVERY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
28.3W
Citations:
83.5W
Organization
Cited Papers
ShareSaveSeparation of divalent and monovalent ions using flow-electrode capacitive deionization with nanofiltration membranes.png)
Desalination
DesalinationIF0
PREAI
ShareSaveShareSaveBenchmarking the acceleration of materials discovery by sequential learningCHEMICAL SCIENCEOAAI
CHEMICAL SCIENCEIF7.4
OAAIShareSaveCommentary: The Materials Project: A materials genome approach to accelerating materials innovationAPL MATERIALS
OAAI
APL MATERIALSIF4.5
OAAIShareSaveFirst-principles study of luminescence in Ce-doped inorganic scintillatorsPHYSICAL REVIEW BOAAI
PHYSICAL REVIEW BIF3.7
OAAIShareSave
View more