Scientific Tools

The simulation of perfect crystalline materials for cathodes with the Materials Project and of organic molecules for electrolytes with the Electrolyte Genome allows thousands of new materials to be explored for energy storage applications. Multimodal characterization of materials by X-rays, infrared spectroscopy, electron microscopy, and nuclear magnetic resonance enables rapid fundamental understanding of the atomic and molecular origins of overall materials behavior. JCESR continues to significantly enhance the capabilities of these incisive tools and apply them in new contexts.

Materials Project and Electrolyte Genome

By providing materials researchers with the atomic and molecular level information they need to design more effectively, the Materials Project and Electrolyte Genome accelerate the discovery process in battery electrodes and in electrolytes. These tools will be extended in JCESR’s second five years to include defects and disorder in crystalline materials and complex hierarchical structures in organic molecules.

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Machine Learning

Machine learning enables systems to learn automatically, based on patterns in data, and make better searches, decisions, or predictions. For this reason, it has become increasingly important to scientific discovery. JCESR will integrate machine learning with computational and experimental approaches to accelerate electrolyte discovery to help fill fundamental science gaps that prevent molecular level design of beyond-lithium-ion energy storage materials.

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Multimodal Characterization

Within JCESR, we have integrated a collection of synthetic and electroanalytical laboratories that, together, create signature research tools for our members. The integration of these labs provides the ability to synthesize model electrode surfaces that are fully characterized (chemically, structurally, and electronically), and then transfer these surfaces directly into ultrapure electrochemical environments without exposing the materials to the atmosphere.

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