Electrolyte Genome
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The Electrolyte Genome Project
Traditional chemistry relies on intuition and experience to select a few materials that might work well for new electrolytes. The Electrolyte Genome streamlines this process by evaluating thousands of materials by simulation on the computer and choosing the most promising few for synthesis in the laboratory. Read More
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Redox Mediators that Promote Three-Dimensional Growth of Li2S on Carbon Current Collectors in Lithium-Sulfur Batteries
Developed, from computation and experiment, redox mediators that allow 3-D growth of Li2S on carbon current collectors for greater capacity utilization in Li-S batteries Read More
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Promising Directions and the Tools to Get Us There
At the halfway point in our five-year charter, we have narrowed our research directions within the three promising energy storage concepts we are pursuing: multivalent intercalation, chemical transformation, non-aqueous redox flow. In converging these directions to proof-of-concept prototypes, we make extensive use of JCESR’s distinguishing tools. Read More
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SC15 High Performance Computing (HPC) Transforms Batteries
A new breakthrough battery—one that has significantly higher energy, lasts longer, and is cheaper and safer—will likely be impossible without a new material discovery. Kristin Persson and other JCESR scientists at Lawrence Berkeley National Laboratory are taking some of the guesswork out of the discovery process with the Electrolyte Genome Project. Read More
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Electrolyte Genome Reveals New Instability Mechanism in Mg Electrolytes
imulations of a matrix of Mg salt and solvent combinations revealed a strong tendency to ion pair formation. Close association of the salt anion and cation within the first solvation shell, even at modest concentrations. Read More
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Accelerating Electrolyte Discovery for Energy Storage with High Throughput Screening
We have developed a strategy to down-select a pool of candidates based on successive property evaluations and to eliminate unpromising candidates at an early stage. Read More
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Realizing the Electrolyte Genome
JCESR is building a highly sophisticated infrastructure for high-throughput evaluation of molecular properties. This involves coupling ab initio DFT methods with rapid classic molecular dynamics, calculations of redox potentials, and solvation structure while more detailed reactivity studies are carried out. Candidates that meet performance metrics for different types of battery applications (redox-flow, Li-air, Li-sulfur and multivalent) are prioritized for synthesis and testing. Read More
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EDL: Discovering Electrochemistry at Atomic and Molecular Levels
Established in situ Electrochemical Discovery Lab (EDL) for systematic studies of wet and dry interfaces for materials-by-design and electrolytes-by-design at atomic and molecular levels Read More
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Architecture-Controlled Ring-Opening Polymerization for Dynamic Covalent Poly(disulfide)s
We reported a strategy to access different topologies of redox-active poly(disulfide)s by ring-opening polymerization. Control over polymerization enables synthesis of high molecular-weight polymers. The polymers undergo catalytic depolymerization to recycle monomer; a promising feature for sustainable flow batteries. Read More
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Adsorption and Thermal Decomposition of Electrolytes on Nanometer Magnesium Oxide: An in Situ 13C MAS NMR Study
The structural and chemical evolution of electrolyte constituents at the nanometric MgO surface were identified, providing a fundamental understanding of heterogeneous interphase evolution. Read More
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Mechanism-Based Design of a High-Potential Catholyte Enables a 3.2 V All-Organic Nonaqueous Redox Flow Battery
Development of an extremely high-potential catholyte leads to the first 3.2 V all-organic flow battery. Read More
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Shedding X‑ray Light on the Interfacial Electrochemistry of Silicon Anodes for Li-Ion Batteries
Our results shed light on the interfacial electrochemistry of silicon anodes for Lithium-ion batteries (LiBs), providing important mechanistic insight into nanometer scale phenomena and how these influence battery performance. Read More
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Accelerating Electrolyte Discovery for Energy Storage through Machine Learning
Utilized high performance computing to generate a database of highly accurate quantum chemical energies of 133 K organic molecules and used machine learning to enable prediction of energies from low fidelity, low cost quantum chemical calculations. Read More
