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Edging Towards a Greener Future

July 29, 2019

Materials scientists at Harvard use computers at the MGHPCC to design better solid-state lithium ion batteries through advanced characterizations and simulation.
As electric cars have evolved, running costs have begun to approach those for their gasoline forebears – more expensive to buy, cheaper to run – but range and charge time remain an outstanding issue. Leading the list of possible game-changers are solid-state lithium ion batteries in which wet electrolyte is replaced with a solid. Simpler, such units have the potential to be a lot cheaper, lighter, and not require liquid cooling. They are expected to also be longer-lasting and fireproof while potentially being much faster-charging too.
A major impediment to the development of solid‐state lithium‐ion batteries, however, are interfacial reactions between ceramic‐sulfide solid‐electrolytes and common electrodes. Such reactivity means that ceramic‐sulfide batteries require a suitable coating material to isolate the electrolyte from the electrode materials.
Xin Li, a materials scientist at Harvard, is focused on the design of new energy-related materials through advanced characterizations and simulations. By combining electrochemistry, electron microscopy, X-ray diffraction, and first-principles simulations, his team uses Harvard’s Odyssey Cluster (housed at the MGHPCC in Holyoke, MA) to understand the relationship between atomistic structure and electrochemical property of materials.
In new work published in the journal Advanced Energy Materials Li, working with graduate student William Fitzhugh and others in Harvard’s Paulson School of Engineering, computationally evaluated the interfacial stability of the lithium sulfide Li10SiP2S12 with over 67 000 potential coating materials. Their study found 2000 materials predicted to form stable interfaces in the cathode voltage range and over 1000 materials for the anode range. Studies like these are invaluable in narrowing the field of potential candidate materials ahead of physical trials.
Systematic assessment through first-principles analysis of large systems like this is of course computationally highly demanding. Innovations introduced in the paper included a new binary‐search algorithm to improve the speed and accuracy for evaluating pseudo and the authors highlight the computational challenges posed by high‐throughput interfacial phase‐diagram calculations as well as pragmatic computational methods they would recommend to make such calculations routinely feasible.
In addition to the over 3000 materials cataloged, representative materials from the anionic classes of oxides, fluorides, and sulfides were also chosen to experimentally demonstrate chemical stability when in contact with Li10SiP2S12. “We chose LiCoO2 as an example cathode material to identify coating compounds that would be stable with both Li10SiP2S12 and a common cathode,” Li explains. “Analyzing the correlation between elemental composition and multiple chemical and electrochemical instability metrics revealed key trends in, amongst others, the role of anion selection.”
Xin says the next step in this work will focus on computationally determining and experimentally realizing the best interface coating material by considering more advanced effects in all-solid-state batteries.
To find out more about this work contact Xin

About the Researcher

Associate Professor of Materials Science
Xin Li

Xin Li is Assistant Professor of Materials Science in the John A. Paulson School of Engineering and Applied Sciences at Harvard University. His research group focuses on the design of new energy-related materials through advanced characterizations and simulations. By combining electrochemistry, (in situ) electron microscopy, (in situ) X-ray diffraction and first-principles simulations, his team seeks to understand the relationship between atomistic structure and electrochemical property of materials.

Publication

William Fitzhugh, Fan Wu, Luhan Ye, Wenye Den, Pengfei Qi, Xin Li (2019), A High‐Throughput Search for Functionally Stable Interfaces in Sulfide Solid‐State Lithium Ion Conductors, Advanced Energy Materials, doi: 10.1002/aenm.201900807

Related

The Culprit of Superconductivity in Cuprates, John A. Paulson School of Engineering and Applied Science News, Harvard University

Links

Xin Li
Li Laboratory

Research projects

A Future of Unmanned Aerial Vehicles
Yale Budget Lab
Volcanic Eruptions Impact on Stratospheric Chemistry & Ozone
The Rhode Island Coastal Hazards Analysis, Modeling, and Prediction System
Towards a Whole Brain Cellular Atlas
Tornado Path Detection
The Kempner Institute – Unlocking Intelligence
The Institute for Experiential AI
Taming the Energy Appetite of AI Models
Surface Behavior
Studying Highly Efficient Biological Solar Energy Systems
Software for Unreliable Quantum Computers
Simulating Large Biomolecular Assemblies
SEQer – Sequence Evaluation in Realtime
Revolutionizing Materials Design with Computational Modeling
Remote Sensing of Earth Systems
QuEra at the MGHPCC
Quantum Computing in Renewable Energy Development
Pulling Back the Quantum Curtain on ‘Weyl Fermions’
New Insights on Binary Black Holes
NeuraChip
Network Attached FPGAs in the OCT
Monte Carlo eXtreme (MCX) – a Physically-Accurate Photon Simulator
Modeling Hydrogels and Elastomers
Modeling Breast Cancer Spread
Measuring Neutrino Mass
Investigating Mantle Flow Through Analyses of Earthquake Wave Propagation
Impact of Marine Heatwaves on Coral Diversity
IceCube: Hunting Neutrinos
Genome Forecasting
Global Consequences of Warming-Induced Arctic River Changes
Fuzzing the Linux Kernel
Exact Gravitational Lensing by Rotating Black Holes
Evolution of Viral Infectious Disease
Evaluating Health Benefits of Stricter US Air Quality Standards
Ephemeral Stream Water Contributions to US Drainage Networks
Energy Transport and Ultrafast Spectroscopy Lab
Electron Heating in Kinetic-Alfvén-Wave Turbulence
Discovering Evolution’s Master Switches
Dexterous Robotic Hands
Developing Advanced Materials for a Sustainable Energy Future
Detecting Protein Concentrations in Assays
Denser Environments Cultivate Larger Galaxies
Deciphering Alzheimer’s Disease
Dancing Frog Genomes
Cyber-Physical Communication Network Security
Avoiding Smash Hits
Analyzing the Gut Microbiome
Adaptive Deep Learning Systems Towards Edge Intelligence
Accelerating Rendering Power
ACAS X: A Family of Next-Generation Collision Avoidance Systems
Neurocognition at the Wu Tsai Institute, Yale
Computational Modeling of Biological Systems
Computational Molecular Ecology
Social Capital and Economic Mobility
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