Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces.
Differentiable fluid simulators are increasingly demonstrating value as useful tools for developing data-driven models in computational fluid dynamics (CFD).
Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES).
Chemellia is an open-source framework for atomistic machine learning in the Julia programming language.
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics.
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries.
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes.
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences.
Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade.
Recent advances in generative models have made exploring design spaces easier for de novo molecule generation.
In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte.
This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation in a large-scale battery pack under minimal sensing, where only pack-level voltage and current are measured.
Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation.
Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 miles of range with payload equivalents of up to 7 passengers.
no code implementations • 3 Nov 2020 • Emil Annevelink, Rachel Kurchin, Eric Muckley, Lance Kavalsky, Vinay I. Hegde, Valentin Sulzer, Shang Zhu, Jiankun Pu, David Farina, Matthew Johnson, Dhairya Gandhi, Adarsh Dave, Hongyi Lin, Alan Edelman, Bharath Ramsundar, James Saal, Christopher Rackauckas, Viral Shah, Bryce Meredig, Venkatasubramanian Viswanathan
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation.
In this work, we experimentally determined the efficacy of several classes of proton donors for lithium-mediated electrochemical nitrogen reduction in a tetrahydrofuran-based electrolyte, an attractive alternative method for producing ammonia.
We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning in the form of Universal Ordinary Differential Equations (U-ODE's) combined with an electrochemical cell model and degradation models which include solid electrolyte interphase (SEI) growth, lithium plating, and charge loss.
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations.
Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases.
We study the dynamics of electrodeposition of a metal in contact with a liquid crystalline electrolyte.
Applied Physics Chemical Physics
We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth.