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Found 21 papers, 2 papers with code
Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning
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 Turbulence II
Differentiable fluid simulators are increasingly demonstrating value as useful tools for developing data-driven models in computational fluid dynamics (CFD).
Differentiable Turbulence: Closure as a partial differential equation constrained optimization
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: An Ecosystem for Atomistic Scientific Machine Learning
Chemellia is an open-source framework for atomistic machine learning in the Julia programming language.
Importance of equivariant and invariant symmetries for fluid flow modeling
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics.
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries.
Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes.
Differentiable physics-enabled closure modeling for Burgers' turbulence
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences.
Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap
Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade.
Score-Based Generative Models for Molecule Generation
Recent advances in generative models have made exploring design spaces easier for de novo molecule generation.
Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning
In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte.
Cell-Level State of Charge Estimation for Battery Packs Under Minimal Sensing
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: A Position Piece
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.
The promise of energy-efficient battery-powered urban aircraft
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.
AutoMat: Accelerated Computational Electrochemical systems Discovery
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.
Closed-Loop Design of Proton Donors for Lithium-Mediated Ammonia Synthesis with Interpretable Models and Molecular Machine Learning
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.
Universal Battery Performance and Degradation Model for Electric Aircraft
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.
Machine Learning Enabled Discovery of Application Dependent Design Principles for Two-dimensional Materials
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.
Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases.
Dendrite suppression of metal electrodeposition with liquid crystalline electrolytes
We study the dynamics of electrodeposition of a metal in contact with a liquid crystalline electrolyte.
Applied Physics Chemical Physics
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth.
