Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

• URL: http://arxiv.org/abs/2310.03047v2
• Date: Wed, 1 Nov 2023 21:54:35 GMT
• Title: Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning
• Authors: Shang Zhu, Bharath Ramsundar, Emil Annevelink, Hongyi Lin, Adarsh Dave, Pin-Wen Guan, Kevin Gering, Venkatasubramanian Viswanathan
• Abstract summary: We develop a differentiable geometric deep learning model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation. We show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. With a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps.
• Score: 0.3141085922386211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

Related papers

• Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design [20.855235423913285]
  We introduce Uni-ELF, a novel multi-level representation learning framework to advance electrolyte design. Our approach involves two-stage pretraining: reconstructing three-dimensional molecular structures at the molecular level using the Uni-Mol model, and predicting statistical structural properties. Through this comprehensive pretraining, Uni-ELF is able to capture intricate molecular and mixture-level information, which significantly enhances its predictive capability. We believe this innovative framework will pave the way for automated AI-based electrolyte design and engineering.
  arXiv  Detail & Related papers  (2024-07-08T17:26:49Z)
• Trapped-Ion Quantum Simulation of Electron Transfer Models with Tunable Dissipation [1.159879739037684]
  We experimentally simulate a paradigmatic model of molecular electron transfer using a multi-species trapped-ion crystal. We observe the real-time dynamics of the spin excitation, measuring the transfer rate in several regimes of adiabaticity and relaxation dynamics.
  arXiv  Detail & Related papers  (2024-05-16T18:03:17Z)
• BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development [11.682763325188525]
  We introduce BAMBOO, a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. BamBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity, and ionic conductivity. We envision this work as paving the way to a "universal MLFF" capable of simulating properties of common organic liquids.
  arXiv  Detail & Related papers  (2024-04-10T17:31:49Z)
• Orbital-Free Density Functional Theory with Continuous Normalizing Flows [54.710176363763296]
  Orbital-free density functional theory (OF-DFT) provides an alternative approach for calculating the molecular electronic energy. Our model successfully replicates the electronic density for a diverse range of chemical systems.
  arXiv  Detail & Related papers  (2023-11-22T16:42:59Z)
• Gradual Optimization Learning for Conformational Energy Minimization [69.36925478047682]
  Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks significantly reduces the required additional data. Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules.
  arXiv  Detail & Related papers  (2023-11-05T11:48:08Z)
• Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing [61.27691515336054]
  In this study, we tackle the issue of high-performance electrodes for desired battery application conditions. We propose a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.
  arXiv  Detail & Related papers  (2023-07-07T13:48:50Z)
• Real-time equation-of-motion CC cumulant and CC Green's function simulations of photoemission spectra of water and water dimer [54.44073730234714]
  We discuss results obtained with the real-time equation-of-motion CC cumulant approach. We compare the ionization potentials obtained with these methods for the valence region. We analyze unique features of the spectral functions, associated with the position of satellite peaks, obtained with the RT-EOM-CC and CCGF methods.
  arXiv  Detail & Related papers  (2022-05-27T18:16:30Z)
• Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning [0.0]
  We introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimize electrolyte conductivity over a single-salt, ternary solvent design space.
  arXiv  Detail & Related papers  (2021-11-23T00:15:19Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.