ArticleNature communications2024
Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential.The journal of physical chemistry letters · 2026Article
- Bridging quantum mechanics to liquid properties via a universal organic force field.Nature communications · 2026Article
- CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning.Nature communications · 2026Article
- High-fidelity hierarchical modeling of lithium-ion batteries: a cross-scale electrochemical-mechanical framework.Communications engineering · 2025Article
- Toward Zero-Excess Alkali Metal Batteries: Bridging Experimental and Computational Insights.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries.Nature communications · 2025Article
- Multitarget Generate Electrolyte Additive for Lithium Metal Batteries.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.