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Electrodeposition

Electrodeposition

Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
Electrodeposition can...

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Machine learning pipelines for the design of solid-state electrolytes.

Vinamr Jain¹, Zhilong Wang¹, Fengqi You^1,2,3

¹College of Engineering, Cornell University, Ithaca, New York 14853, USA. fengqi.you@cornell.edu.

Materials Horizons

|December 8, 2025

Summary

This summary is machine-generated.

Artificial intelligence accelerates the discovery of solid-state electrolytes (SSEs) for safer batteries. Machine learning models predict ionic conductivity and generative approaches propose novel materials, overcoming data gaps for multivalent systems.

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Area of Science:

Materials Science
Electrochemistry
Artificial Intelligence

Background:

Solid-state electrolytes (SSEs) are crucial for safer, high-energy-density batteries.
Discovering new inorganic SSEs is challenging due to vast chemical spaces and limited data, especially for multivalent ions.
Existing research often overlooks multivalent conductor systems (Mg2+, Ca2+, Zn2+, Al3+).

Purpose of the Study:

To present a systematic framework connecting SSE discovery challenges with AI solutions.
To provide a strategic roadmap for researchers in AI-accelerated materials discovery.
To address the specific data gaps and challenges associated with multivalent SSEs.

Main Methods:

Comprehensive survey of machine learning (ML) pipelines, including data resources, feature engineering, classical models, deep learning, and generative approaches.
Utilizing ML interatomic potentials for large-scale molecular dynamics simulations.
Employing advanced neural network architectures (e.g., transformers, GNNs) for ionic conductivity prediction.
Implementing generative models and autonomous closed-loop discovery platforms.

Main Results:

ML interatomic potentials enable accurate, microsecond-scale simulations, revealing non-Arrhenius transport.
Advanced neural networks achieve high accuracy in predicting ionic conductivity across diverse chemical spaces.
Generative models successfully propose and validate novel SSE compositions.
Autonomous platforms demonstrate order-of-magnitude efficiency gains in materials discovery.

Conclusions:

AI offers powerful solutions to accelerate the discovery of novel solid-state electrolytes.
Hybrid workflows combining conventional computational methods with ML overcome individual limitations.
Addressing data gaps for multivalent systems through transfer and active learning is critical.
Recommendations for multi-objective optimization, explainable AI, and physics-informed models guide future research.