Skip to content

Combinatorial Synthesis and Automated Analytics for Material Exploration of Solid Electrolytes

Jul 2026 · ECS Meeting Abstracts · Vol MA2026-01, pp. 696-696 · 0 citations

TL;DR

A high-throughput experimental platform integrating composition-gradient thin-film synthesis, automated structural and electrochemical characterization, and machine-learning pipelines for exploring pseudo-ternary systems for solid electrolytes with high ionic conductivity is presented.

Abstract

The development of solid electrolytes with high ionic conductivity is crucial for advancing all-solid-state batteries. However, conventional materials discovery approaches are hindered by experimental inefficiencies and analytical bottlenecks. Here, we present a high-throughput experimental platform integrating composition-gradient thin-film synthesis, automated structural and electrochemical characterization, and machine-learning pipelines for exploring pseudo-ternary systems. Composition-gradient thin films on 4-inch Si wafers were fabricated by co-sputtering of three targets. Synchrotron X-ray diffraction (SXRD) at SPring-8 BL28XU, equipped with automated sample exchange and XY-stage positioning, allows rapid structural mapping. Non-negative matrix factorization (NMF) first extracts latent phase information as basis patterns with corresponding phase fractions. These basis patterns are subsequently clustered using DBSCAN with dynamic time warping (DTW) distance metrics, which effectively groups solid solutions exhibiting continuous peak shifts into single clusters. Electrochemical impedance spectroscopy was performed using an automated XY-stage with a Z-axis contact probe system. EIS analysis employs Bayesian-navigated equivalent-circuit model (ECM) fitting to ensure consistent and automated extraction of bulk and grain-boundary conductivities. To validate the platform, we investigated the CeF 3 –LaF 3 –SrF 2 pseudo-ternary system for fluoride-ion conductors. SXRD analysis revealed distinct formation regions for tysonite and fluorite structures. The ionic conductivity mapping revealed that Ce-rich tysonite exhibited bulk conductivities exceeding 10 –4 S cm –1 , with values decreasing sharply in the two-phase region and reaching approximately 10 –8 S cm –1 for the fluorite phase. This integrated approach establishes a framework for accelerated discovery and optimization of solid electrolytes. Acknowledgements: This study was conducted using a grant from the project (JPNP21006) commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

View source

Similar papers

Review Open access Aug 2026

AI-Driven Rational Design of Solid-State Electrolytes

The solid-state electrolytes (SSE) are gaining tremendous attention in designing rechargeable batteries with remarkable energy density and safety features for next-generation energy storage device applications. The rational design of SSE with promising ionic conductivity, higher electrochemical stability windows, and stable electrode-electrolyte interfaces remain a formidable challenge, traditionally hindered by trial-and-error experimentation and computationally expensive theoretical simulations. Here, we systematically review the recent breakthroughs in the artificial intelligence (AI)-driven design of SSE, spanning electrochemical stability and ionic conductivity domains, with a particular focus on how machine learning (ML) and deep learning (DL) are fundamentally transforming the discovery and optimization landscape. We critically discuss the synergy between first-principles density functional theory (DFT), molecular dynamics (MD) simulations, and advanced AI algorithms including supervised and unsupervised learning (SL, UL), graph neural networks (GNNs), and Machine Learning Interatomic Potentials (MLIP) that collectively enable accurate prediction of ionic conductivity, elucidation of ion transport mechanisms, and high-throughput screening (HTS) of vast chemical spaces. Emphasis is placed on descriptor engineering that bridges atomic-level structural features (e.g., lattice parameters, activation energies, defect chemistry) with macroscopic electrochemical performance, as well as the emerging paradigm of closed-loop, self-driving laboratories for autonomous materials discovery. Furthermore, AI-guided strategies have demonstrated remarkable interfacial ionic transport mechanism. Despite these transformative advances, persistent challenges including data scarcity, limited descriptor transferability, discrepancies between theoretical predictions and experimental realization, remain significant challenges. Looking forward, the convergence of AI with high-throughput experimentation and multiscale modeling promises to redefine SSE discovery, accelerating the deployment of high-performance all solid-state batteries (ASSBs) for sustainable energy storage.

Jiaying He, Zama Jan, Heqin Guo et al. · 0 citations
Review Aug 2026

Machine Learning and Theoretical Computation Synergy Advancing Halide Electrolytes Toward All‐Solid‐State Lithium Batteries: Recent Advances, Challenges, and Perspectives

This work aims to establish a foundational roadmap for the data‐driven and rational design of advanced HSSEs, thereby advancing the realization of next‐generation ASSLBs.

Jiahui Ye, Ming Gao, Minyu Jia et al. · 0 citations
Review Open access Aug 2026

Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.

Due to their high specific energy, lithium-metal batteries (LMBs) are widely regarded as the promising next-generation energy storage devices. Nevertheless, their practical applications are plagued by the challenges of irregular deposition and dissolution, coupled with the high chemical reactivity of lithium electrodes. Extensive research has focused on the stabilization of electrode-electrolyte interfaces as the key strategy to achieve improved battery performance. However, the exploration process via traditional "trial-and-error" methodologies is impeded by the long period and high cost of the tedious experiments. Machine learning (ML) technologies have become a mainstream force, redefining the revolutionary paradigm, enabling intelligently capturing the complex structure-performance relationships across vast compositional and structural spaces. Herein, ML applications in the discovery of electrolytes, electrodes, and interface engineering are reviewed, with the emphasis on ML-driven investigation workflow covering data collection, feature engineering, model selection and ML-assisted simulations. Moreover, task-oriented ML technologies for expediting materials screening, informative descriptors extraction, mechanistic elucidation, and reverse design of novel electrodes and electrolytes are highlighted. Finally, future trajectories centered on multiscale materials simulation, multimodal modeling, and intelligent platform establishment to overcome persistent challenges are outlined, aiming at catalyzing the rational design of highly stable lithium electrode-electrolyte interface for long-lasting rechargeable LMBs.

Xiaorui Liu, Qingyu Li, Jianghao Liang et al. · 0 citations
Review Open access 2026

AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review

The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for navigating this complexity, enabling rapid prediction of electrochemical properties, de novo design of polymer electrolytes, and precise optimization of nanostructures for supercapacitors and batteries. This review systematically examines the application of ML techniques, including graph neural networks, Bayesian optimization, variational autoencoders, and transformer-based language models, for the discovery of energy storage polymer composites. The discussion critically evaluates ML-driven advancements across lithium-ion batteries, flexible energy storage devices, and solar energy materials, drawing on quantitative performance benchmarks reported in the primary literature. Emerging strategies such as active learning, multi-fidelity data fusion, physics-informed neural networks, and polymer-specific foundation models are discussed alongside persistent challenges related to data scarcity, model interpretability, and the translation gap between computational prediction and experimental synthesis. The review further addresses the landscape of open polymer property databases, the role of autonomous closed-loop experimentation in accelerating materials discovery, and the importance of reproducible, well-documented machine learning pipelines for the field to mature beyond proof-of-concept demonstrations. By consolidating evidence from verified primary sources and presenting original comparative analyses across methods and application domains, this review provides researchers, materials scientists, and computational chemists with an actionable, evidence-based perspective on the current state and future trajectory of AI-accelerated, sustainable energy storage polymer composite discovery.

Manas Kumar Yogi, D. Uma, Yamuna Mundru et al. · 0 citations
Review Aug 2026

Toward accelerated electrocatalyst design: synergistic integration of DFT, machine learning, and microkinetic modeling.

Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.

Swetarekha Ram, Shalini Tomar, S. Bhattacharjee · 0 citations
Review Aug 2026

Progress on Halide Solid State Electrolytes: Structural Regulation, Interfacial Engineering, and Machine Learning Driven Paradigms

Halide solid state electrolytes are applicable for all-solid-state batteries due to their favorable ionic conductivity, desirable mechanical deformability, and wide electrochemical stability window. Here we review recent advances in both lithium-based and sodium-based halide electrolytes. Beginning with materials classification and synthesis methods, this review delves into the effects of crystal framework architecture, coordination environment, and the synergistic regulation of occupancy and vacancies on ion conduction mechanisms, particularly emphasizing the advantages of amorphous halide solid state electrolytes. The underlying origins of air instability and electrolyte/interface failure in halide-based batteries are systematically summarized, followed by a discussion of stability enhancement strategies, including elemental doping, interfacial engineering, and structural optimization. Notably, this review introduces a machine learning perspective, exploring its applications in materials screening, elucidation of ion transport mechanisms, and prediction of interfacial reactions. This review provides a theoretical foundation and technical guidance for the rational design of all-solid-state batteries.

Chang Liu, Xingkun Liu, Chun-Jing Sun · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.