Themed collection Advancing energy-materials through high-throughput experiments and computation
![](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/Ajax-GA-Loader.gif)
Advancing energy materials through high throughput experiments and computation
Moran Balaish, Helge S. Stein, Arghya Bhowmik, and John M. Gregoire introduce the Journal of Materials Chemistry A themed collection on advancing energy materials through high throughput experiments and computation.
J. Mater. Chem. A, 2024, Advance Article
https://doi.org/10.1039/D4TA90112F
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
High throughput identification of complex rutile alloys for the acidic oxygen evolution reaction
Non-precious metal catalysts for acidic OER typically require a high concentration of activity-promoting elements, e.g., Mn. We describe the high throughput discovery of quinary oxide catalysts with low Mn concentration via mixing with Sb, Sn, and Ti.
J. Mater. Chem. A, 2023,11, 25262-25267
https://doi.org/10.1039/D3TA04899C
Microstructural and electron framework-engineered 3D NiSeP-integrated CuFe composites as trifunctional electrocatalysts for sensing and urea-assisted water-splitting applications
Catalytically dynamic NSP-CF@NCW electrode engineered by strategic integration of 3D Se and P-fused NSP microflakes with CF cubes docked NCW was studied as a trifunctional electrocatalyst for urea sensing and urea-assisted water splitting.
J. Mater. Chem. A, 2024,12, 19935-19949
https://doi.org/10.1039/D4TA01919A
Non-aqueous battery electrolytes: high-throughput experimentation and machine learning-aided optimization of ionic conductivity
We study the ionic conductivity of a bi-salt (LiPF6, LiFSI) and ternary solvent (EC, EMC, PC) liquid battery electrolyte with high throughput experimentation and the open source Liquid Electrolyte Composition Analysis (LECA) Machine-Learning library.
J. Mater. Chem. A, 2024,12, 19123-19136
https://doi.org/10.1039/D3TA06249J
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Generalization performance of machine learning models: (upper panel) generalization from small ordered to large disordered structures (SQS); (lower panel) generalization from low-order to high-order systems.
J. Mater. Chem. A, 2024,12, 12412-12422
https://doi.org/10.1039/D4TA00982G
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics
Machine-learning molecular dynamics provides predictions of structural and anharmonic vibrational properties of solid-state ionic conductors with ab initio accuracy. This opens a path towards rapid design of novel battery materials.
J. Mater. Chem. A, 2024,12, 11344-11361
https://doi.org/10.1039/D4TA00452C
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
A bridge between trust and control: computational workflows meet automated battery cycling
We demonstrate a link between workflow management and instrument automation tools, effectively bridging “trust” from tracking data provenance with automated “control” of experiments. We illustrate our approach using a battery cycling case study.
J. Mater. Chem. A, 2024,12, 10773-10783
https://doi.org/10.1039/D3TA06889G
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
High-throughput screening and characterization of novel zeolitic imidazolate framework gels
High-throughput screening and subsequent batch synthesis identified and characterized novel zeolitic imidazolate frameworks gels.
J. Mater. Chem. A, 2024,12, 9102-9112
https://doi.org/10.1039/D3TA06719J
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage
Prediction of crystal structures with desirable material properties is a grand challenge in materials research. We deployed graph theory assisted structure searcher and combined with universal machine learning potentials to accelerate the process.
J. Mater. Chem. A, 2024,12, 8502-8515
https://doi.org/10.1039/D3TA06190F
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
Advancing high-throughput combinatorial aging studies of hybrid perovskite thin films via precise automated characterization methods and machine learning assisted analysis
A comprehensive inert-gas workflow for combinatorial aging studies gives insight into the intrinsic stability of hybrid perovskites under relevant aging conditions.
J. Mater. Chem. A, 2024,12, 7025-7035
https://doi.org/10.1039/D3TA07274F
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
Rapid screening of molecular beam epitaxy conditions for monoclinic (InxGa1−x)2O3 alloys
High-throughput MBE with cyclical growth and in situ etch increases experimental throughput by approximately 6× and substrate utilization by >40×.
J. Mater. Chem. A, 2024,12, 5508-5519
https://doi.org/10.1039/D3TA07220G
Stability prediction of gold nanoclusters with different ligands and doped metals: deep learning and experimental tests
The formation energy of gold nanoclusters could be predicted quickly by deep learning.
J. Mater. Chem. A, 2024,12, 4460-4472
https://doi.org/10.1039/D3TA06892G
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory
Introduction of an SQL and Python-based tool for managing research data from acquisition to publication. The method enables FAIR-compatible data management, minimizes user interaction, and provides customizability for diverse research domains.
J. Mater. Chem. A, 2024,12, 3933-3942
https://doi.org/10.1039/D3TA06247C
Electrochemically and chemically stable electrolyte–electrode interfaces for lithium iron phosphate all-solid-state batteries with sulfide electrolytes
This study identifies suitable coating materials that can prevent the electrode–electrolyte interfacial reaction to remove the obstruction in all-solid-state batteries composed of LiFePO4 and sulfide solid electrolytes.
J. Mater. Chem. A, 2024,12, 3954-3966
https://doi.org/10.1039/D3TA06227A
Experimental discovery of novel ammonia synthesis catalysts via active learning
Active learning based on literature and experimental data enabled the discovery of highly active novel catalysts for ammonia synthesis. Pathway analysis implies that these activities have been achieved by both structural and electronic promotion.
J. Mater. Chem. A, 2024,12, 3046-3060
https://doi.org/10.1039/D3TA05939A
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts
This study highlighted the effectiveness of AI-driven multiobjective Bayesian optimization for electrocatalysis, accelerating the search for active and stable compositions for the acidic oxygen evolution reaction by 17x.
J. Mater. Chem. A, 2024,12, 3072-3083
https://doi.org/10.1039/D3TA06651G
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.150/content/NewImages/open-access-icon-orange.png)
An active learning approach to model solid-electrolyte interphase formation in Li-ion batteries
Li-ion batteries store electrical energy by electrochemically reducing Li ions from a liquid electrolyte in a graphitic electrode.
J. Mater. Chem. A, 2024,12, 2249-2266
https://doi.org/10.1039/D3TA06054C
Developing an FexCoyLaz-based amorphous aerogel catalyst for the oxygen evolution reaction via high throughput synthesis
High-throughput synthesis was used to fabricate ternary FexCoyLaz-based aerogel electrocatalysts for stoichiometric assessment. This work suggests a feasible way to find water-splitting non-precious metal electrocatalysts.
J. Mater. Chem. A, 2024,12, 1793-1803
https://doi.org/10.1039/D3TA06211B
Materials funnel 2.0 – data-driven hierarchical search for exploration of vast chemical spaces
We propose a novel HTCS accelerated inverse design in a very large materials space combining the benefits of generative modeling, computationally efficient machine learning surrogate and high-quality physics-based simulation.
J. Mater. Chem. A, 2023,11, 26551-26561
https://doi.org/10.1039/D3TA05860C
Reinforcement learning-based design of shape-changing metamaterials
We have implemented a new reinforcement learning method able to rationally design unique metamaterial structures, which change shape during operational conditions. We have applied this to design nanostructured silicon anodes for Li-ion batteries.
J. Mater. Chem. A, 2023,11, 21036-21045
https://doi.org/10.1039/D3TA03119E
About this collection
The unprecedented need for new and improved energy conversion and storage materials creates a historic imperative to accelerate the research process and proliferate new and improved materials (and interfaces) from guided and serendipitous discovery to commercial application by 5x - 20x. Integrating high-throughput automated ceramic synthesis, data management, data mining, autonomous materials characterization, and robust data analysis with guidance and uncertainty quantification from artificial intelligence (AI) and machine-learning (ML) can revolutionize how research is conducted. This accelerated way of orchestrating chemistry sparks new avenues in interdisciplinary research across chemistry, physics, material science, computer science, engineering and stimulates breakthroughs in energy materials.
This themed collection of Journal of Materials Chemistry A, Guest Edited by Dr. Moran Balaish (Technical University of Munich, Germany), Prof. Helge Sören Stein (Karlsruhe Institute of Technology, Germany), Prof. Arghya Bhowmik (Technical University of Denmark, Denmark) and Prof. John Gregoire (Caltech, USA), aims to provide a platform for recent developments in the emerging research area of material science and technology accelerated by artificial intelligence, autonomous, and automated methods for discovering, characterizing, understanding and upscaling energy materials and related applications.
We hope you enjoy exploring the articles in this collection!