Themed collection Advancing energy-materials through high-throughput experiments and computation

20 items
Editorial

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.

Graphical abstract: Advancing energy materials through high throughput experiments and computation
Open Access Communication

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.

Graphical abstract: High throughput identification of complex rutile alloys for the acidic oxygen evolution reaction
Paper

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.

Graphical abstract: Microstructural and electron framework-engineered 3D NiSeP-integrated CuFe composites as trifunctional electrocatalysts for sensing and urea-assisted water-splitting applications
From the themed collection: Journal of Materials Chemistry A HOT Papers
Paper

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.

Graphical abstract: Non-aqueous battery electrolytes: high-throughput experimentation and machine learning-aided optimization of ionic conductivity
Paper

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.

Graphical abstract: Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
From the themed collection: Journal of Materials Chemistry A HOT Papers
Open Access Paper

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.

Graphical abstract: Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics
Open Access Paper

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.

Graphical abstract: A bridge between trust and control: computational workflows meet automated battery cycling
From the themed collection: Journal of Materials Chemistry A HOT Papers
Open Access Paper

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.

Graphical abstract: High-throughput screening and characterization of novel zeolitic imidazolate framework gels
Open Access Paper

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.

Graphical abstract: Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage
Open Access Paper

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.

Graphical abstract: Advancing high-throughput combinatorial aging studies of hybrid perovskite thin films via precise automated characterization methods and machine learning assisted analysis
Open Access Paper

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×.

Graphical abstract: Rapid screening of molecular beam epitaxy conditions for monoclinic (InxGa1−x)2O3 alloys
Paper

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.

Graphical abstract: Stability prediction of gold nanoclusters with different ligands and doped metals: deep learning and experimental tests
From the themed collection: Journal of Materials Chemistry A HOT Papers
Open Access Paper

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.

Graphical abstract: Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory
From the themed collection: Journal of Materials Chemistry A HOT Papers
Paper

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.

Graphical abstract: Electrochemically and chemically stable electrolyte–electrode interfaces for lithium iron phosphate all-solid-state batteries with sulfide electrolytes
From the themed collection: Journal of Materials Chemistry A HOT Papers
Paper

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.

Graphical abstract: Experimental discovery of novel ammonia synthesis catalysts via active learning
Open Access Paper

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.

Graphical abstract: Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts
Open Access Paper

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.

Graphical abstract: An active learning approach to model solid-electrolyte interphase formation in Li-ion batteries
Paper

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.

Graphical abstract: Developing an FexCoyLaz-based amorphous aerogel catalyst for the oxygen evolution reaction via high throughput synthesis
Paper

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.

Graphical abstract: Materials funnel 2.0 – data-driven hierarchical search for exploration of vast chemical spaces
Paper

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.

Graphical abstract: Reinforcement learning-based design of shape-changing metamaterials
20 items

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!


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