Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Language: Английский

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Navigating the Evolution of Carbon Nitride Research: Integrating Machine Learning into Conventional Approaches

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Carbon nitride research has reached a promising point in today's endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic structural properties. Recent advances machine learning (ML) have opened new avenues for exploring optimizing the potential of these materials. This study presents comprehensive review integration ML techniques carbon an introduction CN classifications recent advancements. We discuss methodologies employed, such as supervised learning, unsupervised reinforcement predicting material properties, synthesis conditions, enhancing performance metrics. Key findings indicate that algorithms can significantly reduce experimental trial-and-error, accelerate discovery processes, provide deeper insights into structure-property relationships nitride. The synergistic effect combining traditional approaches is highlighted, showcasing studies where driven models successfully predicted novel compositions enhanced functional Future directions this field are also proposed, emphasizing need high-quality datasets, advanced models, interdisciplinary collaborations fully realize materials next-generation technologies.

Language: Английский

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AlloyGPT: End-to-end prediction and design of additively manufacturable alloys using an autoregressive language model

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Rapid progress in additive manufacturing of alloys opens opportunities controlling compositions and microstructures at voxel-size resolution complex geometries, thus unlocking unprecedented design performance various critical engineering applications. However, to fully exploit such potential, capable yet efficient models for navigating the vast spaces alloy compositions, structures properties are great research interest. Here, we present AlloyGPT, an autoregressive alloy-specific language model, that learns composition-structure-property relationship generates novel designs additively manufacturable alloys. Specifically, develop grammar convert physics-rich datasets into readable text records both forward prediction inverse tasks. Then, construct a customized tokenizer generative pre-trained transformer (GPT) model master this through training. At deployment, our can accurately predict multiple phase based on given achieving R2 values ranging from 0.86 0.99 test set. When tested beyond learned composition domain, only degrades gradually stable manner. Given desired structures, same suggest meet goals. And balance between diversity accuracy be further tuned stably. Our AlloyGPT presents way integrating comprehensive knowledge terms simultaneously solve tasks with accuracy, robustness. This fundamental will open new avenues accelerate integration material pure or gradient structural manufactured by traditional manufacturing.

Language: Английский

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Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics

Acta Biomaterialia, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

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Machine learning in electrocatalysis - living up to the hype?

Current Opinion in Electrochemistry, Journal Year: 2025, Volume and Issue: unknown, P. 101649 - 101649

Published: Jan. 1, 2025

Language: Английский

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Lean CNNs for Mapping Electron Charge Density Fields to Material Properties

Integrating materials and manufacturing innovation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Language: Английский

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Green Materials for Water and Wastewater Treatment: Mechanisms and Artificial Intelligence

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 566 - 566

Published: Feb. 17, 2025

Green materials are emerging as sustainable alternatives in water and wastewater treatment. Due to their biodegradability, renewable origin low toxicity characteristics, green an alternative conventional synthetic materials. include nanomaterials of natural origin, biopolymers composites that optimize the adsorption removal contaminants. The applications cellulose nanofibers, alginates, chitosan lignin stand out, well functionalized hydrogels aerogels for heavy metals, dyes organic analysis mechanisms processes contaminant modeling optimization techniques included key tools design these materials, allowing one predict properties, simulate interactions customize solutions. Despite sustainability benefits they face technical economic challenges, such scalability, synthesis costs experimental validation. This work concluded combined with tools, essential move towards more sustainable, efficient environmentally friendly treatment technologies, aligned global objectives development climate change mitigation.

Language: Английский

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A Perspective on Foundation Models in Chemistry

JACS Au, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Foundation models are an emerging paradigm in artificial intelligence (AI), with successful examples like ChatGPT transforming daily workflows. Generally, foundation large-scale, pretrained capable of adapting to various downstream tasks by leveraging extensive data and model scaling. Their success has inspired researchers develop for a wide range chemical challenges, from materials discovery understanding structure-property relationships, areas where conventional machine learning (ML) often face limitations. In addition, hold promise addressing persistent ML challenges chemistry, such as scarcity poor generalization. this perspective, we review recent progress the development chemistry across applications varying scope. We also discuss trends provide outlook on promising approaches advancing chemistry.

Language: Английский

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A Generative Artificial Intelligence Model for Efficient Gas Sensitivity Prediction in Materials without Parameters from First Principle Calculation

Sensors and Actuators A Physical, Journal Year: 2025, Volume and Issue: unknown, P. 116636 - 116636

Published: April 1, 2025

Language: Английский

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Going Digital to Boost Safe and Sustainable Materials Innovation Markets. The Digital Safe-and-Sustainability-by-Design Innovation Approach of the PINK Project

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

In this innovation report, we present the vision of PINK project to foster Safe-and-Sustainable-by-Design (SSbD) advanced materials and chemicals (AdMas&Chems) development by integrating state-of-the-art computational modelling, simulation tools data resources. proposes a novel approach for use SSbD Framework, whose innovative is based on application multi-objective optimisation procedure criteria functionality, safety, sustainability cost efficiency. At core open platform, distributed system that integrates all relevant modelling resources enriched with visualisation an AI-driven decision support system. Data from the, in large parts, independently developed areas functional design, safety assessment, life cycle assessment & costing are brought together newly created Interoperability Framework. The Silico Hub, as user Interface finally guides through complete AdMas&Chems process idea creation market introduction. Guided two Developmental Case Studies, building Platform iterative, ensuring industry readiness implement apply it. Additionally, Industrial Demonstrator programme will be introduced part final phase, which allows partners especially small medium enterprises (SMEs) become consortium. Feedback Demonstrators well other stakeholder-engagement activities collaborations shape platform's look feel and, even more important, assure long-term technical sustainability.

Language: Английский

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