Theory-guided experimental design in battery materials research

Science Advances, Journal Year: 2022, Volume and Issue: 8(19)

Published: May 11, 2022

A reliable energy storage ecosystem is imperative for a renewable future, and continued research needed to develop promising rechargeable battery chemistries. To this end, better theoretical experimental understanding of electrochemical mechanisms structure-property relationships will allow us accelerate the development safer batteries with higher densities longer lifetimes. This Review discusses interplay between theory experiment in materials research, enabling not only uncover hitherto unknown but also rationally design more electrode electrolyte materials. We examine specific case studies theory-guided lithium-ion, lithium-metal, sodium-metal, all-solid-state batteries. offer insights into how framework can be extended multivalent close loop, we outline recent efforts coupling machine learning high-throughput computations experiments. Last, recommendations effective collaboration theorists experimentalists are provided.

Language: Английский

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Coestimation of SOC and Three-Dimensional SOT for Lithium-Ion Batteries Based on Distributed Spatial–Temporal Online Correction

IEEE Transactions on Industrial Electronics, Journal Year: 2022, Volume and Issue: 70(6), P. 5937 - 5948

Published: Aug. 24, 2022

Energy storage system based on batteries is a key to achieve green industrial economy and the online estimation of its status critical for battery management system. Therefore, this article proposed distributed spatial–temporal correction algorithm state charge (SOC) three-dimensional (3-D) temperature (SOT) coestimation battery. First, internal resistance identified, SOC estimated adaptive Kalman filter. Then, improve fidelity electrical under dynamic operation condition, coupled with an restoration temperature. An improved fractal growth process used self-organization convergence during 3-D distribution. Finally, validate thermal parameters, current profiles are used. The method raises by 1.5% at most, compared without SOT estimation. It also keeps mean relative error within 8%. Additionally, robustness dual filters validated. result shows that still has good performance disturbance added.

Language: Английский

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The Combination of Structure Prediction and Experiment for the Exploration of Alkali‐Earth Metal‐Contained Chalcopyrite‐Like IR Nonlinear Optical Material

Advanced Science, Journal Year: 2022, Volume and Issue: 9(15)

Published: April 11, 2022

Design and fabrication of new infrared (IR) nonlinear optical (NLO) materials with balanced properties are urgently needed since commercial chalcopyrite-like (CL) NLO crystals suffering from their intrinsic drawbacks. Herein, the first defect-CL (DCL) alkali-earth metal (AEM) selenide IR material, DCL-MgGa2 Se4 , has been rationally designed fabricated by a structure prediction experiment combined strategy. The introduction AEM tetrahedral unit MgSe4 effectively widens band gap DCL compounds. title compound exhibits wide 2.96 eV, resulting in high laser induced damage threshold (LIDT) ≈3.0 × AgGaS2 (AGS). Furthermore, shows suitable second harmonic generation (SHG) response (≈0.9 AGS) type-I phase-matching (PM) behavior transparent range. results indicate that is promising mid-to-far material give some insights into design CL outstanding based on tetrahedra predication

Language: Английский

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Carbonyl Chemistry for Advanced Electrochemical Energy Storage Systems

ACS Nano, Journal Year: 2024, Volume and Issue: 18(31), P. 19950 - 20000

Published: July 29, 2024

On the basis of sustainable concept, organic compounds and carbon materials both mainly composed light C element have been regarded as powerful candidates for advanced electrochemical energy storage (EES) systems, due to theie merits low cost, eco-friendliness, renewability, structural versatility. It is investigated that carbonyl functionality most common constituent part serves a crucial role, which manifests respective different mechanisms in various aspects EES systems. Notably, systematical review about concept progress chemistry beneficial ensuring in-depth comprehending functionality. Hence, comprehensive has summarized based on state-of-the-art developments. Moreover, working principles fundamental properties unit discussed, generalized three aspects, including redox activity, interaction effect, compensation characteristic. Meanwhile, pivotal characterization technologies also illustrated purposefully studying related structure, mechanism, performance profitably understand chemistry. Finally, current challenges promising directions are concluded, aiming afford significant guidance optimal utilization moiety propel practicality

Language: Английский

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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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Computational understanding and multiscale simulation of secondary batteries

Energy storage materials, Journal Year: 2025, Volume and Issue: unknown, P. 104009 - 104009

Published: Jan. 1, 2025

Language: Английский

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Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

SmartMat, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 9, 2025

ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, models. They can target various definitions, distributions, correlations of concerned physical parameters given polymer systems, have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages building quantitative multivariate largely enhanced the capability scientific understanding discoveries, thus facilitating mechanism exploration, prediction, high‐throughput screening, optimization, rational inverse designs. This article summarizes representative progress recent two decades focusing on design, preparation, application, sustainable development materials based exploration key composition–process–structure–property–performance relationship. The integration both data‐driven insights through ML deepen fundamental discover novel is categorically presented. Despite construction application robust models, strategies algorithms deal with variant tasks science still rapid growth. challenges prospects then We believe that innovation will thrive along approaches, from efficient design applications.

Language: Английский

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Challenges and advances of organic electrode materials for sustainable secondary batteries

Exploration, Journal Year: 2022, Volume and Issue: 2(4)

Published: July 27, 2022

Organic electrode materials (OEMs) emerge as one of the most promising candidates for next-generation rechargeable batteries, mainly owing to their advantages bountiful resources, high theoretical capacity, structural designability, and sustainability. However, OEMs usually suffer from poor electronic conductivity unsatisfied stability in common organic electrolytes, ultimately leading deteriorating output capacity inferior rate capability. Making clear issues microscale macroscale level is great importance exploration novel OEMs. Herein, challenges advanced strategies boost electrochemical performance redox-active sustainable secondary batteries are systematically summarized. Particularly, characterization technologies computational methods elucidate complex redox reaction mechanisms confirm radical intermediates have been introduced. Moreover, design OEMs-based full cells outlook further presented. This review will shed light on in-depth understanding development batteries.

Language: Английский

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Structural design of organic battery electrode materials: from DFT to artificial intelligence

Rare Metals, Journal Year: 2023, Volume and Issue: 42(10), P. 3269 - 3303

Published: Aug. 28, 2023

Language: Английский

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Machine learning in energy storage material discovery and performance prediction

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 492, P. 152294 - 152294

Published: May 16, 2024

Language: Английский

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