Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes DOI Creative Commons
Sheng Bi,

Lisanne Knijff,

Xiliang Lian

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(31), P. 19931 - 19949

Published: July 25, 2024

Capacitive storage devices allow for fast charge and discharge cycles, making them the perfect complements to batteries high power applications. Many materials display interesting capacitive properties when they are put in contact with ionic solutions despite their very different structures (surface) reactivity. Among them, nanocarbons most important practical applications, but many nanomaterials have recently emerged, such as conductive metal-organic frameworks, 2D materials, a wide variety of metal oxides. These heterogeneous complex electrode difficult model conventional approaches. However, development computational methods, incorporation machine learning techniques, increasing performance computing now us tackle these types systems. In this Review, we summarize current efforts direction. We show that depending on nature charging mechanisms, or combinations can provide desirable atomic-scale insight interactions at play. mainly focus two aspects: (i) study ion adsorption nanoporous which require extension constant potential molecular dynamics multicomponent systems, (ii) characterization Faradaic processes pseudocapacitors, involves use electronic structure-based methods. also discuss how developed simulation methods will bridges be made between double-layer capacitors pseudocapacitors future electricity devices.

Language: Английский

SELFIES and the future of molecular string representations DOI Creative Commons
Mario Krenn, Qianxiang Ai, Senja Barthel

et al.

Patterns, Journal Year: 2022, Volume and Issue: 3(10), P. 100588 - 100588

Published: Oct. 1, 2022

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks chemistry materials science. Examples include the prediction of properties, discovery new reaction pathways, or design molecules. The needs read write fluently a chemical language each these tasks. Strings common tool represent molecular graphs, most popular string representation, Smiles, has powered cheminformatics since late 1980s. However, context AI ML chemistry, Smiles several shortcomings—most pertinently, combinations symbols lead invalid results with no valid interpretation. To overcome this issue, molecules was introduced 2020 that guarantees 100% robustness: SELF-referencing embedded (Selfies). Selfies simplified enabled numerous chemistry. In perspective, we look future discuss representations, along their respective opportunities challenges. We propose 16 concrete projects robust representations. These involve extension toward domains, exciting questions at interface languages, interpretability both humans machines. hope proposals will inspire follow-up works exploiting full potential representations

Language: Английский

Citations

156
The 2022 solar fuels roadmap DOI Creative Commons
Gideon Segev, Jakob Kibsgaard, Christopher Hahn

et al.

Journal of Physics D Applied Physics, Journal Year: 2022, Volume and Issue: 55(32), P. 323003 - 323003

Published: May 13, 2022

Abstract Renewable fuel generation is essential for a low carbon footprint economy. Thus, over the last five decades, significant effort has been dedicated towards increasing performance of solar fuels generating devices. Specifically, to hydrogen efficiency photoelectrochemical cells progressed steadily its fundamental limit, and faradaic valuable products in CO 2 reduction systems increased dramatically. However, there are still numerous scientific engineering challenges that must be overcame order turn into viable technology. At electrode device level, conversion efficiency, stability selectivity significantly. Meanwhile, these metrics maintained when scaling up devices while maintaining an acceptable cost footprint. This roadmap surveys different aspects this endeavor: system benchmarking, scaling, various approaches photoelectrodes design, materials discovery, catalysis. Each sections focuses on single topic, discussing state art, key advancements required meet them. The can used as guide researchers funding agencies highlighting most pressing needs field.

Language: Английский

Citations

95
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery DOI Creative Commons
Zhengkai Tu, Thijs Stuyver,

Connor W. Coley

et al.

Chemical Science, Journal Year: 2022, Volume and Issue: 14(2), P. 226 - 244

Published: Nov. 28, 2022

This review outlines several organic chemistry tasks for which predictive machine learning models have been and can be applied.

Language: Английский

Citations

79
Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction DOI Creative Commons
Jin Li, Naiteng Wu, Jian Zhang

et al.

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

Citations

75
Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands DOI

Jordan J. Dotson,

Lucy van Dijk, Jacob C. Timmerman

et al.

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 145(1), P. 110 - 121

Published: Dec. 27, 2022

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe machine learning workflow for multi-objective optimization catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through two sequential required in asymmetric synthesis an active pharmaceutical ingredient. To accomplish this, density functional theory-derived database >550 ligands constructed, designer chemical space mapping technique established. The protocol used classification methods identify catalysts, followed by linear regression model selectivity. led prediction validation significantly improved all outputs, suggesting general strategy can be readily implemented optimizations where performance is controlled

Language: Английский

Citations

70
Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning DOI Creative Commons
Wentao Zhang, Ronghua Chen, Jie Li

et al.

Biochar, Journal Year: 2023, Volume and Issue: 5(1)

Published: April 23, 2023

Abstract Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of related synthesis parameters. But the influence factor numerous, traditional experimental enumeration powerless. In recent years, machine learning has been gradually employed biochar, but there no comprehensive review on whole process regulation adsorbents, covering optimization modeling. This article systematically summarized application in adsorbents from perspective all-round first time, including modeling adsorbents. Firstly, overview was introduced. Then, latest advances removal were summarized, prediction yield physicochemical properties, optimal synthetic conditions economic cost. And by reviewed, efficiency, revelation mechanism. General guidelines whole-process presented. Finally, existing problems future perspectives put forward. We hope that this can promote integration thus light up industrialization biochar. Graphical

Language: Английский

Citations

51
Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization DOI

Liqianyun Xu,

Feng Wu, Renjie Chen

et al.

Energy storage materials, Journal Year: 2023, Volume and Issue: 59, P. 102785 - 102785

Published: April 23, 2023

Language: Английский

Citations

43
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design DOI
Jorge Benavides-Hernández, Franck Dumeignil

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(15), P. 11749 - 11779

Published: July 24, 2024

This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field heterogeneous catalysis, presenting a broad spectrum contemporary methodologies innovations. We methodically segmented text three core areas: catalyst characterization, data-driven exploitation, discovery. In characterization part, we outline current prospective techniques used for HTE how AI-driven strategies can streamline or automate their analysis. The exploitation part is divided themes, strategies, that offer flexibility either modular application creation customized solutions. exploration present applications enable areas outside experimentally tested chemical space, incorporating section on computational methods identifying new prospects. concludes by addressing limitations within suggesting possible avenues future research.

Language: Английский

Citations

24
Machine learning for CO2 capture and conversion: A review DOI Creative Commons
Sung Eun Jerng, Yang Jeong Park, Ju Li

et al.

Energy and AI, Journal Year: 2024, Volume and Issue: 16, P. 100361 - 100361

Published: March 30, 2024

Coupled electrochemical systems for the direct capture and conversion of CO2 have garnered significant attention owing to their potential enhance energy- cost-efficiency by circumventing amine regeneration step. However, optimizing coupled system is more challenging than handling separated because its complexity, caused incorporation solvent heterogeneous catalysts. Nevertheless, deployment machine learning can be immensely beneficial, reducing both time cost ability simulate describe complex with numerous parameters involved. In this review, we summarized techniques employed in development solvents such as ionic liquids, well To optimize a system, these two separately developed will need combined via future.

Language: Английский

Citations

16
AI in single-atom catalysts: a review of design and applications DOI Open Access

Qijun Yu,

Ninggui Ma,

Chihon Leung

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 12, 2025

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances activity, selectivity, and stability. SACs demonstrate substantial promise electrocatalysis applications, such fuel cells, CO2 reduction, hydrogen production, due to ability maximize utilization of active sites. However, the development efficient stable involves intricate design screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) neural networks (NNs), offers powerful tools for accelerating discovery optimization SACs. This review systematically discusses application AI technologies through four key stages: (1) Density functional theory (DFT) ab initio molecular dynamics (AIMD) simulations: DFT AIMD are used investigate mechanisms, with high-throughput applications expanding accessible datasets; (2) Regression models: ML regression models identify features that influence performance, streamlining selection promising materials; (3) NNs: NNs expedite known structural models, facilitating rapid assessment potential; (4) Generative adversarial (GANs): GANs enable prediction novel high-performance tailored specific requirements. work provides comprehensive overview current status insights recommendations future advancements field.

Language: Английский

Citations

2