How machine learning can accelerate electrocatalysis discovery and optimization DOI Creative Commons
Stephan N. Steinmann, Qing Wang, Zhi Wei Seh

et al.

Materials Horizons, Journal Year: 2022, Volume and Issue: 10(2), P. 393 - 406

Published: Dec. 9, 2022

Advances in machine learning (ML) provide the means to bypass bottlenecks discovery of new electrocatalysts using traditional approaches. In this review, we highlight currently achieved work ML-accelerated and optimization via a tight collaboration between computational models experiments. First, applicability available methods for constructing machine-learned potentials (MLPs), which accurate energies forces atomistic simulations, are discussed. Meanwhile, current challenges MLPs context electrocatalysis highlighted. Then, review recent progress predicting catalytic activities surrogate models, including microkinetic simulations more global proxies thereof. Several typical applications ML rationalize thermodynamic predict adsorption activation also Next, developments ML-assisted experiments catalyst characterization, synthesis reaction condition illustrated. particular, ML-enhanced spectra analysis use interpret experimental kinetic data Additionally, show how robotics applied high-throughput synthesis, characterization testing accelerate materials exploration process equipment can be assembled into self-driven laboratories.

Language: Английский

A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage DOI Creative Commons
Muhammad Usman Hadi,

qasem al tashi,

Rizwan Qureshi

et al.

Published: July 10, 2023

<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides comprehensive overview LLMs, their history, architecture, training methods, applications, challenges. The begins by discussing fundamental concepts generative AI architecture pre- trained transformers (GPT). It then an history evolution over time, different methods been used train them. discusses applications medical, education, finance, engineering. also how LLMs shaping future they can be solve real-world problems. challenges associated with deploying scenarios, ethical considerations, model biases, interpretability, computational resource requirements. highlights techniques enhancing robustness controllability addressing bias, fairness, generation quality issues. Finally, concludes highlighting LLM research need addressed order make more reliable useful. is intended provide researchers, practitioners, enthusiasts understanding evolution, By consolidating state-of-the-art knowledge field, this serves valuable further advancements development utilization applications. GitHub repo project available at https://github.com/anas-zafar/LLM-Survey</p>

Language: Английский

Citations

187
Ni-rich layered cathodes for lithium-ion batteries: From challenges to the future DOI
Jun Yang, Xinghui Liang, Hoon‐Hee Ryu

et al.

Energy storage materials, Journal Year: 2023, Volume and Issue: 63, P. 102969 - 102969

Published: Sept. 14, 2023

Language: Английский

Citations

106
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

74
Anion optimization for bifunctional surface passivation in perovskite solar cells DOI
Jian Xu, Hao Chen, Luke Grater

et al.

Nature Materials, Journal Year: 2023, Volume and Issue: 22(12), P. 1507 - 1514

Published: Oct. 30, 2023

Language: Английский

Citations

74
Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects DOI Creative Commons
Muhammad Usman Hadi,

qasem al tashi,

Rizwan Qureshi

et al.

Published: Nov. 16, 2023

<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides comprehensive overview LLMs, their history, architecture, training methods, applications, challenges. The begins by discussing fundamental concepts generative AI architecture pre- trained transformers (GPT). It then an history evolution over time, different methods been used train them. discusses applications medical, education, finance, engineering. also how LLMs shaping future they can be solve real-world problems. challenges associated with deploying scenarios, ethical considerations, model biases, interpretability, computational resource requirements. highlights techniques enhancing robustness controllability addressing bias, fairness, generation quality issues. Finally, concludes highlighting LLM research need addressed order make more reliable useful. is intended provide researchers, practitioners, enthusiasts understanding evolution, By consolidating state-of-the-art knowledge field, this serves valuable further advancements development utilization applications. GitHub repo project available at https://github.com/anas-zafar/LLM-Survey</p>

Language: Английский

Citations

64
Computational chemistry for water-splitting electrocatalysis DOI
Licheng Miao, Wenqi Jia, Xuejie Cao

et al.

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(6), P. 2771 - 2807

Published: Jan. 1, 2024

This review presents the basics of electrochemical water electrolysis, discusses progress in computational methods, models, and descriptors, evaluates remaining challenges this field.

Language: Английский

Citations

51
Modeling Single‐Atom Catalysis DOI Creative Commons
Giovanni Di Liberto, Gianfranco Pacchioni

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(46)

Published: Sept. 25, 2023

Electronic structure calculations represent an essential complement of experiments to characterize single-atom catalysts (SACs), consisting isolated metal atoms stabilized on a support, but also predict new catalysts. However, simulating SACs with quantum chemistry approaches is not as simple often assumed. In this work, the factors that reliable simulation activity are examined. The Perspective focuses importance precise atomistic characterization active site, since even small changes in atom's surroundings can result large reactivity. dynamical behavior and stability under working conditions, well adopting appropriate methods solve Schrödinger equation for quantitative evaluation reaction energies addressed. relevance model adopted. For electrocatalysis must include effects solvent, presence electrolytes, pH, external potential. Finally, it discussed how similarities between coordination compounds may intermediates usually observed electrodes. When these aspects adequately considered, predictive power electronic quite limited.

Language: Английский

Citations

49
Advancements in nanomaterials for nanosensors: a comprehensive review DOI Creative Commons
Moustafa A. Darwish, Walaa Abd‐Elaziem, Ammar H. Elsheikh

et al.

Nanoscale Advances, Journal Year: 2024, Volume and Issue: 6(16), P. 4015 - 4046

Published: Jan. 1, 2024

Nanomaterials (NMs) exhibit unique properties that render them highly suitable for developing sensitive and selective nanosensors across various domains.

Language: Английский

Citations

49
Accelerated chemical science with AI DOI Creative Commons
Seoin Back,

Alán Aspuru-Guzik,

Michele Ceriotti

et al.

Digital Discovery, Journal Year: 2023, Volume and Issue: 3(1), P. 23 - 33

Published: Dec. 6, 2023

The ASLLA Symposium focused on accelerating chemical science with AI. Discussions data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, academic bodies provided.

Language: Английский

Citations

45
Advancing next-generation proton-exchange membrane fuel cell development in multi-physics transfer DOI Creative Commons
Guobin Zhang, Zhiguo Qu, Wen‐Quan Tao

et al.

Joule, Journal Year: 2023, Volume and Issue: 8(1), P. 45 - 63

Published: Dec. 15, 2023

Language: Английский

Citations

44