International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 92, P. 1335 - 1355
Published: Nov. 1, 2024
Language: Английский
ACS Nano, Journal Year: 2023, Volume and Issue: 17(11), P. 9763 - 9792
Published: June 2, 2023
Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth data, adoption exploitation of artificial intelligence (AI) as part materials research framework had tremendous impact on development nanomaterials. AI has enabled revolutionary next-generation paradigms significantly accelerate all stages material discovery facilitate exploration enormous design space. In this review, we summarize recent advancements applications discovery, with special emphasis selected nanotechnology net-zero future including solar cells, hydrogen energy, battery renewable CO2 capture conversion capture, utilization storage (CCUS) addition, discuss limitations challenges current area by identifying gaps that exist development. Finally, present prospect directions order large-scale
Language: Английский
Citations
48
Advanced Science, Journal Year: 2024, Volume and Issue: 11(13)
Published: Jan. 26, 2024
Abstract The availability of an ever‐expanding portfolio 2D materials with rich internal degrees freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together the unique ability to tailor heterostructures made by in a precisely chosen stacking sequence relative crystallographic alignments, offers unprecedented platform for realizing design. However, breadth multi‐dimensional parameter space massive data sets involved is emblematic complex, resource‐intensive experimentation, which not only challenges current state art but also renders exhaustive sampling untenable. To this end, machine learning, very powerful data‐driven approach subset artificial intelligence, potential game‐changer, enabling cheaper – yet more efficient alternative traditional computational strategies. It new paradigm autonomous experimentation accelerated discovery machine‐assisted design functional heterostructures. Here, study reviews recent progress such endeavors, highlight various emerging opportunities frontier research area.
Language: Английский
Citations
47
Nano Energy, Journal Year: 2023, Volume and Issue: 118, P. 108965 - 108965
Published: Oct. 4, 2023
Language: Английский
Citations
46
ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(3), P. 1336 - 1350
Published: Jan. 11, 2024
Intrinsic direct-gap two-dimensional (2D) materials hold great promise as photocatalysts, advancing the application of photocatalytic water splitting for hydrogen production. However, time- and resource-efficient exploration identification such 2D from a vast compositional structural chemical space present significant challenges within realm science research. To this end, we perform data-driven study to find with intrinsic desirable properties overall splitting. By implementing three-staged large-scale screening, which incorporates machine-learned data V2DB, high-throughput density functional theory (DFT), hybrid-DFT calculations, identify 16 promising photocatalysts. Subsequently, conduct comprehensive assessment that are related solar performance, include electronic optical properties, solar-to-hydrogen conversion efficiencies, carrier mobilities. Therefore, not only presents photocatalysts but also introduces rigorous approach future discovery currently unexplored spaces.
Language: Английский
Citations
21
Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(15)
Published: Feb. 13, 2024
Abstract Hydrogen is one of the most important energy alternatives to conventional fossil‐based fuel. Solar based photocatalytic hydrogen evolution (PHE) a salient approach produce fuel but its efficiency generally limited by sluggish and energy‐unfavorable oxidation reaction. Meanwhile, waste treatment has become worldwide problem clean highly demanded avoid vast greenhouse emission currently. Inspiringly, PHE can be effectively coupled with favorable photooxidation many wastes, which kills two birds stone. In this review, recent progress in presented, where typical solid, liquid, gas wastes have been briefly discussed. Focusing on understanding complicated reaction mechanism revelation products, cutting‐edge techniques for photophysics surface chemistry characterization analyzed, are imperative facilitate following investigation. Finally, developing trend existing issues current research also discussed detail so that holistic blueprint portrayed accelerate their application realistic world.
Language: Английский
Citations
17
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
Citations
17
Chemical Communications, Journal Year: 2023, Volume and Issue: 59(39), P. 5795 - 5806
Published: Jan. 1, 2023
By integrating machine learning with automation and robots, accelerated discovery of photocatalysts in the future could be envisioned.
Language: Английский
Citations
37
Materials Today Sustainability, Journal Year: 2022, Volume and Issue: 21, P. 100276 - 100276
Published: Nov. 25, 2022
Language: Английский
Citations
29
ACS Nano, Journal Year: 2024, Volume and Issue: 18(29), P. 19381 - 19390
Published: July 12, 2024
The band gap constraint of the photocatalyst for overall water splitting limits utilization solar energy. A strategy to broaden range light absorption is employing a two-dimensional (2D) polar material as photocatalyst, benefiting from deflection energy level due their intrinsic internal electric field. Here, by using first-principles computational screening, we search 2D semiconductors photocatalytic both ground- and excited-state perspectives. Applying unique electronic structure model materials, there are 13 candidates hydrogen evolution reaction (HER) 8 oxygen (OER) without barrier energies perspective ground-state free variation calculation. In particular, Cu
Language: Английский
Citations
6
Nano Energy, Journal Year: 2025, Volume and Issue: unknown, P. 111010 - 111010
Published: April 1, 2025
Language: Английский
Citations
0