Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins DOI
Zhongwei Chen,

Boran Yang,

Nannan Song

et al.

Chemical Engineering Journal, Journal Year: 2022, Volume and Issue: 455, P. 140547 - 140547

Published: Nov. 24, 2022

Language: Английский

Beyond Solvothermal: Alternative Synthetic Methods for Covalent Organic Frameworks DOI Creative Commons
Ji‐Yun Hu, Zhiyuan Huang, Yi Liu

et al.

Angewandte Chemie International Edition, Journal Year: 2023, Volume and Issue: 62(37)

Published: June 2, 2023

Abstract Covalent organic frameworks (COFs) are crystalline porous materials that hold a wealth of potential applications across various fields. The development COFs, however, is significantly impeded by the dearth efficient synthetic methods. traditional solvothermal approach, while prevalent, fraught with challenges such as complicated processes, excessive energy consumption, long reaction times, and limited scalability, rendering it unsuitable for practical applications. quest simpler, quicker, more energy‐efficient, environmentally benign strategies thus paramount bridging gap between academic COF chemistry industrial application. This Review provides an overview recent advances in alternative methods, particular emphasis on input. We discuss representative examples synthesis facilitated microwave, ultrasound, mechanic force, light, plasma, electric field, electron beam. Perspectives advantages limitations these methods against approach highlighted.

Language: Английский

Citations

45
Self-Driving Laboratories for Chemistry and Materials Science DOI Creative Commons
Gary Tom, Stefan P. Schmid, Sterling G. Baird

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Language: Английский

Citations

39
Transitioning metal–organic frameworks from the laboratory to market through applied research DOI
Ashley M. Wright,

Matthew T. Kapelewski,

Stefan Marx

et al.

Nature Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 8, 2024

Language: Английский

Citations

36
Embracing data science in catalysis research DOI
Manu Suvarna, Javier Pérez‐Ramírez

Nature Catalysis, Journal Year: 2024, Volume and Issue: 7(6), P. 624 - 635

Published: April 23, 2024

Language: Английский

Citations

27
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture DOI Creative Commons
Anuroop Sriram, Sihoon Choi, Xiaohan Yu

et al.

ACS Central Science, Journal Year: 2024, Volume and Issue: 10(5), P. 923 - 941

Published: May 1, 2024

Direct air capture (DAC) of CO

Language: Английский

Citations

23
Machine learning applications in nanomaterials: Recent advances and future perspectives DOI
Liang Yang, Hong Wang,

Deying Leng

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 156687 - 156687

Published: Oct. 1, 2024

Language: Английский

Citations

22
Image and data mining in reticular chemistry powered by GPT-4V DOI Creative Commons
Zhiling Zheng,

Zhiguo He,

Omar Khattab

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(3), P. 491 - 501

Published: Jan. 1, 2024

The integration of artificial intelligence into scientific research opens new avenues with the advent GPT-4V, a large language model equipped vision capabilities.

Language: Английский

Citations

18
Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery DOI
Chunhua Li,

Luqian Bao,

Yixin Ji

et al.

Coordination Chemistry Reviews, Journal Year: 2024, Volume and Issue: 514, P. 215888 - 215888

Published: May 8, 2024

Language: Английский

Citations

18
Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks DOI

Ruolin Du,

R. C. Xin,

Han Wang

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 490, P. 151828 - 151828

Published: May 1, 2024

Language: Английский

Citations

17
Large language models for reticular chemistry DOI
Zhiling Zheng, Nakul Rampal,

Theo Jaffrelot Inizan

et al.

Nature Reviews Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

Citations

4