Capacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials DOI Creative Commons
Souvik Manna, Diptendu Roy, Sandeep Das

et al.

Materials Advances, Journal Year: 2022, Volume and Issue: 3(21), P. 7833 - 7845

Published: Jan. 1, 2022

Machine learning (ML) techniques have been utilized to predict specific capacity for K-ion battery based electrode materials.

Language: Английский

High entropy materials frontier and theoretical insights for logistics CO2 reduction and hydrogenation: Electrocatalysis, photocatalysis and thermo-catalysis DOI
Jasmin S. Shaikh, Meena Rittiruam, Tinnakorn Saelee

et al.

Journal of Alloys and Compounds, Journal Year: 2023, Volume and Issue: 969, P. 172232 - 172232

Published: Sept. 20, 2023

Language: Английский

Citations

30
The role of machine learning in carbon neutrality: Catalyst property prediction, design, and synthesis for carbon dioxide reduction DOI Creative Commons
Zhuo Wang, Zhehao Sun, Hang Yin

et al.

eScience, Journal Year: 2023, Volume and Issue: 3(4), P. 100136 - 100136

Published: April 17, 2023

Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation natural resources that have accompanied development human society. The dioxide reduction reaction (CO2RR) a promising strategy capture convert (CO2) into value-added chemical products. However, traditional trial-and-error method makes it expensive time-consuming understand deeper mechanism behind reaction, discover novel catalysts with superior performance lower cost, determine optimal support structures electrolytes for CO2RR. Emerging machine learning (ML) techniques provide opportunity integrate material science artificial intelligence, which would enable chemists extract implicit knowledge data, be guided insights thereby gained, freed from performing repetitive experiments. In this perspective article, we focus on recent advancements in ML-participated CO2RR applications. After brief introduction ML CO2RR, first ML-accelerated property prediction potential catalysts. Then explore ML-aided catalytic activity selectivity. This followed discussion about ML-guided catalyst electrode design. Next, application ML-assisted experimental synthesis discussed. Finally, present specific challenges opportunities, aim better understanding research using

Language: Английский

Citations

26
Machine Learning Assisted Understanding and Discovery of CO2 Reduction Reaction Electrocatalyst DOI Creative Commons
Erhai Hu, Chuntai Liu, Wei Zhang

et al.

The Journal of Physical Chemistry C, Journal Year: 2023, Volume and Issue: 127(2), P. 882 - 893

Published: Jan. 8, 2023

Electrochemical CO2 reduction reaction (CO2RR) is an important process which a potential way to recycle excessive in the atmosphere. Although electrocatalyst key toward efficient CO2RR, progress of discovering effective catalysts lagging with current methods. Because cost and time efficiency modern machine learning (ML) algorithm, increasing number researchers have applied ML accelerate screening suitable deepen our understanding mechanism. Hence, we reviewed recent applications research CO2RR by types electrocatalyst. An introduction on general methodology discussion pros cons for such are included.

Language: Английский

Citations

24
Future prospects of high-entropy alloys as next-generation industrial electrode materials DOI Creative Commons
Saikat Bolar, Yoshikazu Ito, Takeshi Fujita

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(23), P. 8664 - 8722

Published: Jan. 1, 2024

High-entropy alloys hold significant promise as electrode materials, even from industrial aspect. This potential arises their ability to optimize electronic structures and reaction sites, stemming complex adjustable composition.

Language: Английский

Citations

16
CuNiZn vs CuZn Electrodes: Electrochemical CO2 Reduction, Role of Metal Elements, and Insights for C–C Coupling Chemistry DOI
Yunji Gwon, Seon Young Hwang,

Go Eun Park

et al.

ACS Applied Energy Materials, Journal Year: 2024, Volume and Issue: 7(2), P. 614 - 628

Published: Jan. 3, 2024

Exploring bi- and trimetallic catalysts in electrochemical CO2 reduction (EC CO2R) has been a focal point for discovering products. This study investigates the distinct roles of metal elements CO2R using CuNiZn CuZn electrodes. Bimetallic exhibits superior activity, yielding substantial amounts CO, CH4, C2H4, various liquid products, including formate, ethanol, acetate, propanol, isopropanol. The on suggests potential connections to Fischer–Tropsch (FT) synthesis, indicating their capability produce long-chain hydrocarbons (CnH2n CnH2n+2, n = 2–7) from CO2. EC CO validated FT process over catalysts. discussion explores mechanisms formation C–C coupled C2+ considering potential- concentration-dependent Faradaic efficiencies (FEs). Recycling tests emphasize influence composition FEs. Surface analyses reveal oxidation states compositional changes, while dissolution metals during electrochemistry highlights dynamic surface characteristics. work provides insights into catalysts, states, conditions, advancing our understanding these electrodes role recycling through electrochemistry.

Language: Английский

Citations

11
Machine Learning-Driven Screening of Atomically Precise Pure Metal Nanoclusters for Oxygen Reduction DOI
Nishchal Bharadwaj, Diptendu Roy, A. Das

et al.

ACS Materials Letters, Journal Year: 2025, Volume and Issue: unknown, P. 500 - 507

Published: Jan. 6, 2025

Developing efficient catalysts for the oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells is challenging due to high power density and durability requirements. Subnanometer clusters (SNCs) show promise, but their fluxional behavior complex structure–activity relationships hinder catalyst design. We combine functional theory (DFT) machine learning (ML) study transition metal-based subnanometer nanoclusters (TMSNCs) ranging from 3 30 atoms, aiming establish structure activity relationship (SAR) ORR. Subdividing data set based on size periodic groups significantly improves accuracy of our ML models. Importantly, model predicting ORR catalytic performance validated through DFT calculations, identifying 12 promising catalysts. Late group TMSNCs exhibit enhanced activity, reflected a noticeable shift toward Au/Ag metals volcano plot. This underscores importance investigating late alongside Pt accelerates TMSNC design, surpassing computational screening advancing development.

Language: Английский

Citations

1
Novel Frontiers in High-Entropy Alloys DOI Creative Commons
Denzel Bridges, David Fieser,

Jannira J. Santiago

et al.

Metals, Journal Year: 2023, Volume and Issue: 13(7), P. 1193 - 1193

Published: June 27, 2023

There is little doubt that there significant potential for high-entropy alloys (HEAs) in cryogenic and aerospace applications. However, given the immense design space HEAs, much more to be explored. This review will focus on four areas of application HEAs receive less attention. These include joining technologies, HEA nanomaterial synthesis, catalysis, marine The performance as a filler metal welding brazing well their welded/brazed base discussed. Various methods synthesizing nanomaterials are reviewed with specifically highlighted applications catalysis energy storage. catalysts, particular, discussed detail regarding effectiveness, selectiveness, stability. Marine explored inherent corrosion resistance superior antifouling properties make an intriguing marine-ready material.

Language: Английский

Citations

19
Copper-based catalysts for CO2 hydrogenation: a perspective on active sites DOI Creative Commons

Yunfei Shi,

Sicong Ma, Zhi‐Pan Liu

et al.

EES Catalysis, Journal Year: 2023, Volume and Issue: 1(6), P. 921 - 933

Published: Jan. 1, 2023

This Perspective reviews the understanding of active sites on various Cu-based materials for CO 2 hydrogenation to high-value products from theoretical and experimental advances.

Language: Английский

Citations

17
Electronic structure modulation of high entropy materials for advanced electrocatalysis DOI Creative Commons
Luoluo Qi, Jingqi Guan

Green Energy & Environment, Journal Year: 2024, Volume and Issue: unknown

Published: July 1, 2024

High-entropy materials (HEMs) have managed to make their mark in the field of electrocatalysis. The flexibly adjustable component, unique configuration and proprietary core effect endow HEMs with excellent functional feature, superior stability fast reaction kinetics. Recently, relationship between compositions structures high-entropy catalysts electrocatalytic performances has been extensively investigated. Based on this motivation, we comprehensively systematically summarize HEMs, outline intrinsic properties electrochemical advantages, generalize current state-of-the-art synthetic methods, analyze active centers conjunction characterization techniques, utilize theoretical research conduct a high-throughput screening targeted catalyst exploration mechanisms, importantly, focus specially applications propose strategies for regulating electronic structure accelerate kinetics, including morphological control, defect engineering, element regulation, strain engineering so forth. Finally, provide our personal views challenges further technical improvements catalysts. This work can valuable guidance future electrocatalysts.

Language: Английский

Citations

8
A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO2 DOI

Abdul Samad,

Husnain Saghir,

Abdul Musawwir

et al.

Applied Thermal Engineering, Journal Year: 2024, Volume and Issue: 251, P. 123517 - 123517

Published: May 31, 2024

Language: Английский

Citations

6