Machine Learning and High Throughput Synthesis Acceleration of the Discovery of Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts

析氧 过电位 电催化剂 催化作用 电化学能量转换 电解水 材料科学 电解质 电解 电化学 纳米技术 化学工程 化学 电极 工程类 生物化学 物理化学
作者
Ahmed Sabry Farghaly,Magali Ferrandon,Daniel Schwalbe‐Koda,James Damewood,Jessica Karaguesian,Rafael Gómez‐Bombarelli,Deborah J. Myers
出处
期刊:Meeting abstracts 卷期号:MA2022-02 (44): 1673-1673 被引量:1
标识
DOI:10.1149/ma2022-02441673mtgabs
摘要

Accelerating the development and discovery of new catalysts is vital for advancing many electrochemical energy conversion technologies (EECT) required to achieve a sustainable future utilizing carbon-free fuel, a circular economy, and to meet the grand energy challenges of the 21 st century. The oxygen evolution reaction (OER) is at the heart of many EECT such as water and carbon dioxide electrolyzers, fuel cells, and metal-oxygen batteries. The sluggish kinetics of oxygen electrocatalysis, resulting high overpotential necessary to attain practical current densities, and the high cost of the state-of-the-art OER platinum group metal (PGM) and precious metals catalysts (i.e., IrO 2 and RuO 2 ) limit the cost-effective implementation and development of several promising electrolysis technologies. 1-3 The development of alternative PGM-free OER catalysts, with comparable or superior activity and durability to the PGM catalysts and derived from earth-abundant materials has thus been an active research area for decades. The application of perovskite oxides as PGM-free electrocatalysts for the OER in alkaline environments has seen significant research interest in the last decade, with tri-metallic and tetra-metallic compounds showing activities comparable to PGM-based catalysts. 4,5 The chemical space of these compounds is exceptionally large, yet the development of new perovskite oxides with high OER performance (activity and durability) has been limited and often discovered through trial and error, a time and cost inefficient route that restricted the discovery of more advanced materials. Recent advances in high-performance computing, machine learning (ML), and high throughput material synthesis and screening technologies have enabled high-throughput catalyst design and discovery. 4-10 This presentation will describe how the machine learning and high throughput synthesis technologies worked synergistically to accelerate the discovery of alkaline oxygen evolution reaction electrocatalysts. The role of ML in accelerating the materials synthesis and the role of high throughput synthesis in optimizing the ML model predictions will be discussed. Acknowledgments This work was supported by the U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) under the DIFFERENTIATE program. This work was authored in part by Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. DE-AC02-06CH11357. References Katsounaros, Ioannis, Serhiy Cherevko, Aleksandar R. Zeradjanin, and Karl JJ Mayrhofer. "Oxygen electrochemistry as a cornerstone for sustainable energy conversion." Angewandte Chemie International Edition 53, no. 1 (2014): 102-121. Lee, Youngmin, Jin Suntivich, Kevin J. May, Erin E. Perry, and Yang Shao-Horn. "Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions." The journal of physical chemistry letters 3, no. 3 (2012): 399-404. Cherevko, S. et al. Oxygen and hydrogen evolution reactions on Ru, RuO2, Ir, and IrO2 thin film electrodes in acidic and alkaline electrolytes: A comparative study on activity and stability. Today 262 , 170–180 (2016). Nahar, Lamia, Ahmed A. Farghaly, Richard J. Alan Esteves, and Indika U. Arachchige. "Shape controlled synthesis of Au/Ag/Pd nanoalloys and their oxidation-induced self-assembly into electrocatalytically active aerogel monoliths." Chemistry of Materials 29, no. 18 (2017): 7704-7715. Farghaly, Ahmed A., Rezaul K. Khan, and Maryanne M. Collinson. "Biofouling-resistant platinum bimetallic alloys." ACS applied materials & interfaces 10, no. 25 (2018): 21103-21112. Khan, Rezaul K., Ahmed A. Farghaly, Tiago A. Silva, Dexian Ye, and Maryanne M. Collinson. "Gold-Nanoparticle-Decorated Titanium Nitride Electrodes Prepared by Glancing-Angle Deposition for Sensing Applications." ACS Applied Nano Materials 2, no. 3 (2019): 1562-1569. Farghaly, Ahmed A., Mai Lam, Christopher J. Freeman, Badharinadh Uppalapati, and Maryanne M. Collinson. "Potentiometric measurements in biofouling solutions: comparison of nanoporous gold to planar gold." Journal of The Electrochemical Society 163, no. 4 (2015): H3083. Suntivich, Jin, Kevin J. May, Hubert A. Gasteiger, John B. Goodenough, and Yang Shao-Horn. "A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles." Science 334, no. 6061 (2011): 1383-1385. Hwang, Jonathan, Zhenxing Feng, Nenian Charles, Xiao Renshaw Wang, Dongkyu Lee, Kelsey A. Stoerzinger, Sokseiha Muy et al. "Tuning perovskite oxides by strain: electronic structure, properties, and functions in (electro) catalysis and ferroelectricity." Materials Today 31 (2019): 100-118. Gómez-Bombarelli, Rafael, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. "Automatic chemical design using a data-driven continuous representation of molecules." ACS central science 4, no. 2 (2018): 268-276.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
淡定的思松应助通~采纳,获得10
1秒前
1秒前
明亮的八宝粥完成签到,获得积分10
1秒前
mayungui发布了新的文献求助10
1秒前
大型海狮完成签到,获得积分10
1秒前
搜集达人应助科研菜鸟采纳,获得10
2秒前
雨天有伞完成签到,获得积分10
2秒前
蕾子发布了新的文献求助10
2秒前
2秒前
zhui发布了新的文献求助10
2秒前
wanci应助jxcandice采纳,获得10
2秒前
factor发布了新的文献求助10
2秒前
3秒前
泊声发布了新的文献求助20
3秒前
narthon完成签到 ,获得积分10
3秒前
梦幻完成签到,获得积分10
3秒前
1604531786完成签到,获得积分10
3秒前
研友_LMNjkn发布了新的文献求助10
4秒前
xiao发布了新的文献求助10
4秒前
ww发布了新的文献求助10
4秒前
5秒前
Olsters发布了新的文献求助10
5秒前
深情安青应助该睡觉啦采纳,获得10
5秒前
5秒前
SEV完成签到,获得积分20
5秒前
愉快迎荷完成签到,获得积分10
6秒前
矮小的聪展完成签到,获得积分10
7秒前
factor完成签到,获得积分10
7秒前
Hello应助李来仪采纳,获得10
8秒前
SEV发布了新的文献求助10
8秒前
8秒前
8秒前
坚强亦丝应助隐形机器猫采纳,获得10
9秒前
小马甲应助SCI采纳,获得10
10秒前
老疯智发布了新的文献求助10
10秒前
sweetbearm应助通~采纳,获得10
10秒前
神凰完成签到,获得积分10
10秒前
Z小姐发布了新的文献求助10
11秒前
NexusExplorer应助白泽采纳,获得10
11秒前
12秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527849
求助须知:如何正确求助?哪些是违规求助? 3107938
关于积分的说明 9287239
捐赠科研通 2805706
什么是DOI,文献DOI怎么找? 1540033
邀请新用户注册赠送积分活动 716893
科研通“疑难数据库(出版商)”最低求助积分说明 709794