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An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage [article]

C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Muhammed Shuaibi, Anuroop Sriram (+5 others)
2020 arXiv   pre-print
In this paper, we provide an introduction to the challenges in finding suitable electrocatalysts, how machine learning may be applied to the problem, and the use of the Open Catalyst Project OC20 dataset  ...  The use of machine learning may provide a method to efficiently approximate these calculations, leading to new approaches in finding effective electrocatalysts.  ...  Overview We hope this paper serves as a gentle introduction for the machine learning community to opportunities presented by the problem of renewable energy storage, and the use of efficient ML models  ... 
arXiv:2010.09435v1 fatcat:eynravocobfwhaumi5tmrshpbq

A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts

Ali Malek, Qianpu Wang, Stefan Baumann, Olivier Guillon, Michael Eikerling, Kourosh Malek
2021 Frontiers in Energy Research  
Searching for next-generation electrocatalyst materials for electrochemical energy technologies is a time-consuming and expensive process, even if it is enabled by high-throughput experimentation and extensive  ...  Here, we introduce a material recommendation and screening framework, and demonstrate its capabilities for certain classes of electrocatalyst materials for low or high-temperature CO2 reduction.  ...  ACKNOWLEDGMENTS KM and QW would like to thank NRC international office and NRC's Materials for Fuel Challenge program for their financial support.  ... 
doi:10.3389/fenrg.2021.609070 fatcat:2sch4tj7qvgr7kiyralq6k6jre

A Scoping Review of Renewable Energy, Sustainability and the Environment

Svitlana Kolosok, Yuriy Bilan, Tetiana Vasylieva, Adam Wojciechowski, Michał Morawski
2021 Energies  
The article aims to identify the latest trends in research on renewable energy, sustainability and the environment.  ...  A total of 92,873 publications from 123 Scopus sources for 2020–2021 are compared using the scoping review method.  ...  Introduction Researchers have been significantly interested in renewable energy, sustainable development and environmental protection in recent years.  ... 
doi:10.3390/en14154490 fatcat:jpdeq22dfvf2dkuxt2vlv7udtu

Sustainable AI: Environmental Implications, Challenges and Opportunities [article]

Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta (+13 others)
2022 arXiv   pre-print
Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization  ...  We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system  ...  ACKNOWLEDGEMENT We would like to thank Nikhil Gupta, Lei Tian, Weiyi Zheng, Manisha Jain, Adnan Aziz, and Adam Lerer for their feedback on many iterations of this draft, and in-depth technical discussions  ... 
arXiv:2111.00364v2 fatcat:lvek2xfxv5bfzh7vlio5pnzm64

ROADMPAP TECHNICAL APPENDIX. Part 3 - Sustainable Carbon-based Chemicals and (Jet)fuels

Carina Faber, Yagut Allahverdiyeva-Rinne, Vincent Artero, Laurent Baraton, Andrea Barbieri, Hervé Bercegol, Maximilian Fleischer, Han Huynhthi, Joanna Kargul, Hélène Lepaumier, Laura López, Ann Magnuson (+1 others)
2020 Zenodo  
Apply engineering skills and machine learning for photobioreactor design to optimize light harvesting as well as product biosynthesis and collection, leading to successful up-scaling and valorization  ...  -Integration of new control systems based on predictable models (machine learning) for phototrophic cultivation using natural sunlight.  ...  barriers to be overcome for market introduction Market barriers to be overcome Opportunity criteria What are the criteria that make this technology an opportunity when ready?  ... 
doi:10.5281/zenodo.3923419 fatcat:ouotbqfcdjee7nrnhonzrgpy2m

AutoMat: Accelerated Computational Electrochemical systems Discovery [article]

Emil Annevelink, Rachel Kurchin, Eric Muckley, Lance Kavalsky, Vinay I. Hegde, Valentin Sulzer, Shang Zhu, Jiankun Pu, David Farina, Matthew Johnson, Dhairya Gandhi, Adarsh Dave (+8 others)
2022 arXiv   pre-print
We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned.  ...  Furthermore, we show how to seamlessly integrate multi-fidelity predictions such as machine learning surrogates or automated robotic experiments "in-the-loop".  ...  Department of Energy, under Award Number DE-AR0001211.  ... 
arXiv:2011.04426v4 fatcat:kxyubbbhvrhy7n4s5zbwricnj4

High-Entropy Energy Materials: Challenges and New Opportunities

Yanjiao Ma, Yuan Ma, Qingsong Wang, Simon Schweidler, Miriam Botros, Tongtong Fu, Horst Hahn, Torsten Brezesinski, Ben Breitung
2021 Energy & Environmental Science  
The essential demand for functional materials enabling the realization of new energy technologies has triggered tremendous efforts in scientific and industrial research in recent years.  ...  green, reliable and renewable energy sources, for example, wind, hydropower or solar energy, to name a few. 1 However, even if such a transition can be made, most of the renewable power sources are  ...  Recently, more research focused on machine learning (ML), 167 -180 which Energy & Environmental Science Accepted Manuscript Open Access Article. Published on 02 April 2021.  ... 
doi:10.1039/d1ee00505g fatcat:jlojfxjeh5hmvof3qnw3qiztjy

An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets [article]

Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu
2022 arXiv   pre-print
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.  ...  In addition, we show that with a global receptive field and an adaptive aggregation strategy, Graphormer is more powerful than classic message-passing-based GNNs.  ...  It aims to accelerate the catalyst discovery process for solar fuels syn- thesis, long-term energy storage, and renewable fertilizer production, by using machine learning models to find lowcost electrocatalysts  ... 
arXiv:2203.06123v2 fatcat:xsgi2bhsijak5nhsbl5pplk4pe

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

M. L. Green, C. L. Choi, J. R. Hattrick-Simpers, A. M. Joshi, I. Takeuchi, S. C. Barron, E. Campo, T. Chiang, S. Empedocles, J. M. Gregoire, A. G. Kusne, J. Martin (+5 others)
2017 Applied Physics Reviews  
High-throughput experimentation generates large volumes of experimental data using combinatorial materials synthesis and rapid measurement techniques, making it an ideal experimental complement to bring  ...  This paper reviews the state-of-the-art results, opportunities, and challenges in high-throughput experimentation for materials design.  ...  ACKNOWLEDGMENTS We are grateful to Alex King, John Newsam, John Perkins, Abhijit V.  ... 
doi:10.1063/1.4977487 fatcat:5ydojmvtzvbrpe4elztaoyor4m

BMC Chemical Engineering: an open access publishing venue for the chemical engineering community

Harriet E. Manning, Robert Field, Rafiqul Gani, Adam Lee, Hyunjoo Lee, Jay H. Lee, Gongping Liu, Sang Yup Lee
2019 BMC Chemical Engineering  
that the most relevant chemical engineering research is disseminated widely for all to read and build upon.  ...  The scope of the journal is broad, considering fundamental and applied research in all areas of chemical engineering with the ultimate aim of providing an inclusive, community-focussed venue to ensure  ...  Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  ... 
doi:10.1186/s42480-019-0001-0 fatcat:qefc4oyvefckrp57gls7lkqzlq

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets [article]

Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu
2022 arXiv   pre-print
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.  ...  Empirically, Graphormer could achieve much less MAE than the originally reported results on the PCQM4M quantum chemistry dataset used in KDD Cup 2021.  ...  It aims to accelerate the catalyst discovery process for solar fuels synthesis, long-term energy storage, and renewable fertilizer production, by using machine learning models to find lowcost electrocatalysts  ... 
arXiv:2203.04810v1 fatcat:2w6nhita6bhhfcyt6oh7hrge3i

Machine Learning for Sustainable Energy Systems

Priya L. Donti, J. Zico Kolter
2021 Annual Review Environment and Resources  
We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage.  ...  In recent years, machine learning has proven to be a powerful tool for deriving insights from data.  ...  a cooperative agreement between the National Science Foundation and Carnegie • Machine Learning for Sustainable Energy Systems  ... 
doi:10.1146/annurev-environ-020220-061831 fatcat:pplsvl4zu5arngtbrov4uod74e

Computational discovery of energy materials in the era of big data and machine learning: a critical review

Ziheng Lu
2021 Materials Reports: Energy  
Together these recent innovations in computational chemistry, data informatics, and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics  ...  based on machine learning.  ...  Zhaofu Zhang for the helpful discussions.  ... 
doi:10.1016/j.matre.2021.100047 fatcat:qookvmaqfze7zogocfqrbu7awi

Society News

2017 The Electrochemical Society Interface  
New Division Officers Slates New officers for the fall 2017-spring 2019 term have been nominated for the following Division.  ...  Large scale electrochemical energy storage is critical for the integration of more renewable energy into the grid.  ...  Temperature Energy Conversion and Storage.  ... 
doi:10.1149/2.004173if fatcat:qyqhkjs7bvd35k5iqot3f5gcym

Principle of Water Electrolysis and Recent Progress of Cobalt, Nickel, and Iron-based Oxides for Oxygen Evolution Reaction

Mingquan Yu, Eko Budiyanto, Harun Tüysüz
2021 Angewandte Chemie International Edition  
The availabilities of the sustainable electricity and oxygen evolution reaction (OER) electrocatalyst are the main bottlenecks of the process for large-scale green hydrogen production.  ...  A broad range of OER electrocatalysts has been explored to decrease the overpotential and boost the kinetics of this sluggish half-reaction.  ...  Centre/Transregio 247 "Heterogeneous Oxidation Catalysis in the Liquid Phase" for the financial support.  ... 
doi:10.1002/anie.202103824 pmid:34138511 fatcat:3p7pbrnehfgazobqoooh6yus4q
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