Machine learning for a sustainable energy future

Yao, Zhenpeng; Lum, Yanwei; Johnston, Andrew; Mejia-Mendoza, Luis Martin; Zhou, Xin; Wen, Yonggang; Aspuru-Guzik, Alán; Sargent, Edward H.; Seh, Zhi Wei

doi:10.1038/s41578-022-00490-5

Machine learning for a sustainable energy future

Journal Article · 18 October 2022 · Nature Reviews. Materials

DOI:https://doi.org/10.1038/s41578-022-00490-5· OSTI ID:2421110

^[1]; ; Johnston, Andrew; Mejia-Mendoza, Luis Martin; Zhou, Xin; Wen, Yonggang; ; ;

OSTI

Not provided.

Research Organization:: Harvard Univ., Cambridge, MA (United States); Univ. of Minnesota, Minneapolis, MN (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0019300; SC0008688

OSTI ID:: 2421110

Journal Information:: Nature Reviews. Materials, Journal Name: Nature Reviews. Materials Journal Issue: 3 Vol. 8; ISSN 2058-8437

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

References (165)

Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning Okamoto, Yasuharu; Kubo, Yoshimi ACS Omega, Vol. 3, Issue 7 https://doi.org/10.1021/acsomega.8b00576	journal	July 2018
Optimization of electrochemical performance of a solid oxide fuel cell using Artificial Neural Network Ansari, M. A.; Rizvi, Syed Mohd Aijaz; Khan, Shuab 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) https://doi.org/10.1109/ICEEOT.2016.7755514	conference	March 2016
Unsupervised discovery of solid-state lithium ion conductors Zhang, Ying; He, Xingfeng; Chen, Zhiqian Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-13214-1	journal	November 2019
Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials Doan, Hieu A.; Agarwal, Garvit; Qian, Hai Chemistry of Materials, Vol. 32, Issue 15 https://doi.org/10.1021/acs.chemmater.0c00768	journal	May 2020
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks Kim, Edward; Jensen, Zach; van Grootel, Alexander Journal of Chemical Information and Modeling, Vol. 60, Issue 3 https://doi.org/10.1021/acs.jcim.9b00995	journal	January 2020
Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure Zheng, Chen; Chen, Chi; Chen, Yiming Patterns, Vol. 1, Issue 2 https://doi.org/10.1016/j.patter.2020.100013	journal	May 2020
Exploration versus Exploitation in Global Atomistic Structure Optimization Jørgensen, Mathias S.; Larsen, Uffe F.; Jacobsen, Karsten W. The Journal of Physical Chemistry A, Vol. 122, Issue 5 https://doi.org/10.1021/acs.jpca.8b00160	journal	January 2018
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO ₂ Reduction Ulissi, Zachary W.; Tang, Michael T.; Xiao, Jianping ACS Catalysis, Vol. 7, Issue 10 https://doi.org/10.1021/acscatal.7b01648	journal	August 2017
Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces Choubisa, Hitarth; Askerka, Mikhail; Ryczko, Kevin Matter, Vol. 3, Issue 2 https://doi.org/10.1016/j.matt.2020.04.016	journal	August 2020
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery Rosen, Andrew S.; Iyer, Shaelyn M.; Ray, Debmalya Matter, Vol. 4, Issue 5 https://doi.org/10.1016/j.matt.2021.02.015	journal	May 2021
Identification of the Selective Sites for Electrochemical Reduction of CO to C 2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning Huang, Yufeng; Chen, Yalu; Cheng, Tao ACS Energy Letters, Vol. 3, Issue 12 https://doi.org/10.1021/acsenergylett.8b01933	journal	November 2018
A mobile robotic chemist Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V. Nature, Vol. 583, Issue 7815 https://doi.org/10.1038/s41586-020-2442-2	journal	July 2020
Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes Takagishi, Yoichi; Yamanaka, Takumi; Yamaue, Tatsuya Batteries, Vol. 5, Issue 3 https://doi.org/10.3390/batteries5030054	journal	August 2019
An Overview of Multi-Task Learning in Deep Neural Networks Ruder, Sebastian arXiv https://doi.org/10.48550/arXiv.1706.05098	preprint	January 2017
Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery Park, Cheol Woo; Wolverton, Chris Physical Review Materials, Vol. 4, Issue 6 https://doi.org/10.1103/PhysRevMaterials.4.063801	journal	June 2020
Trends in electrochemical CO2 reduction activity for open and close-packed metal surfaces Shi, Chuan; Hansen, Heine A.; Lausche, Adam C. Physical Chemistry Chemical Physics, Vol. 16, Issue 10 https://doi.org/10.1039/c3cp54822h	journal	January 2014
Data-driven proton exchange membrane fuel cell degradation predication through deep learning method Ma, Rui; Yang, Tao; Breaz, Elena Applied Energy, Vol. 231 https://doi.org/10.1016/j.apenergy.2018.09.111	journal	December 2018
Direct calorimetric verification of thermodynamic instability of lead halide hybrid perovskites Nagabhushana, G. P.; Shivaramaiah, Radha; Navrotsky, Alexandra Proceedings of the National Academy of Sciences, Vol. 113, Issue 28 https://doi.org/10.1073/pnas.1607850113	journal	June 2016
Understanding electrified interfaces Steinmann, Stephan N.; Seh, Zhi Wei Nature Reviews Materials, Vol. 6, Issue 4 https://doi.org/10.1038/s41578-021-00303-1	journal	March 2021
Digital twin driven prognostics and health management for complex equipment Tao, Fei; Zhang, Meng; Liu, Yushan CIRP Annals, Vol. 67, Issue 1 https://doi.org/10.1016/j.cirp.2018.04.055	journal	January 2018
A novel dual iterative Q-learning method for optimal battery management in smart residential environments Wei, Qinglai; Liu, Derong; Shi, Guang IEEE Transactions on Industrial Electronics, Vol. 62, Issue 4 https://doi.org/10.1109/TIE.2014.2361485	journal	April 2015
E(n) Equivariant Graph Neural Networks Satorras, Victor Garcia; Hoogeboom, Emiel; Welling, Max arXiv https://doi.org/10.48550/arXiv.2102.09844	preprint	January 2021
Machine learning: Trends, perspectives, and prospects Jordan, M. I.; Mitchell, T. M. Science, Vol. 349, Issue 6245 https://doi.org/10.1126/science.aaa8415	journal	July 2015
On representing chemical environments Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/PhysRevB.87.184115	journal	May 2013
Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning Mocanu, Elena; Nguyen, Phuong H.; Kling, Wil L. Energy and Buildings, Vol. 116 https://doi.org/10.1016/j.enbuild.2016.01.030	journal	March 2016
The thermodynamic scale of inorganic crystalline metastability Sun, Wenhao; Dacek, Stephen T.; Ong, Shyue Ping Science Advances, Vol. 2, Issue 11 https://doi.org/10.1126/sciadv.1600225	journal	November 2016
State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach Chemali, Ephrem; Kollmeyer, Phillip J.; Preindl, Matthias Journal of Power Sources, Vol. 400 https://doi.org/10.1016/j.jpowsour.2018.06.104	journal	October 2018
Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data Cubuk, Ekin D.; Sendek, Austin D.; Reed, Evan J. The Journal of Chemical Physics, Vol. 150, Issue 21 https://doi.org/10.1063/1.5093220	journal	June 2019
A data fusion approach to optimize compositional stability of halide perovskites Sun, Shijing; Tiihonen, Armi; Oviedo, Felipe Matter, Vol. 4, Issue 4 https://doi.org/10.1016/j.matt.2021.01.008	journal	April 2021
Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city Boretti, Alberto Energy and AI, Vol. 3 https://doi.org/10.1016/j.egyai.2020.100038	journal	March 2021
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI Barredo Arrieta, Alejandro; Díaz-Rodríguez, Natalia; Del Ser, Javier Information Fusion, Vol. 58 https://doi.org/10.1016/j.inffus.2019.12.012	journal	June 2020
Learning-in-Templates Enables Accelerated Discovery and Synthesis of New Stable Double Perovskites Askerka, Mikhail; Li, Ziliang; Lempen, Mathieu Journal of the American Chemical Society, Vol. 141, Issue 8 https://doi.org/10.1021/jacs.8b13420	journal	January 2019
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán Journal of Chemical Information and Modeling, Vol. 57, Issue 4 https://doi.org/10.1021/acs.jcim.6b00332	journal	April 2017
Closed-loop optimization of fast-charging protocols for batteries with machine learning Attia, Peter M.; Grover, Aditya; Jin, Norman Nature, Vol. 578, Issue 7795 https://doi.org/10.1038/s41586-020-1994-5	journal	February 2020
Unsupervised word embeddings capture latent knowledge from materials science literature Tshitoyan, Vahe; Dagdelen, John; Weston, Leigh Nature, Vol. 571, Issue 7763 https://doi.org/10.1038/s41586-019-1335-8	journal	July 2019
Acceleration of saddle-point searches with machine learning Peterson, Andrew A. The Journal of Chemical Physics, Vol. 145, Issue 7 https://doi.org/10.1063/1.4960708	journal	August 2016
Introducing structural sensitivity into adsorption–energy scaling relations by means of coordination numbers Calle-Vallejo, Federico; Loffreda, David; Koper, Marc T. M. Nature Chemistry, Vol. 7, Issue 5 https://doi.org/10.1038/nchem.2226	journal	April 2015
Degradation model of proton exchange membrane fuel cell based on a novel hybrid method Chen, Kui; Laghrouche, Salah; Djerdir, Abdesslem Applied Energy, Vol. 252 https://doi.org/10.1016/j.apenergy.2019.113439	journal	October 2019
Machine learning in energy economics and finance: A review Ghoddusi, Hamed; Creamer, Germán G.; Rafizadeh, Nima Energy Economics, Vol. 81 https://doi.org/10.1016/j.eneco.2019.05.006	journal	June 2019
Commercializing generic technology: The case of advanced materials ventures Maine, Elicia; Garnsey, Elizabeth Research Policy, Vol. 35, Issue 3 https://doi.org/10.1016/j.respol.2005.12.006	journal	April 2006
In Situ / Operando Electrocatalyst Characterization by X-ray Absorption Spectroscopy Timoshenko, Janis; Roldan Cuenya, Beatriz Chemical Reviews, Vol. 121, Issue 2 https://doi.org/10.1021/acs.chemrev.0c00396	journal	September 2020
Maximizing renewable energy use with decentralized residential demand response Dusparic, Ivana; Taylor, Adam; Marinescu, Andrei 2015 IEEE First International Smart Cities Conference (ISC2) https://doi.org/10.1109/ISC2.2015.7366212	conference	October 2015
Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning Aguiar, J. A.; Gong, M. L.; Unocic, R. R. Science Advances, Vol. 5, Issue 10 https://doi.org/10.1126/sciadv.aaw1949	journal	October 2019
Modelling of solid oxide electrolyser cell using extreme learning machine Zhang, Caizhi; Liu, Qinglin; Wu, Qi Electrochimica Acta, Vol. 251 https://doi.org/10.1016/j.electacta.2017.08.113	journal	October 2017
Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart Journal of Power Sources, Vol. 359 https://doi.org/10.1016/j.jpowsour.2017.05.021	journal	August 2017
Neural Networks for the Prediction of Organic Chemistry Reactions Wei, Jennifer N.; Duvenaud, David; Aspuru-Guzik, Alán ACS Central Science, Vol. 2, Issue 10 https://doi.org/10.1021/acscentsci.6b00219	journal	October 2016
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization Jacobsen, T. L.; Jørgensen, M. S.; Hammer, B. Physical Review Letters, Vol. 120, Issue 2 https://doi.org/10.1103/PhysRevLett.120.026102	journal	January 2018
The path towards sustainable energy Chu, Steven; Cui, Yi; Liu, Nian Nature Materials, Vol. 16, Issue 1 https://doi.org/10.1038/nmat4834	journal	December 2016
Electrode Degradation in Lithium-Ion Batteries Pender, Joshua P.; Jha, Gaurav; Youn, Duck Hyun ACS Nano, Vol. 14, Issue 2 https://doi.org/10.1021/acsnano.9b04365	journal	January 2020
Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis Sun, Shijing; Hartono, Noor T. P.; Ren, Zekun D. Joule, Vol. 3, Issue 6 https://doi.org/10.1016/j.joule.2019.05.014	journal	June 2019
Using Machine Learning Techniques to Aid Environmental Policy Analysis Asensio, Omar Isaac; Mi, Ximin; Dharur, Sameer Case Studies in the Environment, Vol. 4, Issue 1 https://doi.org/10.1525/cse.2020.961302	journal	October 2020
GraphEBM: Molecular Graph Generation with Energy-Based Models Liu, Meng; Yan, Keqiang; Oztekin, Bora arXiv https://doi.org/10.48550/arXiv.2102.00546	preprint	January 2021
High entropy oxides for reversible energy storage Sarkar, Abhishek; Velasco, Leonardo; Wang, Di Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05774-5	journal	August 2018
Multi-fidelity machine learning models for accurate bandgap predictions of solids Pilania, G.; Gubernatis, J. E.; Lookman, T. Computational Materials Science, Vol. 129 https://doi.org/10.1016/j.commatsci.2016.12.004	journal	March 2017
Key performance indicators: A useful tool to assess Smart Grid goals Personal, Enrique; Guerrero, Juan Ignacio; Garcia, Antonio Energy, Vol. 76 https://doi.org/10.1016/j.energy.2014.09.015	journal	November 2014
Learning Quality Rating of As-Cut mc-Si Wafers via Convolutional Regression Networks Demant, Matthias; Virtue, Patrick; Kovvali, Aditya IEEE Journal of Photovoltaics, Vol. 9, Issue 4 https://doi.org/10.1109/JPHOTOV.2019.2906036	journal	July 2019
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation Krenn, Mario; Häse, Florian; Nigam, AkshatKumar Machine Learning: Science and Technology, Vol. 1, Issue 4 https://doi.org/10.1088/2632-2153/aba947	journal	November 2020
Online state of health estimation on NMC cells based on predictive analytics Berecibar, Maitane; Devriendt, Floris; Dubarry, Matthieu Journal of Power Sources, Vol. 320 https://doi.org/10.1016/j.jpowsour.2016.04.109	journal	July 2016
Self-driving laboratory for accelerated discovery of thin-film materials MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D. Science Advances, Vol. 6, Issue 20 https://doi.org/10.1126/sciadv.aaz8867	journal	May 2020
Relaxed deep learning for real-time economic generation dispatch and control with unified time scale Yin, Linfei; Yu, Tao; Zhang, Xiaoshun Energy, Vol. 149 https://doi.org/10.1016/j.energy.2018.01.165	journal	April 2018
Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials Sendek, Austin D.; Cubuk, Ekin D.; Antoniuk, Evan R. Chemistry of Materials, Vol. 31, Issue 2 https://doi.org/10.1021/acs.chemmater.8b03272	journal	November 2018
Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials Sun, Wenbo; Zheng, Yujie; Yang, Ke Science Advances, Vol. 5, Issue 11 https://doi.org/10.1126/sciadv.aay4275	journal	November 2019
PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators Broberg, Danny; Medasani, Bharat; Zimmermann, Nils E. R. Computer Physics Communications, Vol. 226 https://doi.org/10.1016/j.cpc.2018.01.004	journal	May 2018
Learning policies for battery usage optimization in electric vehicles Ermon, Stefano; Xue, Yexiang; Gomes, Carla Machine Learning, Vol. 92, Issue 1 https://doi.org/10.1007/s10994-013-5378-z	journal	May 2013
Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy Marcella, Nicholas; Liu, Yang; Timoshenko, Janis Physical Chemistry Chemical Physics, Vol. 22, Issue 34 https://doi.org/10.1039/D0CP02098B	journal	January 2020
Inverse design of nanoporous crystalline reticular materials with deep generative models Yao, Zhenpeng; Sánchez-Lengeling, Benjamín; Bobbitt, N. Scott Nature Machine Intelligence, Vol. 3, Issue 1 https://doi.org/10.1038/s42256-020-00271-1	journal	January 2021
Strategies to break linear scaling relationships Pérez-Ramírez, Javier; López, Núria Nature Catalysis, Vol. 2, Issue 11 https://doi.org/10.1038/s41929-019-0376-6	journal	October 2019
To address surface reaction network complexity using scaling relations machine learning and DFT calculations Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms14621	journal	March 2017
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems Langner, Stefan; Häse, Florian; Perea, José Darío Advanced Materials, Vol. 32, Issue 14 https://doi.org/10.1002/adma.201907801	journal	April 2020
Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships Torrisi, Steven B.; Carbone, Matthew R.; Rohr, Brian A. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00376-6	journal	July 2020
Carbothermal shock synthesis of high-entropy-alloy nanoparticles Yao, Yonggang; Huang, Zhennan; Xie, Pengfei Science, Vol. 359, Issue 6383 https://doi.org/10.1126/science.aan5412	journal	March 2018
Review of energy system flexibility measures to enable high levels of variable renewable electricity Lund, Peter D.; Lindgren, Juuso; Mikkola, Jani Renewable and Sustainable Energy Reviews, Vol. 45 https://doi.org/10.1016/j.rser.2015.01.057	journal	May 2015
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations Ward, Logan; Liu, Ruoqian; Krishna, Amar Physical Review B, Vol. 96, Issue 2 https://doi.org/10.1103/PhysRevB.96.024104	journal	July 2017
Highly Efficient Light-Trapping Structure Design Inspired By Natural Evolution Wang, Chen; Yu, Shuangcheng; Chen, Wei Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep01025	journal	January 2013
Data-driven prediction of battery cycle life before capacity degradation Severson, Kristen A.; Attia, Peter M.; Jin, Norman Nature Energy, Vol. 4, Issue 5 https://doi.org/10.1038/s41560-019-0356-8	journal	March 2019
MatD^3^: A Database and Online Presentation Package for Research Data Supporting Materials Discovery, Design, and Dissemination Laasner, Raul; Du, Xiaochen; Tanikanti, Aditya Journal of Open Source Software, Vol. 5, Issue 45 https://doi.org/10.21105/joss.01945	journal	January 2020
Insightful classification of crystal structures using deep learning Ziletti, Angelo; Kumar, Devinder; Scheffler, Matthias Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05169-6	journal	July 2018
Solar and wind energy production in relation to the electricity load curve and hydroelectricity in the northeast region of Brazil de Jong, P.; Sánchez, A. S.; Esquerre, K. Renewable and Sustainable Energy Reviews, Vol. 23 https://doi.org/10.1016/j.rser.2013.01.050	journal	July 2013
Thermodynamic limit for synthesis of metastable inorganic materials Aykol, Muratahan; Dwaraknath, Shyam S.; Sun, Wenhao Science Advances, Vol. 4, Issue 4 https://doi.org/10.1126/sciadv.aaq0148	journal	April 2018
Paris Agreement Horowitz, Cara A. International Legal Materials, Vol. 55, Issue 4 https://doi.org/10.1017/S0020782900004253	journal	August 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 https://doi.org/10.1145/2939672.2939778	conference	January 2016
Atomic-position independent descriptor for machine learning of material properties Jain, Ankit; Bligaard, Thomas Physical Review B, Vol. 98, Issue 21 https://doi.org/10.1103/PhysRevB.98.214112	journal	December 2018
The role of hydrogen and fuel cells in the global energy system Staffell, Iain; Scamman, Daniel; Velazquez Abad, Anthony Energy & Environmental Science, Vol. 12, Issue 2 https://doi.org/10.1039/C8EE01157E	journal	January 2019
Gaussian process regression for forecasting battery state of health Richardson, Robert R.; Osborne, Michael A.; Howey, David A. Journal of Power Sources, Vol. 357 https://doi.org/10.1016/j.jpowsour.2017.05.004	journal	July 2017
Universal fragment descriptors for predicting properties of inorganic crystals Isayev, Olexandr; Oses, Corey; Toher, Cormac Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms15679	journal	June 2017
Exnovating for a renewable energy transition Davidson, Debra J. Nature Energy, Vol. 4, Issue 4 https://doi.org/10.1038/s41560-019-0369-3	journal	March 2019
Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning Kim, Byung-Gook; Zhang, Yu; van der Schaar, Mihaela IEEE Transactions on Smart Grid, Vol. 7, Issue 5 https://doi.org/10.1109/TSG.2015.2495145	journal	September 2016
A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications Siddaiah, Rajanna; Saini, R. P. Renewable and Sustainable Energy Reviews, Vol. 58 https://doi.org/10.1016/j.rser.2015.12.281	journal	May 2016
Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells Jeong, Jaeki; Kim, Minjin; Seo, Jongdeuk Nature, Vol. 592, Issue 7854 https://doi.org/10.1038/s41586-021-03406-5	journal	April 2021
Predicting the state of charge and health of batteries using data-driven machine learning Ng, Man-Fai; Zhao, Jin; Yan, Qingyu Nature Machine Intelligence, Vol. 2, Issue 3 https://doi.org/10.1038/s42256-020-0156-7	journal	March 2020
Crystal symmetry determination in electron diffraction using machine learning Kaufmann, Kevin; Zhu, Chaoyi; Rosengarten, Alexander S. Science, Vol. 367, Issue 6477 https://doi.org/10.1126/science.aay3062	journal	January 2020
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
A graph-convolutional neural network model for the prediction of chemical reactivity Coley, Connor W.; Jin, Wengong; Rogers, Luke Chemical Science, Vol. 10, Issue 2 https://doi.org/10.1039/C8SC04228D	journal	January 2019
SCScore: Synthetic Complexity Learned from a Reaction Corpus Coley, Connor W.; Rogers, Luke; Green, William H. Journal of Chemical Information and Modeling, Vol. 58, Issue 2 https://doi.org/10.1021/acs.jcim.7b00622	journal	January 2018
Intelligent Hybrid Vehicle Power Control—Part II: Online Intelligent Energy Management Murphey, Yi Lu; Park, Jungme; Kiliaris, Leonidas IEEE Transactions on Vehicular Technology, Vol. 62, Issue 1 https://doi.org/10.1109/TVT.2012.2217362	journal	January 2013
Relational inductive biases, deep learning, and graph networks Battaglia, Peter W.; Hamrick, Jessica B.; Bapst, Victor arXiv https://doi.org/10.48550/arXiv.1806.01261	preprint	January 2018
High-throughput, combinatorial synthesis of multimetallic nanoclusters Yao, Yonggang; Huang, Zhennan; Li, Tangyuan Proceedings of the National Academy of Sciences, Vol. 117, Issue 12 https://doi.org/10.1073/pnas.1903721117	journal	March 2020
Data-centric middleware based digital twin platform for dependable cyber-physical systems Yun, Seongjin; Park, Jun-Hong; Kim, Won-Tae 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) https://doi.org/10.1109/ICUFN.2017.7993933	conference	July 2017
Inverse molecular design using machine learning: Generative models for matter engineering Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán Science, Vol. 361, Issue 6400 https://doi.org/10.1126/science.aat2663	journal	July 2018
Planning chemical syntheses with deep neural networks and symbolic AI Segler, Marwin H. S.; Preuss, Mike; Waller, Mark P. Nature, Vol. 555, Issue 7698 https://doi.org/10.1038/nature25978	journal	March 2018
Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries Allam, Omar; Cho, Byung Woo; Kim, Ki Chul RSC Advances, Vol. 8, Issue 69 https://doi.org/10.1039/C8RA07112H	journal	January 2018
Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells Musztyfaga-Staszuk, M.; Honysz, R. Archives of Metallurgy and Materials, Vol. 60, Issue 3 https://doi.org/10.1515/amm-2015-0290	journal	September 2015
The sensitivity of Cu for electrochemical carbon dioxide reduction to hydrocarbons as revealed by high throughput experiments Lai, Yungchieh; Jones, Ryan J. R.; Wang, Yu Journal of Materials Chemistry A, Vol. 7, Issue 47 https://doi.org/10.1039/C9TA10111J	journal	January 2019
Scanning Electrochemical Flow Cell with Online Mass Spectroscopy for Accelerated Screening of Carbon Dioxide Reduction Electrocatalysts Lai, Yungchieh; Jones, Ryan J. R.; Wang, Yu ACS Combinatorial Science, Vol. 21, Issue 10 https://doi.org/10.1021/acscombsci.9b00130	journal	August 2019
Use machine learning to find energy materials De Luna, Phil; Wei, Jennifer; Bengio, Yoshua Nature, Vol. 552, Issue 7683 https://doi.org/10.1038/d41586-017-07820-6	journal	December 2017
Underspecification Presents Challenges for Credibility in Modern Machine Learning D'Amour, Alexander; Heller, Katherine; Moldovan, Dan arXiv https://doi.org/10.48550/arXiv.2011.03395	preprint	January 2020
Multi-Agent Correlated Equilibrium Q(λ) Learning for Coordinated Smart Generation Control of Interconnected Power Grids Yu, T.; Wang, H. Z.; Zhou, B. IEEE Transactions on Power Systems, Vol. 30, Issue 4 https://doi.org/10.1109/TPWRS.2014.2357079	journal	July 2015
Batch Active Learning Using Determinantal Point Processes Bıyık, Erdem; Wang, Kenneth; Anari, Nima arXiv https://doi.org/10.48550/arXiv.1906.07975	preprint	January 2019
Controlling an organic synthesis robot with machine learning to search for new reactivity Granda, Jarosław M.; Donina, Liva; Dragone, Vincenza Nature, Vol. 559, Issue 7714 https://doi.org/10.1038/s41586-018-0307-8	journal	July 2018
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation Bach, Sebastian; Binder, Alexander; Montavon, Grégoire PLOS ONE, Vol. 10, Issue 7 https://doi.org/10.1371/journal.pone.0130140	journal	July 2015
Graph Attention Networks Veličković, Petar; Cucurull, Guillem; Casanova, Arantxa arXiv https://doi.org/10.48550/arXiv.1710.10903	preprint	January 2017
Building energy load forecasting using Deep Neural Networks Marino, Daniel L.; Amarasinghe, Kasun; Manic, Milos IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society https://doi.org/10.1109/IECON.2016.7793413	conference	October 2016
Numerical modeling of non-planar hydraulic fracture propagation in brittle and ductile rocks using XFEM with cohesive zone method Wang, HanYi Journal of Petroleum Science and Engineering, Vol. 135 https://doi.org/10.1016/j.petrol.2015.08.010	journal	November 2015
Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries Joshi, Rajendra P.; Eickholt, Jesse; Li, Liling ACS Applied Materials & Interfaces, Vol. 11, Issue 20 https://doi.org/10.1021/acsami.9b04933	journal	April 2019
Robot-Accelerated Perovskite Investigation and Discovery Li, Zhi; Najeeb, Mansoor Ani; Alves, Liana Chemistry of Materials, Vol. 32, Issue 13 https://doi.org/10.1021/acs.chemmater.0c01153	journal	June 2020
Detection of Pt[sup z+] Ions and Pt Nanoparticles Inside the Membrane of a Used PEMFC Guilminot, E.; Corcella, A.; Charlot, F. Journal of The Electrochemical Society, Vol. 154, Issue 1 https://doi.org/10.1149/1.2388863	journal	January 2007
Atomic structure of sensitive battery materials and interfaces revealed by cryo–electron microscopy Li, Yuzhang; Li, Yanbin; Pei, Allen Science, Vol. 358, Issue 6362 https://doi.org/10.1126/science.aam6014	journal	October 2017
How machine learning can help select capping layers to suppress perovskite degradation Hartono, Noor Titan Putri; Thapa, Janak; Tiihonen, Armi Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-17945-4	journal	August 2020
Performance Analysis of Machine-Learning Approaches for Modeling the Charging/Discharging Proﬁles of Stationary Battery Systems with Non-Uniform Cell Aging Kandasamy, Nandha; Badrinarayanan, Rajagopalan; Kanamarlapudi, Venkata Batteries, Vol. 3, Issue 2 https://doi.org/10.3390/batteries3020018	journal	June 2017
Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools Odabaşı, Çağla; Yıldırım, Ramazan Nano Energy, Vol. 56 https://doi.org/10.1016/j.nanoen.2018.11.069	journal	February 2019
Enhancement of the chemical stability in confined δ-Bi2O3 Sanna, Simone; Esposito, Vincenzo; Andreasen, Jens Wenzel Nature Materials, Vol. 14, Issue 5 https://doi.org/10.1038/nmat4266	journal	April 2015
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes Ahmad, Zeeshan; Xie, Tian; Maheshwari, Chinmay ACS Central Science, Vol. 4, Issue 8 https://doi.org/10.1021/acscentsci.8b00229	journal	August 2018
Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot Epps, Robert W.; Bowen, Michael S.; Volk, Amanda A. Advanced Materials, Vol. 32, Issue 30 https://doi.org/10.1002/adma.202001626	journal	June 2020
Combining theory and experiment in electrocatalysis: Insights into materials design Seh, Zhi Wei; Kibsgaard, Jakob; Dickens, Colin F. Science, Vol. 355, Issue 6321 https://doi.org/10.1126/science.aad4998	journal	January 2017
Identifying Active Sites for CO 2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations Chen, Yalu; Huang, Yufeng; Cheng, Tao Journal of the American Chemical Society, Vol. 141, Issue 29 https://doi.org/10.1021/jacs.9b04956	journal	June 2019
Assessing Synthetic Accessibility of Chemical Compounds Using Machine Learning Methods Podolyan, Yevgeniy; Walters, Michael A.; Karypis, George Journal of Chemical Information and Modeling, Vol. 50, Issue 6 https://doi.org/10.1021/ci900301v	journal	June 2010
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning Lu, Shuaihua; Zhou, Qionghua; Ouyang, Yixin Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05761-w	journal	August 2018
Sequence Fault Diagnosis for PEMFC Water Management Subsystem Using Deep Learning With t-SNE Liu, Jiawei; Li, Qi; Yang, Hanqing IEEE Access, Vol. 7 https://doi.org/10.1109/ACCESS.2019.2927092	journal	January 2019
Learning properties of ordered and disordered materials from multi-fidelity data Chen, Chi; Zuo, Yunxing; Ye, Weike Nature Computational Science, Vol. 1, Issue 1 https://doi.org/10.1038/s43588-020-00002-x	journal	January 2021
Machine learning in materials science: From explainable predictions to autonomous design Pilania, Ghanshyam Computational Materials Science, Vol. 193 https://doi.org/10.1016/j.commatsci.2021.110360	journal	June 2021
Materials for solar fuels and chemicals Montoya, Joseph H.; Seitz, Linsey C.; Chakthranont, Pongkarn Nature Materials, Vol. 16, Issue 1 https://doi.org/10.1038/nmat4778	journal	December 2016
The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning Zheng, Stephan; Trott, Alexander; Srinivasa, Sunil Science Advances, Vol. 8, Issue 18 https://doi.org/10.1126/sciadv.abk2607	journal	May 2022
Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy Timoshenko, Janis; Anspoks, Andris; Cintins, Arturs Physical Review Letters, Vol. 120, Issue 22 https://doi.org/10.1103/PhysRevLett.120.225502	journal	May 2018
A review of deep learning for renewable energy forecasting Wang, Huaizhi; Lei, Zhenxing; Zhang, Xian Energy Conversion and Management, Vol. 198 https://doi.org/10.1016/j.enconman.2019.111799	journal	October 2019
Accelerated discovery of CO2 electrocatalysts using active machine learning Zhong, Miao; Tran, Kevin; Min, Yimeng Nature, Vol. 581, Issue 7807 https://doi.org/10.1038/s41586-020-2242-8	journal	May 2020
ChemOS: An orchestration software to democratize autonomous discovery Roch, Loïc M.; Häse, Florian; Kreisbeck, Christoph PLOS ONE, Vol. 15, Issue 4 https://doi.org/10.1371/journal.pone.0229862	journal	April 2020
Stabilizing polymer electrolytes in high-voltage lithium batteries Choudhury, Snehashis; Tu, Zhengyuan; Nijamudheen, A. Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-11015-0	journal	July 2019
Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations Ripalda, J. M.; Buencuerpo, J.; García, I. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-07431-3	journal	December 2018
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules Gómez-Bombarelli, Rafael; Wei, Jennifer N.; Duvenaud, David ACS Central Science, Vol. 4, Issue 2 https://doi.org/10.1021/acscentsci.7b00572	journal	January 2018
Design, synthesis and selection of DNA-encoded small-molecule libraries Clark, Matthew A.; Acharya, Raksha A.; Arico-Muendel, Christopher C. Nature Chemical Biology, Vol. 5, Issue 9 https://doi.org/10.1038/nchembio.211	journal	August 2009
Machine-Learning-Accelerated Perovskite Crystallization Kirman, Jeffrey; Johnston, Andrew; Kuntz, Douglas A. Matter, Vol. 2, Issue 4 https://doi.org/10.1016/j.matt.2020.02.012	journal	April 2020
Key Performance Indicators of Charging infrastructure Helmus, J.; Van den Hoed, R. World Electric Vehicle Journal, Vol. 8, Issue 4 https://doi.org/10.3390/wevj8040733	journal	December 2016
A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions Jinich, Adrian; Sanchez-Lengeling, Benjamin; Ren, Haniu ACS Central Science, Vol. 5, Issue 7 https://doi.org/10.1021/acscentsci.9b00297	journal	June 2019
Design of multilayer antireflection coatings made from co-sputtered and low-refractive-index materials by genetic algorithm Schubert, Martin F.; Mont, Frank W.; Chhajed, Sameer Optics Express, Vol. 16, Issue 8 https://doi.org/10.1364/OE.16.005290	journal	April 2008
Self-supervised Learning: Generative or Contrastive Liu, Xiao; Zhang, Fanjin; Hou, Zhenyu IEEE Transactions on Knowledge and Data Engineering https://doi.org/10.1109/TKDE.2021.3090866	journal	January 2021
Adaptive power management using reinforcement learning Tan, Ying; Liu, Wei; Qiu, Qinru Proceedings of the 2009 International Conference on Computer-Aided Design https://doi.org/10.1145/1687399.1687486	conference	November 2009
Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets Hoffmann, Jordan; Bar-Sinai, Yohai; Lee, Lisa M. Science Advances, Vol. 5, Issue 4 https://doi.org/10.1126/sciadv.aau6792	journal	April 2019
Darwin at High Temperature: Advancing Solar Cell Material Design Using Defect Kinetics Simulations and Evolutionary Optimization Fenning, David P.; Hofstetter, Jasmin; Morishige, Ashley E. Advanced Energy Materials, Vol. 4, Issue 13 https://doi.org/10.1002/aenm.201400459	journal	May 2014
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties Xie, Tian; Grossman, Jeffrey C. Physical Review Letters, Vol. 120, Issue 14 https://doi.org/10.1103/PhysRevLett.120.145301	journal	April 2018
Representation Learning: A Review and New Perspectives Bengio, Y.; Courville, A.; Vincent, P. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 8 https://doi.org/10.1109/TPAMI.2013.50	journal	August 2013
Semi-supervised machine-learning classification of materials synthesis procedures Huo, Haoyan; Rong, Ziqin; Kononova, Olga npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0204-1	journal	July 2019
Machine learning bandgaps of double perovskites Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P. Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep19375	journal	January 2016
Local Bayesian optimizer for atomic structures Garijo del Río, Estefanía; Mortensen, Jens Jørgen; Jacobsen, Karsten Wedel Physical Review B, Vol. 100, Issue 10 https://doi.org/10.1103/PhysRevB.100.104103	journal	September 2019
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid Hachmann, Johannes; Olivares-Amaya, Roberto; Atahan-Evrenk, Sule The Journal of Physical Chemistry Letters, Vol. 2, Issue 17 https://doi.org/10.1021/jz200866s	journal	August 2011
A robotic platform for flow synthesis of organic compounds informed by AI planning Coley, Connor W.; Thomas, Dale A.; Lummiss, Justin A. M. Science, Vol. 365, Issue 6453 https://doi.org/10.1126/science.aax1566	journal	August 2019
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution Tran, Kevin; Ulissi, Zachary W. Nature Catalysis, Vol. 1, Issue 9 https://doi.org/10.1038/s41929-018-0142-1	journal	September 2018
Electrochemical Characteristics of Layered Transition Metal Oxide Cathode Materials for Lithium Ion Batteries: Surface, Bulk Behavior, and Thermal Properties Tian, Chixia; Lin, Feng; Doeff, Marca M. Accounts of Chemical Research, Vol. 51, Issue 1 https://doi.org/10.1021/acs.accounts.7b00520	journal	December 2017
Multi-agent residential demand response based on load forecasting Dusparic, Ivana; Harris, Colin; Marinescu, Andrei 2013 1st IEEE Conference on Technologies for Sustainability (SusTech) https://doi.org/10.1109/SusTech.2013.6617303	conference	August 2013
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials Sendek, Austin D.; Yang, Qian; Cubuk, Ekin D. Energy & Environmental Science, Vol. 10, Issue 1 https://doi.org/10.1039/C6EE02697D	journal	January 2017
Machine Learning in Medicine Rajkomar, Alvin; Dean, Jeffrey; Kohane, Isaac New England Journal of Medicine, Vol. 380, Issue 14 https://doi.org/10.1056/NEJMra1814259	journal	April 2019
Vaccine R&D success rates and development times Struck, Mark-M. Nature Biotechnology, Vol. 14, Issue 5 https://doi.org/10.1038/nbt0596-591	journal	May 1996
Using Deep Learning to Predict Fracture Patterns in Crystalline Solids Hsu, Yu-Chuan; Yu, Chi-Hua; Buehler, Markus J. Matter, Vol. 3, Issue 1 https://doi.org/10.1016/j.matt.2020.04.019	journal	July 2020
Crystal Structure Prediction via Deep Learning Ryan, Kevin; Lengyel, Jeff; Shatruk, Michael Journal of the American Chemical Society, Vol. 140, Issue 32 https://doi.org/10.1021/jacs.8b03913	journal	June 2018

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