BetaCitation Atlas

RESEARCH PAPER · Journal of Materials Chemistry A · 2019

Machine learning for renewable energy materials

Geun Ho Gu, Juhwan Noh, I. P. Kim, Yousung Jung

Read original paper ↗

See where this research leads.

307 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Machine learning for renewable energy materialsOriginalConstrained Bayesian Optimization for Automatic Chemical Design45Search for high-capacity oxygen storage materials by materials informatics12Data‐Driven Materials Science: Status, Challenges, and Perspectives700Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Inverse Design of Solid-State Materials via a Continuous Representation379A high throughput molecular screening for organic electronics via machine learning: present status and perspective72A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells41Constrained Bayesian optimization for automatic chemical design using variational autoencoders339Perspectives and guidelines on thermodynamic modelling of deep eutectic solvents138Use of Machine Learning and Infrared Spectra for Rheological Characterization and Application to the Apricot17A review of composite solid-state electrolytes for lithium batteries: fundamentals, key materials and advanced structures1030Correction: Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials4Cost, performance prediction and optimization of a vanadium flow battery by machine-learning122Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials17Interactive human–machine learning framework for modelling of ferroelectric–dielectric composites14Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts219Machine-enabled inverse design of inorganic solid materials: promises and challenges190Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods52Automated in Silico Design of Homogeneous Catalysts186A Critical Review of Machine Learning of Energy Materials606Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy165Predicting the state of charge and health of batteries using data-driven machine learning784Methods for comparing uncertainty quantifications for material property predictions205Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation70Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals49Energy and AI69Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry62Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology484Machine learning assisted materials design and discovery for rechargeable batteries466Improving Smart Cities Safety Using Sound Events Detection Based on Deep Neural Network Algorithms81Modelling and understanding battery materials with machine-learning-driven atomistic simulations114Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combination with data science84Predicting Inorganic Photovoltaic Materials with Efficiencies >26% via Structure-Relevant Machine Learning and Density Functional Calculations28The Cascade Reactions of Indigo with Propargyl Substrates for Heterocyclic and Photophysical Diversity10Machine learning for halide perovskite materials130Modeling the Dielectric Constant of Silicon-Based Nanocomposites Using Machine Learning3Critical review of machine learning applications in perovskite solar research139Deep eutectic solvents (DESs): A short overview of the thermophysical properties and current use as base fluid for heat transfer nanofluids85Importance of Equilibration Method and Sampling for Ab Initio Molecular Dynamics Simulations of Solvent–Lithium-Salt Systems in Lithium-Oxygen Batteries27Advanced mono‐ and multi‐dimensional gas chromatography–mass spectrometry techniques for oxygen‐containing compound characterization in biomass and biofuel samples35“More is Different:” Synergistic Effect and Structural Engineering in Double‐Atom Catalysts349Machine learning for high performance organic solar cells: current scenario and future prospects375Interfacial engineering of Cu2Se/Co3Se4 multivalent hetero-nanocrystals for energy-efficient electrocatalytic co-generation of value-added chemicals and hydrogen100Coronene derivatives for transparent organic photovoltaics through inverse materials design16The Hubbard-U correction and optical properties of d metal oxide photocatalysts18Machine learning toward advanced energy storage devices and systems199Artificial intelligence and machine learning in design of mechanical materials690A data-driven and DFT assisted theoretic guide for membrane design in flow batteries22A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds58A new material discovery platform of stable layered oxide cathodes for K-ion batteries78A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection205Applications of Machine Learning in Harnessing of Renewable Energy2Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learning107Demystifying Structure-Property Correlations for Perovskite Oxides Using Machine Learning to Discover Lead-Free Alternatives Solar Cells0Machine Learning in X-ray Imaging and Microscopy Applications4Predicting the Electronic and Structural Properties of Two-Dimensional Materials Using Machine Learning14The role of artificial intelligence in revealing the results of the interaction of biological materials with each other or with chemicals7Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design62Computational design of structured chemical products3Machine learning for guiding high-temperature PEM fuel cells with greater power density30Enabling thermal-neutral electrolysis for CO2-to-fuel conversions with a hybrid deep learning strategy45Machine learning for advanced energy materials208Comment on acp-2020-12580Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy39Comment on acp-2020-12580Electrochemical ammonia synthesis: Mechanistic understanding and catalyst design493Gas sensing based on organic composite materials: Review of sensor types, progresses and challenges83Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields175Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks201Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Free energy of ( CoxMn1−x)3O4 mixed phases from machine-learning-enhanced ab initio calculations6Accelerated search for ABO3-type the electronic contribution of polycrystalline dielectric constants by machine learning26Comment on acp-2020-12580Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents85Machine Learning Boosts the Design and Discovery of Nanomaterials138Open Catalyst 2020 (OC20) Dataset and Community Challenges765Reply on RC10Reply on RC20Reply on RC30Application-Driven Carbon Nanotube Functional Materials246Probing of Neural Networks as a Bridge from Ab Initio Relevant Characteristics to Differential Scanning Calorimetry Measurements of High‐Energy Compounds31Machine learning prediction of the optimal carrier concentration and band gap of quaternary thermoelectric materials via element feature descriptors12Database Creation, Visualization, and Statistical Learning for Polymer Li+-Electrolyte Design32Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions28Effectively improving the accuracy of PBE functional in calculating the solid band gap via machine learning98Progress and perspective of the cathode/electrolyte interface construction in all‐solid‐state lithium batteries124Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment22Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts32Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning33Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Artificial Intelligence Applied to Battery Research: Hype or Reality?503Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction110Entropy-based active learning of graph neural network surrogate models for materials properties34Machine Learning Accelerates the Discovery of Light-Absorbing Materials for Double Perovskite Solar Cells28Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells90Facile Synthesis of Copper Oxide-Cobalt Oxide/Nitrogen-Doped Carbon (Cu2O-Co3O4/CN) Composite for Efficient Water Splitting41Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework75spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.7Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Adaptive Learning-Driven High-Throughput Synthesis of Oxygen Reduction Reaction Fe-N-C Electrocatalysts27Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency193Machine learning-assisted design of flow fields for redox flow batteries117State-of-the-art practices to upgrade biomass fast pyrolysis derived bio-oil8Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning84Three-dimensional modeling of Lithium-ion battery electrode manufacturing0Performance Predictors for Organic Cathodes of Lithium-Ion Battery23Bimetallic Gold–Silver Nanostructures Drive Low Overpotentials for Electrochemical Carbon Dioxide Reduction34Machine Learning in Materials Chemistry: An Invitation46Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials62Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors122Agents for sequential learning using multiple-fidelity data38Machine learning in energy storage materials106Unsupervised machine learning to classify crystal structures according to their structural distortion: A case study on Li-argyrodite solid-state electrolytes11A novel disassembly process of end-of-life lithium-ion batteries enhanced by online sensing and machine learning techniques65Data‐Driven Materials Innovation and Applications187Transfer learning using attentions across atomic systems with graph neural networks (TAAG)34Continuum Modeling of Porous Electrodes for Electrochemical Synthesis147Artificial Intelligence for Electricity Supply Chain automation84Multivariate Analysis of Mixed Ternary and Quaternary A-Site Organic Cations in Tin Iodide Perovskite Solar Cells16The machine learning in lithium-ion batteries: A review45New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning0Ultra-selective tin oxide-based chemiresistive gas sensor employing signal transform and machine learning techniques64Linking electronic structure to adsorption energies: metal surfaces and single-atom catalysts10Machine-learning-assisted insight into the cathode catalyst layer in proton exchange membrane fuel cells42Advances of machine learning in multi-energy district communities‒ mechanisms, applications and perspectives116Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Inverse Design for Coating Parameters in Nano-Film Growth Based on Deep Learning Neural Network and Particle Swarm Optimization Algorithm9A Generative Approach to Materials Discovery, Design, and Optimization121Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data27Artificial Intelligence‐Based Material Discovery for Clean Energy Future47Machine learning-facilitated multiscale imaging for energy materials27Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects24Machine learning accelerated carbon neutrality research using big data—from predictive models to interatomic potentials5Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Computational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges9Machine Learning Spectroscopy Based on Group Contribution and Molecule Contribution Methods0Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization104Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives113Towards green smart cities using Internet of Things and optimization algorithms: A systematic and bibliometric review75Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Use machine learning algorithms to predict turbine power generation to replace renewable energy with fossil fuels49Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review119Machine learning for intelligent welding and manufacturing systems: research progress and perspective review37Designing graded fuel cell electrodes for proton exchange membrane (PEM) fuel cells with recurrent neural network (RNN) approaches34Advances in machine learning for high value-added applications of lignocellulosic biomass46A chemically inspired convolutional neural network using electronic structure representation4

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

307 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Constrained Bayesian Optimization for Automatic Chemical Design

    arXiv (Cornell University) · 2017-09-16

    Ryan‐Rhys Griffiths, José Miguel Hernández-Lobato

    Read original paper ↗ ·

  2. Search for high-capacity oxygen storage materials by materials informatics

    RSC Advances · 2019-01-01

    Nobuko Ohba, Takuro Yokoya, Seiji Kajita, Kensuke Takechi

    Read original paper ↗ ·

  3. Data‐Driven Materials Science: Status, Challenges, and Perspectives

    Advanced Science · 2019-09-01

    Lauri Himanen, Amber Geurts, Adam Stuart Foster, Patrick Rinke

    Read original paper ↗ ·

  4. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt

    Angewandte Chemie · 2019-09-25

    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen

    Read original paper ↗ ·

  5. Autonomous Discovery in the Chemical Sciences Part I: Progress

    Angewandte Chemie International Edition · 2019-09-25

    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen

    Read original paper ↗ ·

  6. Inverse Design of Solid-State Materials via a Continuous Representation

    Matter · 2019-10-02

    Juhwan Noh, Jaehoon Kim, Helge Sören Stein, Benjamín Sánchez-Lengeling, John M. Gregoire, Alán Aspuru‐Guzik, Yousung Jung

    Read original paper ↗ ·

  7. A high throughput molecular screening for organic electronics via machine learning: present status and perspective

    Japanese Journal of Applied Physics · 2019-10-18

    Akinori Saeki, Kakaraparthi Kranthiraja

    Read original paper ↗ ·

  8. A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells

    Advanced Intelligent Systems · 2019-11-12

    Min-Hsuan Lee

    Read original paper ↗ ·

  9. Constrained Bayesian optimization for automatic chemical design using variational autoencoders

    Chemical Science · 2019-11-18

    Ryan‐Rhys Griffiths, José Miguel Hernández-Lobato

    Read original paper ↗ ·

  10. Perspectives and guidelines on thermodynamic modelling of deep eutectic solvents

    Journal of Molecular Liquids · 2019-12-04

    Ismail I.I. Alkhatib, Daniel Bahamón, Fèlix Llovell, Mohammad R.M. Abu‐Zahra, Lourdes F. Vega

    Read original paper ↗ ·

  11. Use of Machine Learning and Infrared Spectra for Rheological Characterization and Application to the Apricot

    Scientific Reports · 2019-12-16

    Xavier F. Cadet, Ophélie Lo-Thong-Viramoutou, Sylvie Bureau, Réda Dehak, Miloud Bessafi

    Read original paper ↗ ·

  12. A review of composite solid-state electrolytes for lithium batteries: fundamentals, key materials and advanced structures

    Chemical Society Reviews · 2020-01-01

    Yun Hong Zheng, Yuze Yao, Jiahua Ou, Matthew Li, Dan Luo, Haozhen Dou, Zhaoqiang Li, Khalil Amine, Aiping Yu, Zhongwei Chen

    Read original paper ↗ ·

  13. Correction: Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials

    Journal of Materials Chemistry C · 2020-01-01

    Lihao Chen, Ben Xu, Jia Chen, Ke Bi, Changjiao Li, Shengyu Lu, Guo-Sheng Hu, Yuanhua Lin

    Read original paper ↗ ·

  14. Cost, performance prediction and optimization of a vanadium flow battery by machine-learning

    Energy & Environmental Science · 2020-01-01

    Tianyu Li, Feng Xing, Tao Liu, Jiawei Sun, Dingqin Shi, Huamin Zhang, Xianfeng Li

    Read original paper ↗ ·

  15. Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials

    Journal of Materials Chemistry C · 2020-01-01

    Lihao Chen, Ben Xu, Jia Chen, Ke Bi, Changjiao Li, Shengyu Lu, Guo-Sheng Hu, Yuanhua Lin

    Read original paper ↗ ·

  16. Interactive human–machine learning framework for modelling of ferroelectric–dielectric composites

    Journal of Materials Chemistry C · 2020-01-01

    Ning Liu, Achintha Ihalage, Hangfeng Zhang, Henry Giddens, Haixue Yan, Yang Hao

    Read original paper ↗ ·

  17. Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts

    Journal of Materials Chemistry A · 2020-01-01

    Mohammad Zafari, Deepak Kumar, Muhammad Umer, Kwang S. Kim

    Read original paper ↗ ·

  18. Machine-enabled inverse design of inorganic solid materials: promises and challenges

    Chemical Science · 2020-01-01

    Juhwan Noh, Geun Ho Gu, Sungwon Kim, Yousung Jung

    Read original paper ↗ ·

  19. Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods

    Computational Materials Science · 2020-01-16

    Nilesh Varadan Orupattur, Samir H. Mushrif, Vinay Prasad

    Read original paper ↗ ·

  20. Automated in Silico Design of Homogeneous Catalysts

    ACS Catalysis · 2020-01-17

    Marco Foscato, Vidar Remi Jensen

    Read original paper ↗ ·

  21. A Critical Review of Machine Learning of Energy Materials

    Advanced Energy Materials · 2020-01-29

    Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong

    Read original paper ↗ ·

  22. Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

    Advanced Energy Materials · 2020-02-12

    Mingzi Sun, Alan William Dougherty, Bolong Huang, Yuliang Li, Chun‐Hua Yan

    Read original paper ↗ ·

  23. Predicting the state of charge and health of batteries using data-driven machine learning

    Nature Machine Intelligence · 2020-03-02

    Man‐Fai Ng, Jin Cai Zhao, Qingyu Yan, G. J. Conduit, Zhi Wei Seh

    Read original paper ↗ ·

  24. Methods for comparing uncertainty quantifications for material property predictions

    Machine Learning Science and Technology · 2020-03-09

    Kevin Tran, Willie Neiswanger, Junwoong Yoon, Qingyang Zhang, Eric P. Xing, Zachary W. Ulissi

    Read original paper ↗ ·

  25. Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation

    Advanced Materials · 2020-03-20

    Geun Ho Gu, Changhyeok Choi, Yeunhee Lee, Andres Bethavan Situmorang, Juhwan Noh, Yong‐Hyun Kim, Yousung Jung

    Read original paper ↗ ·

  26. Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals

    Journal of Chemical Information and Modeling · 2020-03-25

    Juhwan Noh, Geun Ho Gu, Sungwon Kim, Yousung Jung

    Read original paper ↗ ·

  27. Energy and AI

    Energy and AI · 2020-04-20

    Donghan Jin, Raffaella Ocone, Kui Jiao, Jin Xuan

    Read original paper ↗ ·

  28. Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

    Chemistry of Materials · 2020-05-19

    Anthony Wang, Ryan Murdock, Steven K. Kauwe, Anton O. Oliynyk, Aleksander Gurlo, Jakoah Brgoch, Kristin Aslaug Persson, Taylor D. Sparks

    Read original paper ↗ ·

  29. Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

    Journal of Energy Chemistry · 2020-05-27

    Yongqiang Kang, Lejing Li, Baohua Li

    Read original paper ↗ ·

  30. Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology

    Energy and AI · 2020-06-29

    Yun Wang, Bongjin Seo, Bowen Wang, Nada Zamel, Kui Jiao, Xavier Cordobes Adroher

    Read original paper ↗ ·

  31. Machine learning assisted materials design and discovery for rechargeable batteries

    Energy storage materials · 2020-07-02

    Yue Liu, Biru Guo, Xinxin Zou, Yajie Li, Siqi Shi

    Read original paper ↗ ·

  32. Improving Smart Cities Safety Using Sound Events Detection Based on Deep Neural Network Algorithms

    Informatics · 2020-07-20

    Giuseppe Ciaburro, Gino Iannace

    Read original paper ↗ ·

  33. Modelling and understanding battery materials with machine-learning-driven atomistic simulations

    Journal of Physics Energy · 2020-08-17

    Volker L. Deringer

    Read original paper ↗ ·

  34. Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combination with data science

    Polymer Journal · 2020-08-28

    Akinori Saeki

    Read original paper ↗ ·

  35. Predicting Inorganic Photovoltaic Materials with Efficiencies >26% via Structure-Relevant Machine Learning and Density Functional Calculations

    Cell Reports Physical Science · 2020-09-01

    Hong‐Jian Feng, Kan Wu, Zun‐Yi Deng

    Read original paper ↗ ·

  36. The Cascade Reactions of Indigo with Propargyl Substrates for Heterocyclic and Photophysical Diversity

    Chemistry - A European Journal · 2020-09-04

    Patrick M. McCosker, Nicholas M. Butler, Alireza Shakoori, Michel Volland, Matthew J. Perry, Jesse W. Mullen, Anthony C. Willis, Timothy Clark, John B. Bremner, Dirk Michael Guldi, Paul Anthony Keller

    Read original paper ↗ ·

  37. Machine learning for halide perovskite materials

    Nano Energy · 2020-09-12

    Lei Zhang, Mu He, Shaofeng Shao

    Read original paper ↗ ·

  38. Modeling the Dielectric Constant of Silicon-Based Nanocomposites Using Machine Learning

    International Conference on Actual Problems of Electron Devices Engineering · 2020-09-24

    S.A. Korchagin, Yuri Vasilievich Klinaev, Д. В. Терин, Sergey Petrovich Romanchuk

    Read original paper ↗ ·

  39. Critical review of machine learning applications in perovskite solar research

    Nano Energy · 2020-10-28

    Beyza Yılmaz, Ramazan Yıldırım

    Read original paper ↗ ·

  40. Deep eutectic solvents (DESs): A short overview of the thermophysical properties and current use as base fluid for heat transfer nanofluids

    Journal of Molecular Liquids · 2020-11-11

    Kimia Jafari, Mohammad Hossein Fatemi, Patrice Estellé

    Read original paper ↗ ·

  41. Importance of Equilibration Method and Sampling for Ab Initio Molecular Dynamics Simulations of Solvent–Lithium-Salt Systems in Lithium-Oxygen Batteries

    Journal of Chemical Theory and Computation · 2020-11-12

    Emily J. Crabb, Arthur France‐Lanord, Graham Leverick, Ryan Stephens, Yang Shao‐Horn, Jeffrey C. Grossman

    Read original paper ↗ ·

  42. Advanced mono‐ and multi‐dimensional gas chromatography–mass spectrometry techniques for oxygen‐containing compound characterization in biomass and biofuel samples

    Journal of Separation Science · 2020-11-13

    Marco Beccaria, Anna Luiza Mendes Siqueira, Adrien Maniquet, Pierre Giusti, Marco Piparo, Pierre‐Hugues Stefanuto, Jean‐François Focant

    Read original paper ↗ ·

  43. “More is Different:” Synergistic Effect and Structural Engineering in Double‐Atom Catalysts

    Advanced Functional Materials · 2020-11-16

    Yiran Ying, Xin Luo, Jinli Qiao, Haitao Huang

    Read original paper ↗ ·

  44. Machine learning for high performance organic solar cells: current scenario and future prospects

    Energy & Environmental Science · 2020-11-25

    Asif Mahmood, Jin‐Liang Wang

    Read original paper ↗ ·

  45. Interfacial engineering of Cu2Se/Co3Se4 multivalent hetero-nanocrystals for energy-efficient electrocatalytic co-generation of value-added chemicals and hydrogen

    Applied Catalysis B: Environmental · 2020-12-10

    Bin Zhao, Jianwen Liu, Chenyu Xu, Renfei Feng, Peng‐Fei Sui, Junxuan Luo, Lei Wang, Jiujun Zhang, Jing‐Li Luo, Xian‐Zhu Fu

    Read original paper ↗ ·

  46. Coronene derivatives for transparent organic photovoltaics through inverse materials design

    Journal of Materials Chemistry C · 2020-12-11

    Jeni C. Sorli, Pascal Friederich, Benjamín Sánchez-Lengeling, Nicholas C. Davy, Guy O. Ngongang Ndjawa, Hannah L. Smith, Xin Ping Lin, Steven A. Lopez, Melissa L. Ball, Antoine Kahn, Alán Aspuru‐Guzik, Yueh‐Lin Loo

    Read original paper ↗ ·

  47. The Hubbard-U correction and optical properties of d metal oxide photocatalysts

    The Journal of Chemical Physics · 2020-12-11

    Joshua J. Brown, Alister J. Page

    Read original paper ↗ ·

  48. Machine learning toward advanced energy storage devices and systems

    iScience · 2020-12-13

    Tianhan Gao, Wei D. Lu

    Read original paper ↗ ·

  49. Artificial intelligence and machine learning in design of mechanical materials

    Materials Horizons · 2020-12-17

    Kai Guo, Zhenze Yang, Chi‐Hua Yu, Markus J. Buehler

    Read original paper ↗ ·

  50. A data-driven and DFT assisted theoretic guide for membrane design in flow batteries

    Journal of Materials Chemistry A · 2021-01-01

    Tianyu Li, Wenjing Lu, Zhizhang Yuan, Huamin Zhang, Xianfeng Li

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.