BetaCitation Atlas

RESEARCH PAPER · Journal of the American Chemical Society · 2020

The Role of Machine Learning in the Understanding and Design of Materials

Seyed Mohamad Moosavi, Kevin Maik Jablonka, Berend Smit

Read original paper ↗

See where this research leads.

433 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

The Role of Machine Learning in the Understanding and Design of MaterialsOriginalA data-driven perspective on the colours of metal–organic frameworks29Bayesian optimization of nanoporous materials112Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review306Techno-economic analysis of metal–organic frameworks for adsorption heat pumps/chillers: from directional computational screening, machine learning to experiment48The application of machine learning for predicting the methane uptake and working capacity of MOFs31Machine learning for advanced energy materials208Comparison between conventional solvothermal and aqueous solution-based production of UiO-66-NH2: Life cycle assessment, techno-economic assessment, and implications for CO2 capture and storage115Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties49Training Data Set Refinement for the Machine Learning Potential of Li-Si Alloys via Structural Similarity Analysis1Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks0A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Toward smart carbon capture with machine learning121Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction164Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning57Predicting inorganic dimensionality in templated metal oxides8The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale56Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures32Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search64Unsupervised discovery of thin-film photovoltaic materials from unlabeled data37Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning10Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials25Modeling and optimization of the manufacturing parameters of a hybrid fiber reinforced polymer composite PxGyEz37RETRACTED: Role of machine learning in changing social and business eco-system – a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic25Diversifying databases of metal organic frameworks for high-throughput computational screening0Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units**12Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks20Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering1Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening141Accelerated HKUST-1 Thin-Film Property Optimization Using Active Learning15Thermal Management for Hydrogen Charging and Discharging in a Screened Metal–Organic Framework Particle Tank35Artificial intelligence: machine learning for chemical sciences104Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Data-Driven Methods for Accelerating Polymer Design123Progress in Materials Data Availability and Application: A review1Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling15Knowledge extraction in catalysis utilizing design of experiments and machine learning29Accelerated discovery of defect tolerant organo-halide perovskites1Data mining for predicting gas diffusivity in zeolitic-imidazolate frameworks (ZIFs)26Data-driven design of dual-metal-site catalysts for the electrochemical carbon dioxide reduction reaction35Data-driven efficient synthetic exploration of anionic lanthanide-based metal–organic frameworks6High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications110Implementation of Artificial Neural Networks in the assessment of CO2 solubility in deep eutectic and ionic liquid solvents – Performance and cost comparison8Machine Learning-Assisted Three-Dimensional Sensing Array for Simultaneous Discrimination and Quantification of Hypochlorite and Hydroxyl Radicals0Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond32Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Recent progress in the data-driven discovery of novel photovoltaic materials44The Problem of Scientific Regret in AI-Driven Materials Selection0FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches31Prediction of steady flows passing fixed cylinders using deep learning19Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations41Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network102The materials tetrahedron has a “digital twin”34Generalizability and limitations of machine learning for yield prediction of oxidative coupling of methane6Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?21Inverse Design of Materials by Machine Learning137Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random forest regression model112Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction91Estimating Phosphorescent Emission Energies in IrIII Complexes Using Large‐Scale Quantum Computing Simulations**3Autonomous high-throughput computations in catalysis29Estimating Phosphorescent Emission Energies in Ir III Complexes Using Large‐Scale Quantum Computing Simulations**15Machine Learning–Assisted Design of Material Properties80Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies21Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin464Emerging Strategies for CO 2 Photoreduction to CH 4 : From Experimental to Data‐Driven Design151Machine learning in energy storage materials106Predicting Experimental Formability of Hybrid Organic–Inorganic Perovskites via Imbalanced Learning25A Comprehensive Study on the Role of Machine Learning in Hybrid Biometric Recognition2MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70Supervised learning for accurate mesoscale simulations of suspension flow in wall-bounded geometries4Deeper learning in electrocatalysis: realizing opportunities and addressing challenges13High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration190Machine-Learning-Assisted Catalytic Performance Predictions of Single-Atom Alloys for Acetylene Semihydrogenation30Modeling of toughening effect in rigid particulate-filled polymer composites by artificial intelligence: a review4Inverse Design of Hybrid Organic–Inorganic Perovskites with Suitable Bandgaps via Proactive Searching Progress41Transfer and Active Learning of High Dimensional Neural NetworkPotentials for Transition Metal Clusters and Bulk0CORDATA: an open data management web application to select corrosion inhibitors22Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks75Exploring the Essentials Elements of Data Analysis and Its Impact on Performance Management Framework0Transfer and Active Learning of High Dimensional Neural Network Potentials for Transition Metal Clusters and Bulk0Machine-Learning-Assisted Acceleration on High-Symmetry Materials Search: Space Group Predictions from Band Structures6Thermodynamics-Kinetics-Balanced Metal–Organic Framework for In-Depth Radon Removal under Ambient Conditions45Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data27Revealing the non-covalent interactions between oxygen-containing demulsifiers and interfacially active asphaltenes: A multi-level computational simulation51Deep learning-based quasi-continuum theory for structure of confined fluids4Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies7A Review of Performance Prediction Based on Machine Learning in Materials Science61An ecosystem for digital reticular chemistry4Computation-informed optimization of Ni(PyC)2 functionalization for noble gas separations12CRIPT: A Scalable Polymer Material Data Structure6Finding the optimal CO2 adsorption material: Prediction of multi-properties of metal-organic frameworks (MOFs) based on DeepFM27Artificial Intelligence Algorithm for Flexible Sensors0Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling9Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures4Crystal twins: self-supervised learning for crystalline material property prediction72Using Machine Learning to make nanomaterials sustainable57Multiscale Computational Approaches toward the Understanding of Materials24DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space26Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality24Recent Progress on Anti-Humidity Strategies of Chemiresistive Gas Sensors180Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF31Multi-Scale Computer-Aided Design of Covalent Organic Frameworks for CO 2 Capture in Wet Flue Gas24On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran14Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios25514 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon214A Boosted Machine Learning Framework for the Improvement of Phase and Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and Configurational Parameters0Bayesian optimization of the composition of the lanthanide metal–organic framework MIL-103 for white-light emission11Evolutionary design of machine-learning-predicted bulk metallic glasses14Machine learning of atomic force microscopy images of organic solar cells20Multi-Model and Hybrid AI Systems in Materials Research — Architectures and Trade-Offs0On the use of real-world datasets for reaction yield prediction141Reticular materials for wastewater treatment22System Energy and Band Gap Prediction of Titanium Dioxide Based on Machine Learning0System Energy and Band Gap Prediction of Titanium Dioxide Based on Machine Learning0A Route Map of Machine Learning Approaches in Heterogeneous CO2 Reduction Reaction27Deep neural network for microstructured polymer fiber modeling5De Novo Design of Organic Structure Directing Agents for the Synthesis of Zeolites5Overview of AI in the Understanding and Design of Nanoporous Materials2Visual Analytics for Explainable and Trustworthy Machine Learning0Unveiling Hidden Zeolitic Imidazolate Frameworks Guided by Intuition‐Based Geometrical Factors15Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure67Understanding ligand-protected noble metal nanoclusters at work356Machine learning in gas separation membrane developing: Ready for prime time72“Lubrication Brain” ― A machine learning framework of lubrication oil molecule design22Saccharide concentration prediction from proxy-sea surface microlayer samples analyzed via ATR-FTIR spectroscopy and quantitative machine learning3Impacts of catalyst and process parameters on Ni-catalyzed methane dry reforming via interpretable machine learning58An Ecosystem for Digital Reticular Chemistry52Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Carbon dioxide separation and capture by adsorption: a review275Target‐Driven Design of Deep‐UV Nonlinear Optical Materials via Interpretable Machine Learning125Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures73TEXplorer.org: Thermoelectric material properties data platform for experimental and first-principles calculation results16Efficient discovery of room temperature magnetic transition metal monolayers assisted by artificial neural network7Generative Models as an Emerging Paradigm in the Chemical Sciences313Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87A Smart Gas Sensor Using Machine Learning Algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review39Machine learning for perovskite solar cell design58Machine Learning Descriptors for Data‐Driven Catalysis Study154Near-Infrared Spectroscopy and Machine Learning for Accurate Dating of Historical Books22Prediction and interpretation of photocatalytic NO removal on g-C3N4-based catalysts using machine learning29High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption26Inverse design and AI/Deep generative networks in food design: A comprehensive review59Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33

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

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

  1. A data-driven perspective on the colours of metal–organic frameworks

    Chemical Science · 2020-12-27

    Kevin Maik Jablonka, Seyed Mohamad Moosavi, Mehrdad Asgari, Christopher P. Ireland, Luc Patiny, Berend Smit

    Read original paper ↗ ·

  2. Bayesian optimization of nanoporous materials

    Molecular Systems Design & Engineering · 2021-01-01

    Aryan Deshwal, Cory M. Simon, Janardhan Rao Doppa

    Read original paper ↗ ·

  3. Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

    Energy & Environmental Science · 2021-01-01

    Yongliang Yan, Yongliang Yan, Tohid Nejad Ghaffar Borhani, Sai Gokul Subraveti, Kasturi Nagesh Pai, Vinay Prasad, Arvind Rajendran, Paula Nkulikiyinka, Jude Odianosen Asibor, Zhien Zhang, Ding Shao, Lijuan Wang, Wenbiao Zhang, Yong Gang Yan, Yong Gang Yan, William Ampomah, Junyu You, Meihong Wang, Edward John Anthony, Vasilije M. Manovic, Peter T. Clough

    Read original paper ↗ ·

  4. Techno-economic analysis of metal–organic frameworks for adsorption heat pumps/chillers: from directional computational screening, machine learning to experiment

    Journal of Materials Chemistry A · 2021-01-01

    Zenan Shi, Xueying Yuan, Yaling Yan, Yuanlin Tang, Junjie Li, Hong Liang, Lianpeng Tong, Zhiwei Qiao

    Read original paper ↗ ·

  5. The application of machine learning for predicting the methane uptake and working capacity of MOFs

    Faraday Discussions · 2021-01-01

    Mikhail V. Suyetin

    Read original paper ↗ ·

  6. Machine learning for advanced energy materials

    Energy and AI · 2021-01-24

    Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An

    Read original paper ↗ ·

  7. Comparison between conventional solvothermal and aqueous solution-based production of UiO-66-NH2: Life cycle assessment, techno-economic assessment, and implications for CO2 capture and storage

    Journal of environmental chemical engineering · 2021-02-05

    Hongxi Luo, Fangwei Cheng, Luke D. Huelsenbeck, Natalie Smith

    Read original paper ↗ ·

  8. Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties

    The Journal of Physical Chemistry Letters · 2021-03-01

    Eun Hyun Cho, Li‐Chiang Lin

    Read original paper ↗ ·

  9. Training Data Set Refinement for the Machine Learning Potential of Li-Si Alloys via Structural Similarity Analysis

    arXiv (Cornell University) · 2021-03-07

    Nan Xu, Chen W. Li, Mandi Fang, Qing Shao, Yingying Lü, Yao Shi, Yi He

    Read original paper ↗ ·

  10. Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

    ChemRxiv · 2021-03-09

    Yu Kitamura, Emi Terado, Zechen Zhang, Hirofumi Yoshikawa, Tomoko Inose, Hiroshi Uji‐i, Masaharu Tanimizu, Akihiro Inokuchi, Yoshinobu Kamakura, Daisuke Tanaka

    Read original paper ↗ ·

  11. A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    ChemRxiv · 2021-03-16

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

    Read original paper ↗ ·

  12. Toward smart carbon capture with machine learning

    Cell Reports Physical Science · 2021-04-01

    Mohammad Rahimi, Seyed Mohamad Moosavi, Berend Smit, T. Alan Hatton

    Read original paper ↗ ·

  13. Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction

    Journal of the American Chemical Society · 2021-04-12

    Ying Guo, Xinru He, Yuming Su, Yiheng Dai, Mingcan Xie, Shuangli Yang, Jiawei Chen, Kun Wang, Da Zhou, Cheng Wang

    Read original paper ↗ ·

  14. Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning

    ACS Macro Letters · 2021-04-27

    Shuochen Zhao, Tianyun Cai, Liangshun Zhang, Weihua Li, Jiaping Lin

    Read original paper ↗ ·

  15. Predicting inorganic dimensionality in templated metal oxides

    The Journal of Chemical Physics · 2021-05-12

    Qianxiang Ai, Davion Marquise Williams, Matthew Danielson, Liam G. Spooner, Joshua A. Engler, Zihui Ding, Mat­thias Zeller, Alexander J. Norquist, Joshua Schrier

    Read original paper ↗ ·

  16. The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning

    Cell Reports Physical Science · 2021-06-30

    Hang Yin, Zhehao Sun, Zhuo Wang, Dawei Tang, Cheng Heng Pang, Xue‐Feng Yu, Amanda Susan Barnard, Haitao Zhao, Zongyou Yin

    Read original paper ↗ ·

  17. Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

    Angewandte Chemie International Edition · 2021-07-01

    Xiang Chen, Xinyan Liu, Xin Pu Shen, Qiang Zhang

    Read original paper ↗ ·

  18. Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

    Angewandte Chemie · 2021-07-01

    Xiang Chen, Xinyan Liu, Xin Pu Shen, Qiang Zhang

    Read original paper ↗ ·

  19. Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

    The Journal of Chemical Physics · 2021-07-01

    Zhao Li, Benjamin J. Bucior, Haoyuan Chen, Maciej Harańczyk, Joern Ilja Siepmann, Randall Q. Snurr

    Read original paper ↗ ·

  20. Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search

    Journal of Chemical Information and Modeling · 2021-08-04

    Michael Tynes, Wenhao Gao, Daniel J. Burrill, Enrique R. Batista, Danny Pérez, Ping Xiong Yang, Nicholas Lubbers

    Read original paper ↗ ·

  21. Unsupervised discovery of thin-film photovoltaic materials from unlabeled data

    npj Computational Materials · 2021-08-12

    Zhilong Wang, Junfei Cai, Qingxun Wang, Sicheng Wu, Jinjin Li

    Read original paper ↗ ·

  22. Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning

    International Journal of Molecular Sciences · 2021-08-23

    Shi-Ping Peng, Xinyu Yang, Yi Zhao

    Read original paper ↗ ·

  23. Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    Chemistry of Materials · 2021-09-07

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

    Read original paper ↗ ·

  24. Modeling and optimization of the manufacturing parameters of a hybrid fiber reinforced polymer composite PxGyEz

    The International Journal of Advanced Manufacturing Technology · 2021-09-17

    Bassey Okon Samuel, Malachy Sumaila, Bashar Dan-asabe

    Read original paper ↗ ·

  25. RETRACTED: Role of machine learning in changing social and business eco-system – a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic

    World Journal of Engineering · 2021-09-25

    Sanil S. Hishan, D. R. Singh, K. Bhavana Raj, Somya Choubey, Narinder Kumar Bhasin, Ranjeeta Yadav, Kamal Gulati

    Read original paper ↗ ·

  26. Diversifying databases of metal organic frameworks for high-throughput computational screening

    Unknown journal · 2021-09-28

    Sauradeep Majumdar, Seyed Mohamad Moosavi, Kevin Maik Jablonka, Daniele Ongari, Berend Smit

    Read original paper ↗ ·

  27. Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control

    Journal of the American Chemical Society · 2021-10-04

    Manzhang Xu, Bijun Tang, Yuhao Lu, Chao Fu Zhu, Qianbo Lu, Chao Fu Zhu, Lu Zheng, Jingyu Zhang, Nannan Han, Weidong Fang, Yuxi Guo, Jun Di, Pin Song, Yongmin He, Lixing Kang, Zhiyong Zhang, Wu Yun Zhao, Cuntai Guan, Xuewen Wang, Zheng Liu

    Read original paper ↗ ·

  28. Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units**

    Chemistry - A European Journal · 2021-10-09

    Yu Kitamura, Emi Terado, Zechen Zhang, Hirofumi Yoshikawa, Tomoko Inose, Hiroshi Uji‐i, Masaharu Tanimizu, Akihiro Inokuchi, Yoshinobu Kamakura, Daisuke Tanaka

    Read original paper ↗ ·

  29. Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

    Advanced Materials · 2021-10-12

    Watcharop Chaikittisilp, Yusuke Yamauchi, Katsuhiko Ariga

    Read original paper ↗ ·

  30. Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    Unknown journal · 2021-10-26

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

    Read original paper ↗ ·

  31. Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks

    Chemistry of Materials · 2021-10-27

    Thomas C. Nicholas, Eugeny V. Alexandrov, Vladislav A. Blatov, Alexander P. Shevchenko, Davide Maria Proserpio, Andrew L. Goodwin, Volker L. Deringer

    Read original paper ↗ ·

  32. Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering

    arXiv (Cornell University) · 2021-11-15

    Utkarsh Saha, Koyendrila Debnath, Soumitra Satapathi

    Read original paper ↗ ·

  33. Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

    ACS Applied Materials & Interfaces · 2021-12-15

    Sauradeep Majumdar, Seyed Mohamad Moosavi, Kevin Maik Jablonka, Daniele Ongari, Berend Smit

    Read original paper ↗ ·

  34. Accelerated HKUST-1 Thin-Film Property Optimization Using Active Learning

    ACS Applied Materials & Interfaces · 2021-12-16

    Luke D. Huelsenbeck, Sangeun Jung, Roberto Herrera del Valle, Prasanna V. Balachandran, Gaurav Giri

    Read original paper ↗ ·

  35. Thermal Management for Hydrogen Charging and Discharging in a Screened Metal–Organic Framework Particle Tank

    ACS Applied Materials & Interfaces · 2021-12-17

    Hui Wang, Zhiguo Qu, Ying Yin, Jianfei Zhang, Pingwen Ming

    Read original paper ↗ ·

  36. Artificial intelligence: machine learning for chemical sciences

    Journal of Chemical Sciences · 2021-12-21

    Akshaya Karthikeyan, U. Deva Priyakumar

    Read original paper ↗ ·

  37. Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks

    The Journal of Physical Chemistry Letters · 2021-12-23

    Yuta Miyake, Akinori Saeki

    Read original paper ↗ ·

  38. Data-Driven Methods for Accelerating Polymer Design

    ACS Polymers Au · 2021-12-28

    Tarak K. Patra

    Read original paper ↗ ·

  39. Progress in Materials Data Availability and Application: A review

    IEEE Signal Processing Magazine · 2021-12-28

    Chandler A. Becker, James A. Warren

    Read original paper ↗ ·

  40. Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling

    Analytical Chemistry · 2021-12-29

    Christopher W. Smith, Mustafa Salih Hizir, Nidhi Nandu, Mehmet Veysel Yigit

    Read original paper ↗ ·

  41. Knowledge extraction in catalysis utilizing design of experiments and machine learning

    Current Opinion in Chemical Engineering · 2021-12-29

    Blake MacQueen, Rasika Jayarathna, Jochen A. Lauterbach

    Read original paper ↗ ·

  42. Accelerated discovery of defect tolerant organo-halide perovskites

    Journal of Materials Chemistry C · 2022-01-01

    N. Işleyen, Ayşenur Corcor, S. Çakirefe, N. Ormanli, E. N. Kanat, İlhan Yavuz

    Read original paper ↗ ·

  43. Data mining for predicting gas diffusivity in zeolitic-imidazolate frameworks (ZIFs)

    Journal of Materials Chemistry A · 2022-01-01

    Panagiotis Krokidas, Stelios Karozis, Salvador Moncho, George Giannakopoulos, Edward N. Brothers, M.E. Kainourgiakis, Ioannis G. Economou, Theodore Steriotis

    Read original paper ↗ ·

  44. Data-driven design of dual-metal-site catalysts for the electrochemical carbon dioxide reduction reaction

    Journal of Materials Chemistry A · 2022-01-01

    Haisong Feng, Hu Ding, Peinan He, Si Wang, Zeyang Li, Zikang Zheng, Yusen Yang, Min Wei, Xin Zhang

    Read original paper ↗ ·

  45. Data-driven efficient synthetic exploration of anionic lanthanide-based metal–organic frameworks

    Chemical Communications · 2022-01-01

    Yu Kitamura, Yuiga Nakamura, Kunihisa Sugimoto, Hirofumi Yoshikawa, Daisuke Tanaka

    Read original paper ↗ ·

  46. High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications

    Energy & Environmental Science · 2022-01-01

    Xijun Wang, Yunfei Gao, Emily Krzystowczyk, Sherafghan Iftikhar, Jian Dou, Runxia Cai, Haiying Wang, Chongyan Ruan, Sheng Ye, Fanxing Li

    Read original paper ↗ ·

  47. Implementation of Artificial Neural Networks in the assessment of CO2 solubility in deep eutectic and ionic liquid solvents – Performance and cost comparison

    Sustainable Chemistry for Climate Action · 2022-01-01

    Avikal Sagar, Sreedevi Upadhyayula

    Read original paper ↗ ·

  48. Machine Learning-Assisted Three-Dimensional Sensing Array for Simultaneous Discrimination and Quantification of Hypochlorite and Hydroxyl Radicals

    SSRN Electronic Journal · 2022-01-01

    Long Yu, Xin Li, Pengchen Su, Mingtai Sun, Chao Yuan, Suhua Wang

    Read original paper ↗ ·

  49. Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond

    Physical Chemistry Chemical Physics · 2022-01-01

    Dennis Russell Salahub

    Read original paper ↗ ·

  50. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

    Catalysis Science & Technology · 2022-01-01

    Annette Trunschke

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