BetaCitation Atlas

RESEARCH PAPER · Nanoscale Advances · 2020

Machine learning-driven new material discovery

Jiazhen Cai, Xuan Chu, Kun Xu, Hongbo Li, Jing Wei

Read original paper ↗

See where this research leads.

295 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-driven new material discoveryOriginalDEFORMATION MANIFOLD LEARNING MODEL FOR MULTI WALLED CARBON NANOTUBES0Smart Cellulose Composites: Advanced Applications and Properties Prediction Using Machine Learning6Machine learning for advanced energy materials208A complete review on biochar: Production, property, multifaceted applications, interaction mechanism and computational approach197Perspective—Accelerated Discovery of Organic-Inorganic Hybrid Materials via Machine Learning1Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning13Rapid discovery of narrow bandgap oxide double perovskites using machine learning59Machine Learning Analysis of Ni/SiC Electrodeposition Using Association Rule Mining and Artificial Neural Network12The search for new materials and the role of novel processing routes24Membrane Science and membrane Engineering for a sustainable industrial development48Machine learning and evolutionary prediction of superhard B-C-N compounds68Structural Analysis of Nanoscale Network Materials Using Graph Theory75Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies65Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars95Recent machine learning guided material research - A review12Superconducting ternary hydrides under high pressure64A 3D orthogonal vision-based band-gap prediction using deep learning: A proof of concept19Defects and passivation in perovskite solar cells33Artificial intelligence: machine learning for chemical sciences104Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning7Mapping between Density of States and Energy Band Gap for Bismuth-Based Semiconductors Via Machine Learning Prediction6Metal-related electrocatalysts for Li–CO 2 batteries: an overview of the fundamentals to explore future-oriented strategies39Recent major advances and challenges in the emerging graphene-based nanomaterials in electrocatalytic fuel cell technology27Scientific Validation in Materials AI—A Critical Survey of Conceptual Approaches: A Review Study0Understanding, discovery, and synthesis of 2D materials enabled by machine learning171An Easy, Simple, and Accessible Web-based Machine Learning Platform, SimPL-ML2Realizing Quantum Technologies in Nanomaterials and Nanoscience25Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations14Agents for sequential learning using multiple-fidelity data38Large-scale synthesis of graphene and other 2D materials towards industrialization396Data‐Driven Materials Innovation and Applications187Review on 2D Molybdenum Diselenide (MoSe 2 ) and Its Hybrids for Green Hydrogen (H 2 ) Generation Applications97Data-Driven and Multiscale Modeling of DNA-Templated Dye Aggregates9Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength36Machine Learning and First-Principles Discovery of Ternary Superhard Materials3Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model11Research on the Selection of Raw Material Suppliers for Enterprises0Toward a universal metasurface for optical imaging, communication, and computation59Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength9Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives90Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water37A Strategic Approach to Machine Learning for Material Science: How to Tackle Real-World Challenges and Avoid Pitfalls71QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization10On the value of popular crystallographic databases for machine learning prediction of space groups14MetaNOR: A meta-learnt nonlocal operator regression approach for metamaterial modeling6A machine learning approach to predict the structural and magnetic properties of Heusler alloy families25Materials discovery of ion-selective membranes using artificial intelligence30Reduced-Order Damage Assessment Model for Dual-Phase Steels5Building a better machine learning model of extubation for neurocritical care patients3Accelerated Design of Flame Retardant Polymeric Nanocomposites via Machine Learning Prediction45Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics141Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback60TransG-net: transformer and graph neural network based multi-modal data fusion network for molecular properties prediction14Machine learning‐based model for predicting the material properties of nanostructured aerogels32Toward Deep Generation of Guided Wave Representations for Composite Materials7Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning45Optimizing density-functional simulations for two-dimensional metals9Accelerating materials discovery using integrated deep machine learning approaches31Inkjet printing of novel materials for solar water splitting0Machine learning in the evaluation and prediction models of biochar application: A review55Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron( ii ) chloride-heavy water electrochromic liquid11Multi-Model and Hybrid AI Systems in Materials Research — Architectures and Trade-Offs0Scientific Decision-Making with Materials AI — How Models Actually Influence Action: A Review Study1Self-Supervised Representation Learning of Microstructures from Electron Microscopy for Property Prediction0The Application of Novel Functional Materials to Machine Learning0Analysis and evaluation of machine learning applications in materials design and discovery33Advanced ocean wave energy harvesting: current progress and future trends54Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses14Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems112Quantification of similarity and physical awareness of microstructures generated via generative models18High‐Throughput Scanning Second‐Harmonic‐Generation Microscopy for Polar Materials27Phase prediction and experimental realisation of a new high entropy alloy using machine learning85Small data machine learning in materials science866Magnetic iron-cobalt silicides discovered using machine-learning17Accelerated prediction of perovskite material properties with classical machine learning and graph neural network6Efficient discovery of room temperature magnetic transition metal monolayers assisted by artificial neural network7Impact of membrane engineering on the process engineering progresses: Towards a sustainable development10Autonomous x-ray scattering26Predicting mechanical properties of newly generated two-dimensional materials with minimum uncertainty7Catalysis distillation neural network for the few-shot open catalyst challenge0Methods, progresses, and opportunities of materials informatics72AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122Catalysis distillation neural network for the few-shot open catalyst challenge1Temperature effects and a prediction method of field-dependent yield stress in graphite magnetorheological grease7Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells40Reinforced active learning for CVD-grown two-dimensional materials characterization3Virtual sample generation in machine learning assisted materials design and discovery20AI powered, automated discovery of polymer membranes for carbon capture48Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods22Comprehensive Machine Learning Pipeline for Prediction of Power Conversion Efficiency in Perovskite Solar Cells6The Impact of Digitalized Data Management on Materials Systems Workflows5Application of Machine Learning in Material Synthesis and Property Prediction166PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations64TRACING THE EVOLUTION OF AI AND MACHINE LEARNING APPLICATIONS IN ADVANCING MATERIALS DISCOVERY AND PRODUCTION PROCESSES53Extensive Review on the Role of Machine Learning for Multifactorial Genetic Disorders Prediction49A versatile strategy for hybridizing small experimental and large simulation data: A case for ceramic tape-casting process5Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles27The emergent role of explainable artificial intelligence in the materials sciences32Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance19Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207Mechanical properties prediction of ferrite ductile iron via machine learning9On the use of Machine Learning modeling tools in chemical process and product engineering0Accelerating search for the polar phase stability of ferroelectric oxide by machine learning4Scope of machine learning in materials research—A review230Machine learning driven prediction of lattice constants in transition metal dichalcogenides1Artificial Intelligence Agents for Materials Sciences19Machine learning for expediting next-generation of fire-retardant polymer composites43A comprehensive review of covalent organic frameworks (COFs) and their derivatives in environmental pollution control327A Theory of Reproducibility for AI-Generated Materials Insights: What Must Be True for Claims to Hold0Conceptual Foundations of Applied AI in Materials Science - Definitions, Assumptions, and Open Debates0Machine learning approaches for screening of materials in flexible electronic devices0Multi-Modal Transformer Architectures for Correlating Processing Conditions to Functional Performance in Perovskite Solar Cells0Nanomaterials in robotics and artificial intelligence0Progress of machine learning in materials design for Li-Ion battery19Self-assembly prediction of architecture-controlled bottlebrush copolymers in solution using graph convolutional networks5The future of self-driving laboratories: from human in the loop interactive AI to gamification102What Does “Sparsity” Mean in High-Dimensional Materials Design? A Boundary Problem for Active Learning0AI methods in materials design, discovery and manufacturing: A review142Application of Machine Learning Tools to Material Science: A Mini-Review5Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances78Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery7Machine learning for polyphenol-based materials10Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models18Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology40Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning18AI energized hydrogel design, optimization and application in biomedicine194Artificial intelligence adoption in public organizations: a case study8Physics-informed shape optimization using coordinate projection9Polymer-Unit Graph: Advancing Interpretability in Graph Neural Network Machine Learning for Organic Polymer Semiconductor Materials29Optimizing the Synthesis Parameters of Double Perovskites with Machine Learning Using a Multioutput Regression Model10Decoding the Pair Distribution Function of Uranium in Molten Fluoride Salts from X-Ray Absorption Spectroscopy Data by Machine Learning12Strain sensing characteristics of 3D-printed carbon nanotubes/polypyrrole/UV-curable composites: experimental validation and machine learning predictions9Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective60Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures35Enhancing glass property predictions through ab initio‐derived descriptors7Critical Review and Analytical Energetic Assessment of Key-parameters for Industry 4.0 Development in Italy through a Cross-comparison with the EU Digitalization Scenarios1Prediction of lattice parameters of tetragonal oxyhalides AOX1Machine Learning for Perovskite Solar Cells: An Open‐Source Pipeline11Explorative prediction of novel superhard carbon allotropes with lager cell: Density functional theory-assisted deep learning10Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes29Polymers simulation using machine learning interatomic potentials19Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules17Ab initio stability to efficiency study of SrGeO3 perovskite9Model construction of corrosion resistance of alloying elements for low alloy steel in marine atmospheric corrosive environment based on machine learning8Optimizing the prediction of adsorption in metal–organic frameworks leveraging Q‐learning12Navigating the complexity of hybrid materials without structural dependency: PerovGNN as a map2Autonomous research and development of structural materials – An introduction and vision21Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets5

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

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

  1. DEFORMATION MANIFOLD LEARNING MODEL FOR MULTI WALLED CARBON NANOTUBES

    Unknown journal · 2021-01-01

    Shashank Pathrudkar

    Read original paper ↗ ·

  2. Smart Cellulose Composites: Advanced Applications and Properties Prediction Using Machine Learning

    Elsevier eBooks · 2021-01-01

    Jordina Torrents‐Barrena, Eva Pellicer

    Read original paper ↗ ·

  3. Machine learning for advanced energy materials

    Energy and AI · 2021-01-24

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

    Read original paper ↗ ·

  4. A complete review on biochar: Production, property, multifaceted applications, interaction mechanism and computational approach

    Fuel · 2021-02-15

    Kadarkaraithangam Jeyasubramanian, Baskaran Thangagiri, Arumugam Sakthivel, Jeyaraj Dhaveethu Raja, S. Seenivasan, P. Vallinayagam, D. Madhavan, S. Malathi Devi, Dr B Rathika

    Read original paper ↗ ·

  5. Perspective—Accelerated Discovery of Organic-Inorganic Hybrid Materials via Machine Learning

    ECS Journal of Solid State Science and Technology · 2021-02-24

    Sajjad Husain Mir

    Read original paper ↗ ·

  6. Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning

    Journal of Molecular Graphics and Modelling · 2021-03-13

    Kai Lin Woon, Zhao Xian Chong, Azhar Ariffin, Chee Seng Chan

    Read original paper ↗ ·

  7. Rapid discovery of narrow bandgap oxide double perovskites using machine learning

    Computational Materials Science · 2021-04-28

    Xue Yang, Long Li, Qiuling Tao, Wencong Lu, Minjie Li

    Read original paper ↗ ·

  8. Machine Learning Analysis of Ni/SiC Electrodeposition Using Association Rule Mining and Artificial Neural Network

    Journal of The Electrochemical Society · 2021-06-01

    Aysegul Kilic, Ramazan Yıldırım, Damla Eroğlu

    Read original paper ↗ ·

  9. The search for new materials and the role of novel processing routes

    Discover Materials · 2021-06-07

    Peter J. Wellmann

    Read original paper ↗ ·

  10. Membrane Science and membrane Engineering for a sustainable industrial development

    Separation and Purification Technology · 2021-06-27

    Enrico Drioli, Francesca Macedonio, Elena Tocci

    Read original paper ↗ ·

  11. Machine learning and evolutionary prediction of superhard B-C-N compounds

    npj Computational Materials · 2021-07-21

    Wei-Chih Chen, Joanna N. Schmidt, Da Yan, Yogesh K. Vohra, Cheng-Chien Chen

    Read original paper ↗ ·

  12. Structural Analysis of Nanoscale Network Materials Using Graph Theory

    ACS Nano · 2021-07-27

    Drew A. Vecchio, Samuel H. Mahler, Mark D. Hammig, Nicholas A. Kotov

    Read original paper ↗ ·

  13. Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies

    Journal of Chemical Information and Modeling · 2021-08-10

    Pengcheng Xu, Dongping Chang, Tian Lu, Long Li, Minjie Li, Wencong Lu

    Read original paper ↗ ·

  14. Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

    Processes · 2021-08-20

    Cindy Trinh, Dimitrios Meimaroglou, Sandrine Hoppe

    Read original paper ↗ ·

  15. Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars

    Chemosphere · 2021-09-08

    Xuan Cuong Nguyen, Quang Viet Ly, Thi Thanh Huyen Nguyen, Hien Thi Thu Ngo, Yunxia Hu, Zhenghua Zhang

    Read original paper ↗ ·

  16. Recent machine learning guided material research - A review

    Computational Condensed Matter · 2021-09-20

    Mohammad Asaduzzaman Chowdhury, Nayem Hossain, Md. Bengir Ahmed Shuvho, Mohamad Fotouhi, Md Sakibul Islam, Md. Ramjan Ali, Mohammod Abul Kashem

    Read original paper ↗ ·

  17. Superconducting ternary hydrides under high pressure

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2021-10-19

    Xiaohua Zhang, Yaping Zhao, Guochun Yang

    Read original paper ↗ ·

  18. A 3D orthogonal vision-based band-gap prediction using deep learning: A proof of concept

    Computational Materials Science · 2021-10-21

    Ricardo Espinosa, Hiram Ponce-Espinosa, Josué Ortiz‐Medina

    Read original paper ↗ ·

  19. Defects and passivation in perovskite solar cells

    Surface Innovations · 2021-12-08

    Yaobo Li, Zhaohan Li, Fangze Liu, Jing Wei

    Read original paper ↗ ·

  20. Artificial intelligence: machine learning for chemical sciences

    Journal of Chemical Sciences · 2021-12-21

    Akshaya Karthikeyan, U. Deva Priyakumar

    Read original paper ↗ ·

  21. Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning

    Physical Chemistry Chemical Physics · 2022-01-01

    Shweta Jindal, Po‐Jen Hsu, Huu Trong Phan, Pei‐Kang Tsou, Jer‐Lai Kuo

    Read original paper ↗ ·

  22. Mapping between Density of States and Energy Band Gap for Bismuth-Based Semiconductors Via Machine Learning Prediction

    Fuel · 2022-01-01

    Yang Ling, Zhengxin Chen, Site Li, Yunxiao Guan, Min Shi, Jun Yue Zhu, Zhihai Cheng, Jiang Wu, Chaojie Yin, Mengjie Bai

    Read original paper ↗ ·

  23. Metal-related electrocatalysts for Li–CO 2 batteries: an overview of the fundamentals to explore future-oriented strategies

    Journal of Materials Chemistry A · 2022-01-01

    Huanzhu Lv, Xiang Huang, Xiaoqi Zhu, Bin Wang

    Read original paper ↗ ·

  24. Recent major advances and challenges in the emerging graphene-based nanomaterials in electrocatalytic fuel cell technology

    Journal of Materials Chemistry C · 2022-01-01

    Ayesha Khan Tareen, Karim Khan, Muhammad Waqas Iqbal, Ye Zhang, Zhongjian Xie, Asif Mahmood, Nasir Mahmood, Jianyu Long, Chuan Li, Han Zhang

    Read original paper ↗ ·

  25. Scientific Validation in Materials AI—A Critical Survey of Conceptual Approaches: A Review Study

    Journal of Artificial Intelligence for Materials Science · 2022-01-01

    Daniel Fischer, Laura Meier, Thomas Braun, Stefan Koch, Felix Roth

    Read original paper ↗ ·

  26. Understanding, discovery, and synthesis of 2D materials enabled by machine learning

    Chemical Society Reviews · 2022-01-01

    Byunghoon Ryu, Luqing Wang, Haihui Pu, Maria K. Y. Chan, Junhong Chen

    Read original paper ↗ ·

  27. An Easy, Simple, and Accessible Web-based Machine Learning Platform, SimPL-ML

    Integrating materials and manufacturing innovation · 2022-01-21

    Seunghun Jang, Gyoung S. Na, Jung‐Ho Lee, Jung‐Ho Shin, Hyun Woo Kim, Hyunju Chang

    Read original paper ↗ ·

  28. Realizing Quantum Technologies in Nanomaterials and Nanoscience

    Advanced Materials · 2022-02-04

    Sathvik Ajay Iyengar, Anand B. Puthirath, V. Swaminathan

    Read original paper ↗ ·

  29. Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations

    Scientific Data · 2022-02-21

    Fancy Qian Wang, Kamal Kumar Choudhary, Yu Liu, Jianjun Hu, Ming Hu

    Read original paper ↗ ·

  30. Agents for sequential learning using multiple-fidelity data

    Scientific Reports · 2022-03-18

    Aini Palizhati, Steven B. Torrisi, Muratahan Aykol, Santosh K. Suram, Jens S. Hummelshøj, Joseph H. Montoya

    Read original paper ↗ ·

  31. Large-scale synthesis of graphene and other 2D materials towards industrialization

    Nature Communications · 2022-03-18

    Soo Ho Choi, Seok Joon Yun, Yo Seob Won, Chang Seok Oh, Soo Min Kim, Ki Kang Kim, Young Hee Lee

    Read original paper ↗ ·

  32. Data‐Driven Materials Innovation and Applications

    Advanced Materials · 2022-04-22

    Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, Xue‐Feng Yu

    Read original paper ↗ ·

  33. Review on 2D Molybdenum Diselenide (MoSe 2 ) and Its Hybrids for Green Hydrogen (H 2 ) Generation Applications

    ACS Omega · 2022-05-12

    Muhammad B. Wazir, Muhammad Daud, Soma Safeer, Faisal Abdulla AlMarzooqi, Ahsanulhaq Qurashi

    Read original paper ↗ ·

  34. Data-Driven and Multiscale Modeling of DNA-Templated Dye Aggregates

    Molecules · 2022-05-27

    Austin Biaggne, Lawrence Spear, German Barcenas, Maia N. Ketteridge, Young C. Kim, Joseph S. Melinger, William B. Knowlton, Bernard Yurke, Lan Li

    Read original paper ↗ ·

  35. Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength

    ACS Omega · 2022-06-09

    Pengcheng Xu, Can Chen, Shuizhou Chen, Wencong Lu, Quan Qian, Yi Han Zeng

    Read original paper ↗ ·

  36. Machine Learning and First-Principles Discovery of Ternary Superhard Materials

    ACS symposium series · 2022-06-14

    Wei-Chih Chen, Da Yan, Cheng-Chien Chen

    Read original paper ↗ ·

  37. Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

    Scientific Reports · 2022-06-28

    Yucheng Fu, William E. Frazier, Kyoo Sil Choi, Lei Li, Zhijie Xu, Vineet V. Joshi, Ayoub Soulami

    Read original paper ↗ ·

  38. Research on the Selection of Raw Material Suppliers for Enterprises

    International Conference on E-business, Management and Economics (ICEME) · 2022-07-16

    Yuzhe Hu, Jian‐Qiang Zhong, Hong Pan, Yuer Yang

    Read original paper ↗ ·

  39. Toward a universal metasurface for optical imaging, communication, and computation

    Nanophotonics · 2022-07-21

    Prachi Thureja, Ruzan Sokhoyan, Claudio U. Hail, Jared F. Sisler, Morgan Foley, Meir Y. Grajower, Harry Albert Atwater

    Read original paper ↗ ·

  40. Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength

    Journal of The Institution of Engineers (India) Series D · 2022-07-25

    Vedant Shrikant Utpat, Swanand Gajanan Kulkarni

    Read original paper ↗ ·

  41. Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

    Nanomaterials · 2022-08-01

    Georgios Konstantopoulos, Elias P. Koumoulos, Costas A. Charitidis

    Read original paper ↗ ·

  42. Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water

    Environmental Research · 2022-08-04

    Xiaocui Yang, Xuan Cuong Nguyen, Quoc Ba Tran, Thi Thanh Huyen Nguyen, Shengbo Ge, Dinh Duc Nguyen, Van-Truc Nguyen, Phuoc–Cuong Le, Eldon Raj Rene, Pardeep Singh, Pankaj Raizada, Tansir Ahamad, Saad M. Alshehri, Changlei Xia, Soo Young Kim, Quyet Van Le

    Read original paper ↗ ·

  43. A Strategic Approach to Machine Learning for Material Science: How to Tackle Real-World Challenges and Avoid Pitfalls

    Chemistry of Materials · 2022-09-01

    Piyush Karande, Brian J. Gallagher, T. Yong-Jin Han

    Read original paper ↗ ·

  44. QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization

    Journal of Chemical Information and Modeling · 2022-09-08

    Masato Sumita, Kei Terayama, Ryo Tamura, Koji Tsuda

    Read original paper ↗ ·

  45. On the value of popular crystallographic databases for machine learning prediction of space groups

    Acta Materialia · 2022-09-10

    Vishwesh Venkatraman, P.A. Carvalho

    Read original paper ↗ ·

  46. MetaNOR: A meta-learnt nonlocal operator regression approach for metamaterial modeling

    MRS Communications · 2022-09-19

    Lu Zhang, Huaiqian You, Yue Yu

    Read original paper ↗ ·

  47. A machine learning approach to predict the structural and magnetic properties of Heusler alloy families

    Computational Materials Science · 2022-10-07

    Srimanta Mitra, Aquil Ahmad, Sajib Biswas, A. K. Das

    Read original paper ↗ ·

  48. Materials discovery of ion-selective membranes using artificial intelligence

    Communications Chemistry · 2022-10-20

    Reza Maleki, Seyed Mohammadreza Shams, Yasin Mehdizadeh Chellehbari, Sima Rezvantalab, Ahmad Miri Jahromi, Mohsen Asadnia, Rouzbeh Abbassi, Tejraj Malleshappa Aminabhavi, Amir Razmjou

    Read original paper ↗ ·

  49. Reduced-Order Damage Assessment Model for Dual-Phase Steels

    Integrating materials and manufacturing innovation · 2022-10-31

    Sanket Thakre, Anand K. Kanjarla

    Read original paper ↗ ·

  50. Building a better machine learning model of extubation for neurocritical care patients

    Intensive Care Medicine · 2022-11-05

    Shohei Ono

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