BetaCitation Atlas

RESEARCH PAPER · Nanoscale · 2019

Nanoinformatics, and the big challenges for the science of small things

Amanda Susan Barnard, Benyamin Motevalli, Amanda J. Parker, Julia Melisande Theresa Agatha Fischer, Christopher A. Feigl, George Opletal

Read original paper ↗

See where this research leads.

91 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

Nanoinformatics, and the big challenges for the science of small thingsOriginalPredicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning33Ab Initio Simulations and Materials Chemistry in the Age of Big Data37Feature Engineering of Solid‐State Crystalline Lattices for Machine Learning6Machine learning reveals multiple classes of diamond nanoparticles25The Emerging Role of Nano-informatics in America4Machine learning at the (sub)atomic scale: next generation scanning probe microscopy43Shaping the Future of Solid‐State Electrolytes through Computational Modeling43Understanding and Predicting the Cause of Defects in Graphene Oxide Nanostructures Using Machine Learning42Nanomaterial Transformation in the Soil–Plant System: Implications for Food Safety and Application in Agriculture166Accurate prediction of binding energies for two‐dimensional catalytic materials using machine learning18Selecting machine learning models for metallic nanoparticles35Role of Artificial Intelligence and Machine Learning in Nanosafety175Best Practice Leads to the Best Materials Informatics15Classification of platinum nanoparticle catalysts using machine learning46Exchange Spin Coupling from Gaussian Process Regression27Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base37Flow Synthesis of Metal Halide Perovskite Quantum Dots: From Rapid Parameter Space Mapping to AI-Guided Modular Manufacturing84A review of the biomechanical properties of single extracellular vesicles78TÉCNICAS DE MINERAÇÃO DE TEXTO APLICADAS EM ANÁLISE DE PATENTES0The pure and representative types of disordered platinum nanoparticles from machine learning12Advanced Technologies in Health and Neurodegenerative Diseases1Charge-Dependent Fermi Level of Graphene Oxide Nanoflakes from Machine Learning10Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles10Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks14Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment96Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics12Enhancing classical gold nanoparticle simulations with electronic corrections and machine learning1The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62Interfacial informatics4Machine-Learned Decision Trees for Predicting Gold Nanorod Sizes from Spectra42Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning8Inverse Design of Nanoparticles Using Multi‐Target Machine Learning41Nanomaterial transformation in root–soil interface: a function of root exudate or microbial activity?2Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning5Daphnia magna and mixture toxicity with nanomaterials – Current status and perspectives in data-driven risk prediction46Predicting the Probability of Observation of Arbitrary Graphene Oxide Nanoflakes Using Artificial Neural Networks6Text Mining for Energy Materials3Data-driven causal inference of process-structure relationships in nanocatalysis26Safety-by-design using forward and inverse multi-target machine learning16Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology232Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference6Threats to Terrestrial Plants from Emerging Nanoplastics102Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools35The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts7Using Machine Learning to make nanomaterials sustainable57Federated data processing and learning for collaboration in the physical sciences11Prediction and Construction of Energetic Materials Based on Machine Learning Methods53Advanced Technologies in Health and Neurodegenerative Diseases0An Investigation on the Impacts of Calcium Oxide Nanoparticles Enriched Feed in the Survival, Bioaccumulation and Physiological Phenomenon in Zebra Fish (Danio Rerio)0Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference8Machine Learning for Molecular Recognition at the Nanoscale0Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation85Importance of Structural Features and the Influence of Individual Structures of Graphene Oxide Using Shapley Value Analysis17Application of Artificial Intelligence in the Exploration and Optimization of Biomedical Nanomaterials91The emergent role of explainable artificial intelligence in the materials sciences32Structure-Free Mendeleev Encodings of Material Compounds for Machine Learning18Superior Prediction of Graphene Nanoflake Properties withUnbiased Graph Embedding2Simultaneous Prediction and Optimization of Charge Transfer Properties of Graphene and Graphene Oxide Nanoflakes from Multitarget Machine Learning6Scanning probe microscopy in the age of machine learning29Insights into Nanodiamond from Machine Learning1Understanding the importance of individual samples and their effects on materials data using explainable artificial intelligence16Unsupervised learning and pattern recognition in alloy design15Untitled work0Advancing electron microscopy using deep learning15Online meta-learned gradient norms for active learning in science and technology2Classification of battery compounds using structure-free Mendeleev encodings12How scanning probe microscopy can be supported by artificial intelligence and quantum computing?10New Health Technologies and Regulatory Approaches0Predicting battery applications for complex materials based on chemical composition and machine learning14Comprehensive insights into mechanism of nanotoxicity, assessment methods and regulatory challenges of nanomedicines186Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience17Diverse explanations from data-driven and domain-driven perspectives in the physical sciences10Properties optimisation of nanostructures via machine learning: Progress and perspective3Recent advances in biomedical applications of smart nanomaterials: a comprehensive review28Artificial Intelligence Enabled Biomineralization for Eco‐Friendly Nanomaterial Synthesis: Charting Future Trends56How nanoinformatics could pave the way to safer design of engineered nanomaterials?9Large extracellular vesicles subsets and contents discrimination: the potential of morpho mechanical approaches at single vesicle level0Understanding interpretable patterns of Shapley behaviours in materials data4What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials2Nanoinformatics: A Potential Tool for Precision Targeting in Pediatric Cancer2Machine learning prediction and efficient screening method for thermally induced fluorescence enhancement of Er 3+ doped materials6Impact of nanoparticle morphologies on property prediction using explainable AI1Leveraging AI Innovation in Nanomaterials, Synthesis, Characterization, and Device Design0Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs13A review on algorithmic approaches for nano material design and optimization in advanced engineering applications2AI and Machine Learning: Revolutionizing Nanomaterial Research Through Synthesis, Characterization, and Applications0Artificial intelligence-driven nanomaterial design and applications: advances, challenges, and future directions0Computational Predictive Models for Nanomedicines0Machine learning applications in nanomaterial preparation for thermochemical processes0J³SPM AI: An integrated open-source platform for AI-assisted image analysis and image-guided workflows in scanning probe microscopy2Artificial intelligence in nanotechnology: current applications and future perspectives0

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

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

  1. Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning

    Nanoscale · 2019-01-01

    Amanda Susan Barnard, George Opletal

    Read original paper ↗ ·

  2. Ab Initio Simulations and Materials Chemistry in the Age of Big Data

    Journal of Chemical Information and Modeling · 2019-10-25

    Gabriel Ravanhani Schleder, A. C. M. Padilha, Alexandre Reily Rocha, Gustavo M. Dalpian, A. Fazzio

    Read original paper ↗ ·

  3. Feature Engineering of Solid‐State Crystalline Lattices for Machine Learning

    Advanced Theory and Simulations · 2019-12-15

    Timothy O. Cox, Benyamin Motevalli, George Opletal, Amanda Susan Barnard

    Read original paper ↗ ·

  4. Machine learning reveals multiple classes of diamond nanoparticles

    Nanoscale Horizons · 2020-01-01

    Amanda J. Parker, Amanda Susan Barnard

    Read original paper ↗ ·

  5. The Emerging Role of Nano-informatics in America

    SSRN Electronic Journal · 2020-01-01

    Rahul Reddy Nadikattu

    Read original paper ↗ ·

  6. Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

    Machine Learning Science and Technology · 2020-03-06

    Oliver Gordon, Philip Moriarty

    Read original paper ↗ ·

  7. Shaping the Future of Solid‐State Electrolytes through Computational Modeling

    Advanced Materials · 2020-03-06

    Ardeshir Baktash, James C. Reid, Qinghong Yuan, Tanglaw Roman, Debra J. Searles

    Read original paper ↗ ·

  8. Understanding and Predicting the Cause of Defects in Graphene Oxide Nanostructures Using Machine Learning

    The Journal of Physical Chemistry C · 2020-03-08

    Benyamin Motevalli, Baichuan Sun, Amanda Susan Barnard

    Read original paper ↗ ·

  9. Nanomaterial Transformation in the Soil–Plant System: Implications for Food Safety and Application in Agriculture

    Small · 2020-04-27

    Peng Zhang, Zhiling Guo, Zhiyong Zhang, Hualing Fu, Jason Christopher White, Iseult Lynch

    Read original paper ↗ ·

  10. Accurate prediction of binding energies for two‐dimensional catalytic materials using machine learning

    ChemCatChem · 2020-06-02

    Julia Melisande Theresa Agatha Fischer, Michelle A. Hunter, Marlies Hankel, Debra J. Searles, Amanda J. Parker, Amanda Susan Barnard

    Read original paper ↗ ·

  11. Selecting machine learning models for metallic nanoparticles

    Nano Futures · 2020-06-12

    Amanda Susan Barnard, George Opletal

    Read original paper ↗ ·

  12. Role of Artificial Intelligence and Machine Learning in Nanosafety

    Small · 2020-06-15

    David Alan Winkler

    Read original paper ↗ ·

  13. Best Practice Leads to the Best Materials Informatics

    Matter · 2020-07-01

    Amanda Susan Barnard

    Read original paper ↗ ·

  14. Classification of platinum nanoparticle catalysts using machine learning

    Journal of Applied Physics · 2020-07-01

    Amanda J. Parker, George Opletal, Amanda Susan Barnard

    Read original paper ↗ ·

  15. Exchange Spin Coupling from Gaussian Process Regression

    The Journal of Physical Chemistry A · 2020-09-22

    Marc Philipp Bahlke, Natnael Mogos, Jonny Proppe, Carmen Herrmann

    Read original paper ↗ ·

  16. Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base

    Nanomaterials · 2020-09-29

    Diego Stéfani T. Martinez, Gabriela Helena da Silva, Aline Maria Zigiotto de Medeiros, Latif Ullah Khan, Anastasios G. Papadiamantis, Iseult Lynch

    Read original paper ↗ ·

  17. Flow Synthesis of Metal Halide Perovskite Quantum Dots: From Rapid Parameter Space Mapping to AI-Guided Modular Manufacturing

    Matter · 2020-10-01

    Kameel Abdel‐Latif, Fazel Bateni, Steven H. Crouse, Milad Abolhasani

    Read original paper ↗ ·

  18. A review of the biomechanical properties of single extracellular vesicles

    Nano Select · 2020-10-27

    Michael D. LeClaire, James Kazimierz Gimzewski, Shivani Sharma

    Read original paper ↗ ·

  19. TÉCNICAS DE MINERAÇÃO DE TEXTO APLICADAS EM ANÁLISE DE PATENTES

    Anais ... Congresso Internacional do Conhecimento e Inovação · 2020-11-18

    Luciano Zamperetti Wolski, L Artese, Alexandre Gonçalves, Rafael Mattos

    Read original paper ↗ ·

  20. The pure and representative types of disordered platinum nanoparticles from machine learning

    Nanotechnology · 2020-11-20

    Amanda J. Parker, Benyamin Motevalli, George Opletal, Amanda Susan Barnard

    Read original paper ↗ ·

  21. Advanced Technologies in Health and Neurodegenerative Diseases

    Springer eBooks · 2021-01-01

    Nikolaos Naziris, Costas Demetzos

    Read original paper ↗ ·

  22. Charge-Dependent Fermi Level of Graphene Oxide Nanoflakes from Machine Learning

    Computational Materials Science · 2021-01-01

    Benyamin Motevalli, Bronwyn Louise Fox, Amanda Susan Barnard

    Read original paper ↗ ·

  23. Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles

    Nanoscale · 2021-01-01

    Haonan Zhang, Amanda Susan Barnard

    Read original paper ↗ ·

  24. Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks

    Nanoscale Horizons · 2021-01-01

    Amanda J. Parker, Amanda Susan Barnard

    Read original paper ↗ ·

  25. Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

    Journal of Chemical Information and Modeling · 2021-02-25

    José Jiménez-Luna, Miha Škalič, Nils Weskamp, Gisbert Schneider

    Read original paper ↗ ·

  26. Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics

    Machine Learning Science and Technology · 2021-05-14

    Tommy Liu, Amanda Susan Barnard

    Read original paper ↗ ·

  27. Enhancing classical gold nanoparticle simulations with electronic corrections and machine learning

    Journal of Physics Condensed Matter · 2021-06-02

    Ryan Stocks, Amanda Susan Barnard

    Read original paper ↗ ·

  28. 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 ↗ ·

  29. Interfacial informatics

    Journal of Physics Materials · 2021-07-01

    Julia Melisande Theresa Agatha Fischer, Amanda J Parker, Amanda Susan Barnard

    Read original paper ↗ ·

  30. Machine-Learned Decision Trees for Predicting Gold Nanorod Sizes from Spectra

    The Journal of Physical Chemistry C · 2021-08-26

    Katsuya Shiratori, Logan D. C. Bishop, Behnaz Ostovar, Rashad Baiyasi, Yiyu Cai, Peter J. Rossky, Christy F. Landes, Stephan Link

    Read original paper ↗ ·

  31. Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

    Beilstein Journal of Nanotechnology · 2021-11-29

    Richard Marchese Robinson, Haralambos Sarimveis, Philip Doganis, Xiaodong Jia, Marianna I. Kotzabasaki, Christiana Gousiadou, Stacey L. Harper, Terry Wilkins

    Read original paper ↗ ·

  32. Inverse Design of Nanoparticles Using Multi‐Target Machine Learning

    Advanced Theory and Simulations · 2021-12-07

    Sichao Li, Amanda Susan Barnard

    Read original paper ↗ ·

  33. Nanomaterial transformation in root–soil interface: a function of root exudate or microbial activity?

    Elsevier eBooks · 2022-01-01

    Peng Zhang, Wanqin Dai, Chaonan Dong, Zhiyong Zhang

    Read original paper ↗ ·

  34. Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning

    Lecture notes in computer science · 2022-01-01

    Sichao Li, Jonathan Y. C. Ting, Amanda Susan Barnard

    Read original paper ↗ ·

  35. Daphnia magna and mixture toxicity with nanomaterials – Current status and perspectives in data-driven risk prediction

    Nano Today · 2022-02-18

    Diego Stéfani T. Martinez, Laura‐Jayne A. Ellis, Gabriela Helena da Silva, Romana Petry, Aline Maria Zigiotto de Medeiros, Hossein Hayat Davoudi, Anastasios G. Papadiamantis, A. Fazzio, Antreas Afantitis, Georgia Melagraki, Iseult Lynch

    Read original paper ↗ ·

  36. Predicting the Probability of Observation of Arbitrary Graphene Oxide Nanoflakes Using Artificial Neural Networks

    Advanced Theory and Simulations · 2022-02-20

    Benyamin Motevalli, Lachlan Hyde, Bronwyn Louise Fox, Amanda Susan Barnard

    Read original paper ↗ ·

  37. Text Mining for Energy Materials

    Journal of Research in Science and Engineering · 2022-03-31

    Read original paper ↗ ·

  38. Data-driven causal inference of process-structure relationships in nanocatalysis

    Current Opinion in Chemical Engineering · 2022-04-13

    Jonathan Y. C. Ting, Amanda Susan Barnard

    Read original paper ↗ ·

  39. Safety-by-design using forward and inverse multi-target machine learning

    Chemosphere · 2022-05-23

    Sichao Li, Amanda Susan Barnard

    Read original paper ↗ ·

  40. Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology

    ACS Nano · 2022-06-22

    Cátia Domingues, Ana Cláudia Paiva‐Santos, Carmen Alvarez‐Lorenzo, Ángel Concheiro, Ivana Jarak, Francisco Veiga, I.R. Barbosa, Marı́lia Dourado, Ana Figueiras

    Read original paper ↗ ·

  41. Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference

    Advanced Theory and Simulations · 2022-07-24

    Jonathan Y. C. Ting, Sichao Li, Amanda Susan Barnard

    Read original paper ↗ ·

  42. Threats to Terrestrial Plants from Emerging Nanoplastics

    ACS Nano · 2022-10-06

    Fei Dang, Qingyu Wang, Xiliang Yan, Yuanye Zhang, Jiachen Yan, Huan Zhong, Dongmei Zhou, Yongming Luo, Yong‐Guan Zhu, Baoshan Xing, Yujun Wang

    Read original paper ↗ ·

  43. Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

    International Journal of Molecular Sciences · 2022-10-19

    Elena O. Kutumova, Ilya R. Akberdin, Ilya N. Kiselev, Ruslan Sharipov, Vera S. Egorova, Anastasiia O. Syrocheva, Alessandro Parodi, Andrey A. Zamyatnin, Fedor Kolpakov

    Read original paper ↗ ·

  44. The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts

    Journal of Computational Science · 2022-10-30

    Sichao Li, Jonathan Y. C. Ting, Amanda Susan Barnard

    Read original paper ↗ ·

  45. Using Machine Learning to make nanomaterials sustainable

    The Science of The Total Environment · 2022-11-18

    Janeck James Scott-Fordsmand, Mónica J.B. Amorim

    Read original paper ↗ ·

  46. Federated data processing and learning for collaboration in the physical sciences

    Machine Learning Science and Technology · 2022-12-01

    Wenjiang Huang, Amanda Susan Barnard

    Read original paper ↗ ·

  47. Prediction and Construction of Energetic Materials Based on Machine Learning Methods

    Molecules · 2022-12-31

    Xiaowei Zang, Xiang Zhou, Haitao Bian, Weiping Jin, Xuhai Pan, Juncheng Jiang, M. Yu. Koroleva, Ruiqi Shen

    Read original paper ↗ ·

  48. Advanced Technologies in Health and Neurodegenerative Diseases

    Springer eBooks · 2023-01-01

    Nikolaos Naziris, Costas Demetzos

    Read original paper ↗ ·

  49. An Investigation on the Impacts of Calcium Oxide Nanoparticles Enriched Feed in the Survival, Bioaccumulation and Physiological Phenomenon in Zebra Fish (Danio Rerio)

    SSRN Electronic Journal · 2023-01-01

    Madhubala Kumari, Aastha Tiwary, Biplab Sarkar, Koel Mukherjee

    Read original paper ↗ ·

  50. Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference

    Chemistry of Materials · 2023-01-09

    Jonathan Y. C. Ting, Amanda J. Parker, Amanda Susan Barnard

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