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RESEARCH PAPER · The Journal of Physical Chemistry C · 2020

Learning from the Machine: Uncovering Sustainable Nanoparticle Design Rules

Clyde A. Daly, Rigoberto Hernandez

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17 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
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Learning from the Machine: Uncovering Sustainable Nanoparticle Design RulesOriginalOptimizing bags of artificial neural networks for the prediction of viability from sparse data5The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry3The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry6The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry2Deep learning methods for the computation of vibrational wavefunctions6Data Shepherding in Nanotechnology: An Antimicrobial Functionality Data Capture Template12A Supervised Machine-Learning Prediction of Textile’s Antimicrobial Capacity Coated with Nanomaterials16Health and environmental safety of nanomaterials: O Data, Where Art Thou?31Machine Learning-Assisted Carbon Dot Synthesis: Prediction of Emission Color and Wavelength89Progress in Biomedical Applications Using Sustainable Nanoparticles1Machine Learning Frontier Orbital Energies of Nanodiamonds8Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles6Does the surface charge of the nanoparticles drive nanoparticle–cell membrane interactions?67Structure and Zeta Potential of Gold Nanoparticles with Coronas of Varying Size and Composition1033 Unresolved Questions in Nanoscience and Nanotechnology58Toward Robust and Stable Model Selection in Machine Learning for Materials Science via Systematic Hyperparameter Optimization0MCCX: Combining Monte Carlo, Convolutional Neural Networks and Boosting in Predicting Nanoparticle Viability0

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  1. Optimizing bags of artificial neural networks for the prediction of viability from sparse data

    The Journal of Chemical Physics · 2020-08-04

    Clyde A. Daly, Rigoberto Hernandez

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  2. The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry

    The Journal of Physical Chemistry C · 2020-11-05

    Andrew L. Ferguson, Johannes Hachmann, Thomas F. Miller, Jim Pfaendtner

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  3. The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry

    The Journal of Physical Chemistry B · 2020-11-05

    Andrew L. Ferguson, Johannes Hachmann, Thomas F. Miller, Jim Pfaendtner

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  4. The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry

    The Journal of Physical Chemistry A · 2020-11-05

    Andrew L. Ferguson, Johannes Hachmann, Thomas F. Miller, Jim Pfaendtner

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  5. Deep learning methods for the computation of vibrational wavefunctions

    Communications in Nonlinear Science and Numerical Simulation · 2021-08-31

    Laia Domingo, F. Borondo

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  6. Data Shepherding in Nanotechnology: An Antimicrobial Functionality Data Capture Template

    Coatings · 2021-12-02

    Irini Furxhi, Alessio Varesano, Hesham H. Salman, Mahsa Mirzaei, Vittoria Battistello, Ivonne Tonani Tomasoni, Magda Blosi

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  7. A Supervised Machine-Learning Prediction of Textile’s Antimicrobial Capacity Coated with Nanomaterials

    Coatings · 2021-12-13

    Mahsa Mirzaei, Irini Furxhi, Finbarr Murphy, Martin Mullins

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  8. Health and environmental safety of nanomaterials: O Data, Where Art Thou?

    NanoImpact · 2021-12-22

    Irini Furxhi

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  9. Machine Learning-Assisted Carbon Dot Synthesis: Prediction of Emission Color and Wavelength

    Journal of Chemical Information and Modeling · 2022-11-17

    Ravithree D. Senanayake, Xiaoxiao Yao, Clarice E. Froehlich, Meghan S. Cahill, Trever R. Sheldon, Mary McIntire, Christy L. Haynes, Rigoberto Hernandez

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  10. Progress in Biomedical Applications Using Sustainable Nanoparticles

    Springer eBooks · 2023-01-01

    Lidawani Lambuk, Suhana Ahmad, Siti Khatijah Abdul Razak, Ros Akmal Mohd Idris, Rohimah Mohamud

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  11. Machine Learning Frontier Orbital Energies of Nanodiamonds

    Journal of Chemical Theory and Computation · 2023-04-13

    Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé

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  12. Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles

    The Journal of Physical Chemistry A · 2024-03-27

    Ravithree D. Senanayake, Clyde A. Daly, Rigoberto Hernandez

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  13. Does the surface charge of the nanoparticles drive nanoparticle–cell membrane interactions?

    Current Opinion in Biotechnology · 2024-04-05

    Sandor Balog, Mauro Sousa de Almeida, Patricia Taladriz‐Blanco, Barbara Rothen‐Rutishauser, Alke Petri‐Fink

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  14. Structure and Zeta Potential of Gold Nanoparticles with Coronas of Varying Size and Composition

    The Journal of Physical Chemistry C · 2025-02-18

    Xingfei Wei, Arham R. Alam, Qian‐Kun Mo, Rigoberto Hernandez

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  15. 33 Unresolved Questions in Nanoscience and Nanotechnology

    ACS Nano · 2025-09-04

    Chad Alexander Mirkin, Sarah Hurst Petrosko, Natalie Artzi, Koray Aydın, Austin Biaggne, C. Jeffrey Brinker, Katherine E. Bujold, Y. Charles Cao, Rachel R. Chan, Chaojian Chen, Pengcheng Chen, Xiaodong Chen, Olivier J. G. L. Chevalier, Chung Hang Jonathan Choi, Richard M. Crooks, Vinayak P. Dravid, Jingshan S. Du, Sasha B. Ebrahimi, Hongyou Fan, Omar K. Farha, C. Adrian Figg, Tanner D. Fink, Connor M. Forsyth, Harald Fuchs, Franz M. Geiger, Nathan C. Gianneschi, Kyle J. Gibson, David S. Ginger, Sishi Guo, Justin S. Hanes, Liangliang Hao, Jin Shan Huang, Bryan Michael Hunter, Fengwei Huo, Jeongmin Hwang, Rongchao M. Jin, Shana O. Kelley, Thomas J. Kempa, Youngeun Kim, Sergej Kudruk, Sneha Kumari, Kaitlin M. Landy, Ki‐Bum Lee, Noel J. Leon, Jun Li, Yuanwei Li, Zhiwei Li, Bin Liu, Guoliang Liu, Xiaogang Liu, Luis M. Liz‐Marzán, Jochen H. Lorch, Taokun Luo, Robert J. Macfarlane, Jill E. Millstone, Milan Mrksich, Catherine Jones Murphy, Rajesh R. Naik, André E. Nel, Christopher Oetheimer, Jenny Orbeck, So‐Jung Park, Benjamin E. Partridge, Nicholas A. Peppas, Michelle L. Personick, Arindam Raj, Namrata Ramani, Michael B. Ross, Susan E. Ross, Edward Hartley Sargent, Tanushri Sengupta, George Chappell Schatz, Dwight S. Seferos, Tamar Seideman, Soyoung E. Seo, Bo Shen, Wooyoung Shim, Donghoon Shin, Ulrich Simon, Andrew J. Sinegra, Peter T. Smith, Alexander M. Spokoyny, Peter J. Stang, Alexander H. Stegh, James Fraser Stoddart, Dayne F. Swearer, Weihong Tan, Michelle H. Teplensky, Colby Shad Thaxton, David R. Walt, Mary X. Wang, Zhe Wang, Wei David Wei, Paul S. Weiss, Peter H. Winegar, Younan Xia, Yi Xie, Xiaoyang Xu, Peidong Yang, Yiming Yang

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  16. Toward Robust and Stable Model Selection in Machine Learning for Materials Science via Systematic Hyperparameter Optimization

    Advanced Theory and Simulations · 2026-01-01

    Yuliana Nur Azizah, Fitri Aulia Permatasari, Ferry Iskandar

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  17. MCCX: Combining Monte Carlo, Convolutional Neural Networks and Boosting in Predicting Nanoparticle Viability

    ACS Applied Engineering Materials · 2026-08-20

    Qiankun Mo, Xingfei Wei, Ravithree D. Senanayake, Amelyn Phang, Rigoberto Hernandez

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