| Study of metal-organic framework MIL-101 (Cr) for natural gas (methane) storage and compare with other MOFs (metal-organic frameworks) S Kayal, B Sun, A Chakraborty Energy 91, 772-781, 2015 | 162 | 2015 |
| Adsorption characteristics of AQSOA zeolites and water for adsorption chillers S Kayal, S Baichuan, BB Saha International Journal of Heat and Mass Transfer 92, 1120-1127, 2016 | 150 | 2016 |
| Study of HKUST (Copper benzene-1, 3, 5-tricarboxylate, Cu-BTC MOF)-1 metal organic frameworks for CH4 adsorption: An experimental Investigation with GCMC (grand canonical Monte … B Sun, S Kayal, A Chakraborty Energy 76, 419-427, 2014 | 115 | 2014 |
| An adsorption isotherm equation for multi-types adsorption with thermodynamic correctness A Chakraborty, B Sun Applied Thermal Engineering 72 (2), 190-199, 2014 | 92 | 2014 |
| Thermodynamic frameworks of adsorption kinetics modeling: Dynamic water uptakes on silica gel for adsorption cooling applications B Sun, A Chakraborty Energy 84, 296-302, 2015 | 88 | 2015 |
| Thermodynamic formalism of water uptakes on solid porous adsorbents for adsorption cooling applications B Sun, A Chakraborty Applied physics letters 104 (20), 2014 | 88 | 2014 |
| Machine learning for silver nanoparticle electron transfer property prediction B Sun, M Fernandez, AS Barnard Journal of chemical information and modeling 57 (10), 2413-2423, 2017 | 69 | 2017 |
| Statistics, damned statistics and nanoscience–using data science to meet the challenge of nanomaterial complexity B Sun, M Fernandez, AS Barnard Nanoscale Horizons 1 (2), 89-95, 2016 | 38 | 2016 |
| Understanding and predicting the cause of defects in graphene oxide nanostructures using machine learning B Motevalli, B Sun, AS Barnard The Journal of Physical Chemistry C 124 (13), 7404-7413, 2020 | 32 | 2020 |
| The representative structure of graphene oxide nanoflakes from machine learning B Motevalli, AJ Parker, B Sun, AS Barnard Nano Futures 3 (4), 045001, 2019 | 30 | 2019 |
| Visualising multi-dimensional structure/property relationships with machine learning B Sun, AS Barnard Journal of Physics: Materials 2 (3), 034003, 2019 | 27 | 2019 |
| The impact of size and shape distributions on the electron charge transfer properties of silver nanoparticles B Sun, AS Barnard Nanoscale 9 (34), 12698-12708, 2017 | 26 | 2017 |
| Representing molecular and materials data for unsupervised machine learning E Swann, B Sun, DM Cleland, AS Barnard Molecular simulation 44 (11), 905-920, 2018 | 22 | 2018 |
| Classifying and predicting the electron affinity of diamond nanoparticles using machine learning CA Feigl, B Motevalli, AJ Parker, B Sun, AS Barnard Nanoscale Horizons 4 (4), 983-990, 2019 | 18 | 2019 |
| The devil is in the labels: Semantic segmentation from sentences W Yin, Y Liu, C Shen, A Hengel, B Sun arXiv preprint arXiv:2202.02002, 2022 | 15 | 2022 |
| From process to properties: correlating synthesis conditions and structural disorder of platinum nanocatalysts B Sun, H Barron, G Opletal, AS Barnard The Journal of Physical Chemistry C 122 (49), 28085-28093, 2018 | 15 | 2018 |
| Texture based image classification for nanoparticle surface characterisation and machine learning B Sun, AS Barnard Journal of Physics: Materials 1 (1), 016001, 2018 | 13 | 2018 |
| Predicting archetypal nanoparticle shapes using a combination of thermodynamic theory and machine learning T Yan, B Sun, AS Barnard Nanoscale 10 (46), 21818-21826, 2018 | 13 | 2018 |
| Disordered Platinum Nanoparticle Data Set, v1 A Barnard, B Sun, G Opletal CSIRO Data Collection, 2018 | 10 | 2018 |
| Silver nanoparticle data set AS Barnard, B Sun, BM Soumehsaraei, G Opletal CSIRODataCollection (https://doi. org/10. 4225/08/595f2a960c870), 2017 | 9 | 2017 |
Amanda S. BarnardSenior Professor of Computational Science, Australian National UniversityVerified email at anu.edu.au
