| Self-assembly of magnetite nanocubes into helical superstructures G Singh, H Chan, A Baskin, E Gelman, N Repnin, P Král, R Klajn Science 345 (6201), 1149-1153, 2014 | 502 | 2014 |
| Chiral templating of self-assembling nanostructures by circularly polarized light J Yeom, B Yeom, H Chan, KW Smith, S Dominguez-Medina, JH Bahng, ... Nature materials 14 (1), 66-72, 2015 | 368 | 2015 |
| Nanostructured layered cathode for rechargeable Mg-ion batteries S Tepavcevic, Y Liu, D Zhou, B Lai, J Maser, X Zuo, H Chan, P Král, ... ACS nano 9 (8), 8194-8205, 2015 | 206 | 2015 |
| Crude-oil-repellent membranes by atomic layer deposition: oxide interface engineering HC Yang, Y Xie, H Chan, B Narayanan, L Chen, RZ Waldman, ... ACS nano 12 (8), 8678-8685, 2018 | 159 | 2018 |
| Machine learning coarse grained models for water H Chan, MJ Cherukara, B Narayanan, TD Loeffler, C Benmore, SK Gray, ... Nature communications 10 (1), 379, 2019 | 157 | 2019 |
| Diffusion and filtration properties of self-assembled gold nanocrystal membranes J He, XM Lin, H Chan, L Vukovic, P Král, HM Jaeger Nano letters 11 (6), 2430-2435, 2011 | 149 | 2011 |
| Self-assembly of nanoparticles into biomimetic capsid-like nanoshells M Yang, H Chan, G Zhao, JH Bahng, P Zhang, P Král, NA Kotov Nature chemistry 9 (3), 287-294, 2017 | 120 | 2017 |
| Machine learning classical interatomic potentials for molecular dynamics from first-principles training data H Chan, B Narayanan, MJ Cherukara, FG Sen, K Sasikumar, SK Gray, ... The Journal of Physical Chemistry C 123 (12), 6941-6957, 2019 | 97 | 2019 |
| Screening of therapeutic agents for COVID-19 using machine learning and ensemble docking studies R Batra, H Chan, G Kamath, R Ramprasad, MJ Cherukara, ... The journal of physical chemistry letters 11 (17), 7058-7065, 2020 | 83 | 2020 |
| Defect Dynamics in 2-D MoS2 Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy TK Patra, F Zhang, DS Schulman, H Chan, MJ Cherukara, M Terrones, ... ACS nano 12 (8), 8006-8016, 2018 | 83 | 2018 |
| Magnetic field-induced self-assembly of iron oxide nanocubes G Singh, H Chan, T Udayabhaskararao, E Gelman, D Peddis, A Baskin, ... Faraday discussions 181, 403-421, 2015 | 70 | 2015 |
| Machine learning enabled autonomous microstructural characterization in 3D samples H Chan, M Cherukara, TD Loeffler, B Narayanan, ... npj Computational Materials 6 (1), 1, 2020 | 67 | 2020 |
| Interfacial localization and voltage-tunable arrays of charged nanoparticles MK Bera, H Chan, DF Moyano, H Yu, S Tatur, D Amoanu, W Bu, ... Nano letters 14 (12), 6816-6822, 2014 | 60 | 2014 |
| Machine learning overcomes human bias in the discovery of self-assembling peptides R Batra, TD Loeffler, H Chan, S Srinivasan, H Cui, IV Korendovych, ... Nature chemistry 14 (12), 1427-1435, 2022 | 55 | 2022 |
| Colloidal nanocube supercrystals stabilized by multipolar coulombic coupling H Chan, A Demortière, L Vukovic, P Král, C Petit ACS nano 6 (5), 4203-4213, 2012 | 52 | 2012 |
| Perovskite nickelates as bio-electronic interfaces HT Zhang, F Zuo, F Li, H Chan, Q Wu, Z Zhang, B Narayanan, ... Nature communications 10 (1), 1651, 2019 | 46 | 2019 |
| Creation of Single-Photon Emitters in WSe2 Monolayers Using Nanometer-Sized Gold Tips L Peng, H Chan, P Choo, TW Odom, SKRS Sankaranarayanan, X Ma Nano letters 20 (8), 5866-5872, 2020 | 43 | 2020 |
| Rapid 3D nanoscale coherent imaging via physics-aware deep learning H Chan, YSG Nashed, S Kandel, SO Hruszkewycz, ... Applied Physics Reviews 8 (2), 2021 | 39 | 2021 |
| Learning in continuous action space for developing high dimensional potential energy models S Manna, TD Loeffler, R Batra, S Banik, H Chan, B Varughese, ... Nature communications 13 (1), 368, 2022 | 38 | 2022 |
| Active learning the potential energy landscape for water clusters from sparse training data TD Loeffler, TK Patra, H Chan, M Cherukara, SKRS Sankaranarayanan The Journal of Physical Chemistry C 124 (8), 4907-4916, 2020 | 33 | 2020 |
Subramanian KRS SankaranarayananArgonne National Laboratory在 anl.gov 的电子邮件经过验证
