| Bayesian uncertainty quantification and propagation in molecular dynamics simulations: a high performance computing framework P Angelikopoulos, C Papadimitriou, P Koumoutsakos The Journal of chemical physics 137 (14), 2012 | 237 | 2012 |
| On the Kapitza Resistance between Few-Layer Graphene and Water: Liquid Layering Effects D Alexeev, J Chen, JH Walther, KP Giapis, P Angelikopoulos, ... Nano Letters 15 (9), 5744–5749, 2015 | 223 | 2015 |
| Personalized radiotherapy design for glioblastoma: integrating mathematical tumor models, multimodal scans, and Bayesian inference J Lipková, P Angelikopoulos, S Wu, E Alberts, B Wiestler, C Diehl, ... IEEE transactions on medical imaging 38 (8), 1875-1884, 2019 | 155 | 2019 |
| Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models PE Hadjidoukas, P Angelikopoulos, C Papadimitriou, P Koumoutsakos Journal of Computational Physics 284, 1-21, 2015 | 144 | 2015 |
| X-TMCMC: Adaptive kriging for Bayesian inverse modeling P Angelikopoulos, C Papadimitriou, P Koumoutsakos Computer Methods in Applied Mechanics and Engineering 289, 409-428, 2015 | 136 | 2015 |
| Dispersing individual single-wall carbon nanotubes in aqueous surfactant solutions below the cmc P Angelikopoulos, A Gromov, A Leen, O Nerushev, H Bock, ... The Journal of Physical Chemistry C 114 (1), 2-9, 2010 | 97 | 2010 |
| Homogeneous hydrophobic–hydrophilic surface patterns enhance permeation of nanoparticles through lipid membranes P Gkeka, L Sarkisov, P Angelikopoulos The journal of physical chemistry letters 4 (11), 1907-1912, 2013 | 94 | 2013 |
| The science of dispersing carbon nanotubes with surfactants P Angelikopoulos, H Bock Physical chemistry chemical physics 14 (27), 9546-9557, 2012 | 81 | 2012 |
| Membrane partitioning of anionic, ligand-coated nanoparticles is accompanied by ligand snorkeling, local disordering, and cholesterol depletion P Gkeka, P Angelikopoulos, L Sarkisov, Z Cournia PLoS computational biology 10 (12), e1003917, 2014 | 80 | 2014 |
| Directed self-assembly of surfactants in carbon nanotube materials P Angelikopoulos, H Bock The Journal of Physical Chemistry B 112 (44), 13793-13801, 2008 | 75 | 2008 |
| Dual nanoscale roughness on plasma-etched Si surfaces: Role of etch inhibitors G Kokkoris, V Constantoudis, P Angelikopoulos, G Boulousis, ... Physical Review B—Condensed Matter and Materials Physics 76 (19), 193405, 2007 | 72 | 2007 |
| Bayesian annealed sequential importance sampling: an unbiased version of transitional Markov chain Monte Carlo S Wu, P Angelikopoulos, C Papadimitriou, P Koumoutsakos ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B …, 2018 | 70 | 2018 |
| Self-assembly of anionic, ligand-coated nanoparticles in lipid membranes P Angelikopoulos, L Sarkisov, Z Cournia, P Gkeka Nanoscale 9 (3), 1040-1048, 2017 | 64 | 2017 |
| Data driven, predictive molecular dynamics for nanoscale flow simulations under uncertainty P Angelikopoulos, C Papadimitriou, P Koumoutsakos The Journal of Physical Chemistry B 117 (47), 14808-14816, 2013 | 63 | 2013 |
| A hierarchical Bayesian framework for force field selection in molecular dynamics simulations S Wu, P Angelikopoulos, C Papadimitriou, R Moser, P Koumoutsakos Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2016 | 58 | 2016 |
| Bayesian hierarchical models for uncertainty quantification in structural dynamics GC Ballesteros, P Angelikopoulos, C Papadimitriou, P Koumoutsakos Vulnerability, uncertainty, and risk: Quantification, mitigation, and …, 2014 | 41 | 2014 |
| The differences in surfactant adsorption on carbon nanotubes and their bundles P Angelikopoulos, H Bock Langmuir 26 (2), 899-907, 2010 | 40 | 2010 |
| Hierarchical stochastic model in Bayesian inference for engineering applications: Theoretical implications and efficient approximation S Wu, P Angelikopoulos, JL Beck, P Koumoutsakos ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B …, 2019 | 38 | 2019 |
| Fusing heterogeneous data for the calibration of molecular dynamics force fields using hierarchical Bayesian models S Wu, P Angelikopoulos, G Tauriello, C Papadimitriou, P Koumoutsakos The Journal of chemical physics 145 (24), 2016 | 38 | 2016 |
| Bayesian uncertainty quantification and propagation for discrete element simulations of granular materials PE Hadjidoukas, P Angelikopoulos, D Rossinelli, D Alexeev, ... Computer Methods in Applied Mechanics and Engineering 282, 218-238, 2014 | 38 | 2014 |
Petros KoumoutsakosHarvard UniversityVerified email at seas.harvard.edu