UT Shield

Force fields, Coarse graining, Machine Learning

Nanofluidics – Force Fields, Coarse-Graining, Machine Learning:

  • H. Oliaei, N. R. Aluru, “IR Spectra for the EMIM-TFSI Ion Pair Using Deep Potentials”, Journal of Chemical Theory and Computation, Vol. 21, No. 13, pp. 6622-6632, July 2025. DOI: 10.1021/acs.jctc.5c00187 (link)
  • I. Nadkarni, J. P. Martínez Cordeiro, N. R. Aluru, “Molecular Denoising Using Diffusion Models with Physics-Informed Priors”, The Journal of Physical Chemistry Letters, Vol. 16, No. 12, pp. 3078-3085, March 2025. DOI: 10.1021/acs.jpclett.5c00274 (link)
  • J. P. Martínez Cordeiro, N. R. Aluru, “Thermostatting nonequilibrium systems: A thermal energy constraint for systems under directive perturbations”, The Journal of Chemical Physics, Vol. 162, No. 12, Art. No. 124112, March 2025. DOI: 10.1063/5.0257970 (link)
  • I. Nadkarni, J. Jeong, B. Yalcin, N. R. Aluru, “Modulating Coarse-Grained Dynamics by Perturbing Free Energy Landscapes”, The Journal of Physical Chemistry A, Vol. 128, No. 46, pp. 10029-10040, Nov 2024. DOI: 10.1021/acs.jpca.4c04530 (link)
  • H. Wu, C. Liang, J. Jeong, N. R. Aluru, “From ab initio to continuum: Linking multiple scales using deep-learned forces”, The Journal of Chemical Physics, Vol. 159, No. 18, Art. No. 184108, Nov 2023. DOI: 10.1063/5.0166927 (link)
  • I. Nadkarni, H. Wu, N. R. Aluru, “Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement”, Journal of Chemical Theory and Computation, Vol. 19, No. 20, pp. 7358-7370, Oct 2023. DOI: 10.1021/acs.jctc.3c00633 (link)
  • A. Moradzadeh, H. Oliaei, N. R. Aluru, “Topology-Based Phase Identification of Bulk, Interface, and Confined Water Using an Edge-Conditioned Convolutional Graph Neural Network”, The Journal of Physical Chemistry C, Vol. 127, No. 5, pp. 2612-2621, 2023. DOI: 10.1021/acs.jpcc.2c07423 (link)
  • H. Wu, N. R. Aluru, “Deep learning-based quasi-continuum theory for structure of confined fluids”, The Journal of Chemical Physics, Vol. 157, No. 8, Art. No. 084121, 2022. DOI: 10.1063/5.0096481 (link)
  • A. Moradzadeh, N. R. Aluru, “Many-Body Neural Network-Based Force Field for Structure-Based Coarse-Graining of Water”, The Journal of Physical Chemistry A, Vol. 126, No. 12, pp. 2031-2041, 2022. DOI: 10.1021/acs.jpca.1c09786 (link)
  • J. Jeong, A, Moradzadeh, N. R. Aluru, “Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining”, The Journal of Physical Chemistry A, Vol. 126, No. 9, pp. 1562-1570, 2022. DOI: 10.1021/acs.jpca.1c10865 (link)
  • A. Moradzadeh, N. R. Aluru, “Understanding simple liquids through statistical and deep learning approaches”, Journal of Chemical Physics, Vol. 154, No. 20, Art. No. 204503, 2021. DOI: 10.1063/5.0046226 (link) 
  • S. Mashayak and N. R. Aluru, “A multiscale model for charge inversion in electric double layers”, Journal of Chemical Physics, Vol. 148, No. 21, Art. No. 214102, 2018. DOI: 10.1063/1.5026975 
  • R. Bhadauria and N. R. Aluru, “A multiscale transport model for non-classical nanochannel electroosmosis”, Journal of Chemical Physics, Vol. 147, No. 21, Art. No. 214105, 2017. DOI: 10.1063/1.5005127 
  • R. Bhadauria and N. R. Aluru, “Multiscale modeling of electroosmotic flow: Effects of discrete ion, enhanced viscosity, and surface friction”, Journal of Chemical Physics, Vol. 146, No. 18, Art. No. 184106, 2017. 
  • M. H. Motevaselian and N. R. Aluru, “An EQT-based cDFT approach for thermodynamic properties of confined fluid mixtures”, Journal of Chemical Physics, Vol. 146, No. 15, Art. No. 154102, 2017.
  • S. Mashayak and N. R. Aluru, “Langevin-Poisson-EQT: A dipolar solvent based quasi-continuum approach for electric double layers”, Journal of Chemical Physics, Vol. 146, No. 4, Art. No. 044108, 2017.
  • R. Bhadauria and N. R. Aluru, “A multiscale transport model for Lennard-Jones binary mixtures based on interfacial friction”, Journal of Chemical Physics, Vol. 145, No. 7, Art. No. 074115, 2016. 
  • R. Bhadauria, T. Sanghi and N. R. Aluru, “Interfacial friction based quasi-continuum hydrodynamical model for nanofluidic transport of water”, Journal of Chemical Physics, Vol. 143, No. 17, Art. No. 174702, 2015.
  • M. H. Motevaselian, S. Y. Mashayak and N. R. Aluru, “An EQT-based cDFT approach for a confined Lennard-Jones fluid mixture”, Journal of Chemical Physics, Vol. 143, No. 12, Art. No. 124106, 2015.
  • S. Y. Mashayak, M. H. Motevaselian and N. R. Aluru, “An EQT-cDFT approach to determine thermodynamic properties of confined fluids”, Journal of Chemical Physics, Vol. 142, No. 24, Art. No. 244116, 2015.
  • T. Sanghi and N. R. Aluru, “Thermal noise in confined fluids”, Journal of Chemical Physics, Vol. 141, No. 17, Art. No. 174707, 2014. 
  • R. Bhadauria and N. R. Aluru, “A quasi-continuum hydrodynamic model for slit shaped nanochannel flow”, Journal of Chemical Physics, Vol. 139, No. 7, Art. No. 074109, 2013.
  • T. Sanghi and N. R. Aluru, “A combined quasi-continuum/Langevin equation approach to study the self-diffusion dynamics of confined fluids”, Journal of Chemical Physics, Vol. 138, No. 12, Art. No. 124109, 2013. 
  • A. V. Raghunathan, J. H. Park and N. R. Aluru, “Interatomic potential-based semiclassical theory for Lennard-Jones fluids”, Journal of Chemical Physics, Vol. 127, No. 17, Art. No. 174701, 2007. 
  • S. Joseph and N. R. Aluru, “Hierarchical multiscale simulation of electrokinetic transport in silica nanochannels at the point of zero charge”, Langmuir, Vol. 22, No. 21, pp. 9041-9051, 2006.
  • R. Qiao and N. R. Aluru, “Multiscale simulation of electroosmotic transport using embedding techniques”, International Journal for Multiscale Computational Engineering, Vol. 2, No. 2, pp. 173-188, 2004.