About

Dr. Rigoberto Hernandez is the Gompf Family Professor in the Department of Chemistry at Johns Hopkins University, a position he has held since July 1, 2016. He also serves as the Director of the Open Chemistry Collaborative in Diversity Equity (OXIDE) since 2011. Prior to his current appointment, he was a Professor in the School of Chemistry and Biochemistry at Georgia Tech, where he co-founded and co-directed the Center for Computational Molecular Science and Technology. Hernandez holds a B.S.E. in Chemical Engineering and Mathematics from Princeton University (1989) and a Ph.D. in Chemistry from the University of California, Berkeley (1993). Born in Güinez, Havana, Cuba, he was raised and educated in the United States from primary school and is a U.S. citizen by birthright.

Research topics

• Computer Science
• Artificial Intelligence
• Optoelectronics
• Physics
• Chemistry
• Nanotechnology
• Materials science
• Data science
• Geography
• Geology
• Engineering
• Computational chemistry
• Thermodynamics
• Biological system
• Composite material
• Statistical physics
• Management science

Selected publications

• Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data

  Digital Discovery · 2026-01-01

  articleOpen access

  A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data.

  PublisherOA PDFDOI

• Polymer-networked engineered nanoparticles are primitives for neuromorphic computing

  The Journal of Chemical Physics · 2026-04-06

  articleSenior author

  The information flow through a combinatorial threshold linear network of polymer-networked engineered nanoparticle composites has been found to exhibit primitive neuromorphic computing behavior. Our systematic analysis of a 4-node, 1-sink network reveals specific conditions for the emergence of Limit Cycles (LCs), which could serve as a mechanism for information storage in affine networks. By examining various input profiles, we establish quantitative relationships between input parameters and the resulting LC characteristics. We demonstrate that peak amplitudes and frequencies of these oscillatory attractors can function as system outputs within specific input regimes, enabling predictable system input-output relationships for computational operations. We perturb the network to assess system robustness by introducing additional structural sinks (creating 5-node and 6-node structures). We find that certain network architectures maintain stable LC behavior despite structural modifications, suggesting the potential for scalability in more complex implementations.

  PublisherDOI

• Supplementary Material for "Polymer-networked engineered nanoparticles are primitives for neuromorphic computing"

  AIP Publishing · 2026-04-06

  articleOpen accessSenior author

  This Supplementary Material (SM) contains: • Description of the changes made to the original CTLN code by Curto and coworkers [48] • Specific values of θi creating the example attractors shown in Fig. 1 in the main text • Figures S1 - S6 contain examples of the attractors resulting from all the investigated input gradients in the 4-node network • Figure S7 contains the values of x5(0) resulting in Limit Cycles (LCs) in the 5-node network and the values of x6(0) resulting in LCs in the 6-node network. • Figures S8-S13 contain examples of the attractors resulting from applying an input gradient to all of the investigated in the 5- and 6-node networks.

  PublisherDOI

• Supplementary Material for "Polymer-networked engineered nanoparticles are primitives for neuromorphic computing"

  AIP Publishing · 2026-04-06

  articleOpen accessSenior author

  This Supplementary Material (SM) contains: • Description of the changes made to the original CTLN code by Curto and coworkers [48] • Specific values of θi creating the example attractors shown in Fig. 1 in the main text • Figures S1 - S6 contain examples of the attractors resulting from all the investigated input gradients in the 4-node network • Figure S7 contains the values of x5(0) resulting in Limit Cycles (LCs) in the 5-node network and the values of x6(0) resulting in LCs in the 6-node network. • Figures S8-S13 contain examples of the attractors resulting from applying an input gradient to all of the investigated in the 5- and 6-node networks.

  PublisherDOI

• The Role of Metal Complexation in the Unfolding Energetics of a Nudix Hydrolase

  Biochemistry · 2026-04-17

  articleOpen accessSenior authorCorresponding

  Dihydroneopterin triphosphate pyrophosphatase (DHNTPase) catalyzes an essential step in bacterial folate biosynthesis. A characteristic of the enzyme is that it can be stabilized by divalent cations. To better characterize the nature of its stabilization, we combine equilibrium denaturation with all-atom adaptive steered molecular dynamics (ASMD) on three forms of E. coli DHNTPase─viz apo (PDB: 5U7E), Co2+/SO42−-bound (PDB: 5U7F), and Ni2+/SO42−-bound (PDB: 5U7H)─and identify the structural features that govern the native structure’s resistance to unfolding. The metal–liganded forms of the enzyme were seen in experiments to unfold at a higher denaturant midpoint and with a slower rate than apo, indicating increased stability. ASMD yields the potential of mean force (PMF) profiles, and observables─such as native contacts Q, intrapeptide and protein–water H-bonds, residue distances, active-site spread, and site-resolved metal coordination─along a steered coordinate pulling the protein apart. Our findings support a pathway-specific mechanism in which the duration of active-site coherence (compact spread and intact coordination) is the dominant predictor of mechanical/chemical stability. Along the pulling coordinate, residues Glu 117 and Thr40, and metal–sulfate interactions are also seen to be levers for stabilization or disruption.

  PublisherDOI

• The need to implement FAIR principles in biomolecular simulations

  Nature Methods · 2025-04-01 · 53 citations

  articleOpen access
  PublisherOA PDFDOI

• Gold Nanoparticle Ligand Structure Investigated with Solution NMR: Effects of Ligand Length on Headgroup Dynamics and Ion Penetration

  Chemistry of Materials · 2025-06-18 · 2 citations

  article

  Herein, we report the synthesis of a library of 16 gold nanoparticle (AuNP) types (2, 4, 9, and 12 nm in diameter and appended with mercapto-(X-alkyl)-N,N,N-trimethylammonium bromide (MxTAB) ligands (X = 11, 16, 18, or 20)) and detailed characterization of their ligand shell with solution 1H NMR in deuterium oxide. The trimethylammonium headgroup is bulky, and the unique chemical shifts of its protons allow for systematic studies of ligand density and dynamics as a function of both nanoparticle size and ligand length for fully saturated surfaces. Chemical shift analysis of the solvent-exposed headgroup protons supports the notion that ligand headgroups pack closer together as the AuNP diameter increases for all ligands. Quantitative analysis shows that ligand density for the shorter ligands (MUTAB (X = 11) and MTAB (X = 16)) is dependent on nanoparticle size, ranging from ∼10 to ∼2 molecules/nm2 as the nanoparticle size increases, while ligand density is independent of size (∼2 molecules/nm2) for longer ligands (MOTAB (X = 18) and MITAB (X = 20)). T2 relaxation analysis shows less headgroup motion and therefore more ordering as both the NP diameter and the chain length increase. Gold etching experiments with potassium cyanide were performed to determine the ability of ions to penetrate the ligand layers; core protection and headgroup motion, as judged by T2, were negatively correlated for the two shorter ligands but not correlated with the two longer ligands. Molecular dynamics simulations indicated that the longer ligands have a stronger tendency to form ligand islands on curved surfaces due to increased van der Waals interactions between the alkane portions of ligands, suggesting that the presence of patchy ligand islands displays hydrophobic character that prevents the cyanide ion from penetrating the AuNP cores. The relationship between ligand length and nanoparticle diameter/curvature leads to rudimentary predictions of ligand dynamics.

  PublisherDOI

• Emergence of Polymer-Networked Nanoparticle Structures as Primitive Neuromorphic Computing States

  The Journal of Physical Chemistry A · 2025-08-30 · 2 citations

  articleOpen accessSenior authorCorresponding

  Polymer-networked nanoparticles are a promising alternative to silicon semiconductors for the realization of neuromorphic computing platforms. Variations in the interaction between gold nanoparticles (AuNPs) and polyelectrolyte linkers lead to the controlled formation of engineered nanoparticle network (ENPN) structures exhibiting a broad range of topologies and dynamics. Using dissipative particle dynamics (DPD) simulations, we designed triblock copolymers with polyelectrolyte ends that can selectively attach to each of two AuNPs and bridged them together through a middle polymer segment (or block). We leverage our earlier finding that AuNPs have well-defined valencies─that is, an optimal number of polymers that can fit (or fill) their surface, for a specific choice of the outer blocks at a given polymer length. The precise selection of the AuNP valence allows for controlled binding between the polymers and AuNPs. Meanwhile, the choice of the middle block enables control over internanoparticle spacing and network topology. We found that ENPNs can achieve distinct and stable states, satisfying a necessary condition for primitive neuromorphic computing. By swapping the surface coating ligand from citrate to mercaptopropionic acid (MPA), the valence on a given nanoparticle is also increased. Thus, we found that the selection of the surface coating consequently affects the designed ENPN structures, allowing for more flexibility in searching for the optimal components.

  PublisherOA PDFDOI

• In vivo transformations of positively charged nanoparticles alter the formation and function of RuBisCO photosynthetic protein corona

  Nature Nanotechnology · 2025-06-03 · 7 citations

  article
  PublisherDOI

• Cytochrome C Facilitates Binding Between Lipid Bilayers and Citrate-Coated Gold Nanoparticles in Coarse-Grained Simulations

  ChemRxiv · 2025-03-17

  preprintOpen accessSenior author

  Characterization and prediction of the interactions between engineered nanoparticles (ENPs), proteins, and biological membranes is critical for advancing applications to nanomedicine and nanomanufacturing while mitigating nanotoxicological risks. In this work, we employ a coarse-grained dissipative particle dynamics (DPD) simulation to investigate the interactions among cytochrome c (CytC), lipid bilayers, and citrate-coated gold nanoparticles (AuNPs). We updated the DPD potential to accurately represent binding potentials between molecules, and validated the model relative to an all-atom representation. The DPD simulations successfully replicate experimental observations: CytC facilitates the binding of citrate-coated AuNPs to lipid bilayers composed of 90% dioleoylphosphatidylcholine (DOPC) mixed with 10\% stearoylphosphatidylinositol (SAPI) or 10% tetraoleoyl cardiolipin (TOCL)but not to pure 100% DOPC bilayers. In addition, the simulations reveal nuanced differences in binding preferences between CytC, the lipid bilayers, and the ENP, at a scale that is not presently directly observable in experiments. Specifically, we found that the surface coating of the nanoparticles---%viz variations in the CytC surface density---%affects the protein-mediated binding with the bilayers.Such a molecular-sensitive result underscores the utility of DPD simulations in simulating complex biological systems.

  PublisherOA PDFDOI

Recent grants

• Nonequilibrium Molecular Dynamics Simulations of Structured Colloidal Particles

  NSF · $430k · 2011–2015

• Nonequilibrium Molecular Dynamics: Theory, Simulations and Applications

  NSF · $435k · 2016–2020

• Nonequilibrium Molecular Dynamics: Dynamical Consistency Across Scales

  NSF · $497k · 2021–2025

• Collaborative Research: Autonomous Computing Materials

  NSF · $330k · 2019–2021

• Simulations of Driven Colloidal Suspensions

  NSF · $400k · 2008–2012

Frequent coauthors

• Srikant K. Iyer

  Johns Hopkins University

  72 shared

• K. S. Bjorkman

  University of Toledo

  64 shared

• Ashley Donovan

  American Chemical Society

  64 shared

• Marilyne Stains

  University of Virginia

  64 shared

• Peter K. Dorhout

  Iowa State University

  64 shared

• Philip W. Hammer

  Johns Hopkins University

  64 shared

• Andrew L. Feig

  64 shared

• Jennifer L. Ross

  Syracuse University

  64 shared

Awards & honors

• NSF CAREER Award (1997)
• Research Corporation Cottrell Scholar Award (1999)
• Alfred P. Sloan Fellow Award (2000)
• Humboldt Research Fellowship (2006-07)
• ACS Award for Encouraging Disadvantaged Students into Career…