arXiv subjects

Rigoberto Hernandez

Publications and source records attributed to Rigoberto Hernandez.

At least 19 recordsLinked to original sources

arXiv2026

Polymer-Linked Nanoparticle Networks Running on Heat Can Act as Computing Devices

Developing physical neural network (PNN) hardwares is important to next generation artificial intelligence systems. Phononic devices-using heat current to encode and process information-is one of the solutions to neuromorphic computing. In this work, we back map an artificial neural network (ANN) into a PNN simulation model using polymer networked nanoparticles (PNNPs). Our atomistic simulation results demonstrate that the polymer linked nanoparticle networks can potentially realize information processing using heat current. Using high-throughput molecular dynamics (MD) simulations and the trust region Bayesian optimization (TuRBO) methods, we tune the plasticity of polymer linkers and the temperatures of nanoparticles to optimize the performance of the PNNP machines, which is similar to tune the weights and bias in ANNs. After 5 rounds of high-throughput MD simulations, we show that the PNNP machines have improved in performance. We also use a testing data set to verify the heat flow outputs from the top 5 PNNP machines in each round.

physics.chem-ph↗

arXiv2026

Binding Affinity between Polymer Dots (Pdots) and Ovalbumin Protein at Varying pH

Determining the binding mechanisms between polymer dots and proteins is important for developing novel nanotechnologies for biomedicine and bioimaging. In this work, we use all-atom molecular dynamics simulations to determine the binding affinity of Pdots with ovalbumin protein at pH = 7 and 1. The selected Pdots are mixtures of Poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-co-(1,4-benzo-(2,1',3)-thiadiazole)] (PFBT) and poly(styrene/maleic anhydride) (PSMA) with varying composition. At pH = 7, the Pdots have a net negative charge due to the COO- functional groups on the PFBT, and the protein also has a net negative charge. At pH = 1, the Pdots are charge neutral with PFBT containing only COOH functional groups, and the protein also has a net positive charge. We sample the initial position of the protein by varying its initial position through all 6 orientations of a cube. For each orientation, we pull the protein towards the PFBT region of the Pdot. We compare the Coulombic and Lennard-Jones interaction energies for the 6 different interacting faces and two selected pH values. We find that the LJ interaction energies are similar among all 12 of these cases. The measured Coulombic interaction energies suggest that pH = 1 has better binding affinity than pH = 7. The potentials of mean force (PMF) along the pulling coordinates differ with pH. The PMFs from 2 of the 6 initial configurations at pH = 1 are negative whereas none of them are negative at pH = 7, confirming the preferred binding affinity when pH = 1. One of the faces at pH = 1 has the lowest PMF of about -30 kcal/mol, which is much lower than about 6 kcal/mol seen for the lowest case at pH = 7. Comparison of protein residue charge distributions at pH = 7 and 1 further shows that the electrostatic interaction is critical to the binding affinity, and negatively charged residues reduce at pH = 7 does not bind to negatively charged Pdot.

physics.chem-ph↗

arXiv2026

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

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: Given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.

physics.chem-ph↗

arXiv2025

The need to implement FAIR principles in biomolecular simulations

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/) Now published as: Amaro, R.E., et al. The need to implement FAIR principles in biomolecular simulations. Nat Methods (2025) https://doi.org/10.1038/s41592-025-02635-0

q-bio.BM↗

arXiv2025

DNA Origami Nanostructures Observed in Transmission Electron Microscopy Images can be Characterized through Convolutional Neural Networks

Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional neural network (CNN) models can characterize DNA origami nanostructures employed in programmable self-assembling, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models -- viz. AlexNet, GoogLeNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 -- to characterize the ligation number of DNA origami nanostructures in transmission electron microscopy (TEM) images. We first pre-train CNN models using a large image dataset of 720 images from our coarse-grained (CG) molecular dynamics (MD) simulations. Then, we fine-tune the pre-trained CNN models, using a small experimental TEM dataset with 146 TEM images. All CNN models were found to have similar computational time requirements, while their model sizes and performances are different. We use 20 test MD images to demonstrate that among all of the pre-trained CNN models ResNet50 and VGG16 have the highest and second highest accuracies. Among the fine-tuned models, VGG16 was found to have the highest agreement on the test TEM images. Thus, we conclude that fine-tuned VGG16 models can quickly characterize the ligation number of nanostructures in large TEM images.

physics.chem-ph↗

arXiv2022

Transition state theory characterizes thin film macrospin dynamics driven by an oscillatory magnetic field: Inertial effects

Understanding the magnetization switching process in ferromagnetic thin films is essential for many technological applications. We investigate the effects of periodic driving via magnetic fields on a macrospin system under explicit consideration of inertial dynamics. This is usually achieved by extending the Landau-Lifshitz-Gilbert equation with a term including the second time derivative of the magnetization. The dynamics of the magnetization switching can then be characterized by its switching rate. We apply methods from transition state theory for driven systems to resolve the rate of magnetization switching in this general case. In doing so, we find that magnetization exhibits resonance-like behavior under certain driving conditions, and it can be affected strongly by the system's relaxation rate.

cond-mat.mtrl-sci↗

arXiv2022

Mean first-passage times for solvated LiCN isomerization at intermediate to high temperatures

The behavior of a particle in a solvent has been framed using stochastic dynamics since the early theory of Kramers. A particle in a chemical reaction reacts slower in a diluted solvent because of the lack of energy transfer via collisions. The flux-over-population reaction rate constant rises with increasing density before falling again for very dense solvents. This Kramers turnover is observed in this paper at intermediate and high temperatures in the backward reaction of the LiNC $\rightleftharpoons$ LiCN isomerization via Langevin dynamics and mean first-passage times (MFPTs). It is in good agreement with the Pollak-Grabert-Hänggi (PGH) reaction rates at lower temperatures. Furthermore, we find a square root behavior of the reaction rate at high temperatures and have made direct comparisons of the methods in the intermediate- and high- temperature regimes; all suggesting increased ranges in accuracy of both the PGH and MFPT approaches.

physics.chem-ph↗

arXiv2021

On the stability of satellites at unstable libration points of sun-planet-moon systems

The five libration points of a sun-planet system are stable or unstable fixed positions at which satellites or asteroids can remain fixed relative to the two orbiting bodies. A moon orbiting around the planet causes a time-dependent perturbation on the system. Here, we address the sense in which invariant structure remains. We employ a transition state theory developed previously for periodically driven systems with a rank-1 saddle in the context of chemical reactions. We find that a satellite can be parked on a so-called time-periodic transition state trajectory -- which is an orbit restricted to the vicinity of the libration point L2 for infinitely long time -- and investigate the stability properties of that orbit.

astro-ph.EP↗

arXiv2021

Transition state dynamics of a driven magnetic free layer

Magnetization switching in ferromagnetic structures is an important process for technical applications such as data storage in spintronics, and therefore the determination of the corresponding switching rates becomes essential. We investigate a free-layer system in an oscillating external magnetic field resulting in an additional torque on the spin. The magnetization dynamics including inertial damping can be described by the phenomenological Gilbert equation. The magnetization switching between the two stable orientations on the sphere then requires the crossing of a potential region characterized by a moving rank-1 saddle. We adopt and apply recent extensions of transition state theory for driven systems to compute both the time-dependent and average switching rates of the activated spin system in the saddle region.

cond-mat.mtrl-sci↗

arXiv2021

Controlling reaction dynamics in chemical model systems through external driving

The rate of a chemical reaction can often be determined by the properties of a rank-1 saddle and the associated transition state separating reactants and products. We have found evidence that such rates can be controlled and even enhanced by external driving in at least one such system. Specifically, we analyze a reactive model in two degrees of freedom that has been used earlier to describe driven chemical reactions. Therein, changes in the external driving can lead to a local maximum of the decay rate constant or even to bifurcations of periodic trajectories on the normally hyperbolic invariant manifold (NHIM) corresponding to the transition state. Inspired by these bifurcations, we show that in this case, the dynamics on the NHIM can be connected to the geometry of reactive trajectories and to reaction probabilities of Maxwell-Boltzmann distributed reactant ensembles.

physics.chem-ph↗

arXiv2021

Dynamics and decay rates of a time-dependent two-saddle system

The framework of transition state theory (TST) provides a powerful way for analyzing the dynamics of physical and chemical reactions. While TST has already been successfully used to obtain reaction rates for systems with a single time-dependent saddle point, multiple driven saddles have proven challenging because of their fractal-like phase space structure. This paper presents the construction of an approximately recrossing-free dividing surface based on the normally hyperbolic invariant manifold in a time-dependent two-saddle model system. Based on this, multiple methods for obtaining instantaneous (time-resolved) decay rates of the underlying activated complex are presented and their results discussed.

physics.chem-ph↗

arXiv2021

Identifying Reaction Pathways in Phase Space via Asymptotic Trajectories

In this paper, we revisit the concepts of the reactivity map and the reactivity bands as an alternative to the use of perturbation theory for the determination of the phase space geometry of chemical reactions. We introduce a reformulated metric, called the asymptotic trajectory indicator, and an efficient algorithm to obtain reactivity boundaries. We demonstrate that this method has sufficient accuracy to reproduce phase space structures such as turnstiles for a 1D model of the isomerization of ketene in an external field. The asymptotic trajectory indicator can be applied to higher dimensional systems coupled to Langevin baths as we demonstrate for a 3D model of the isomerization of ketene.

physics.chem-ph↗

arXiv2021

Thermal decay rates of an activated complex in a driven model chemical reaction

Recent work has shown that in a non-thermal, multidimensional system, the trajectories in the activated complex possess different instantaneous and time-averaged reactant decay rates. Under dissipative dynamics, it is known that these trajectories, which are bound on the normally hyperbolic invariant manifold (NHIM), converge to a single trajectory over time. By subjecting these dissipative systems to thermal noise, we find fluctuations in the saddle-bound trajectories and their instantaneous decay rates. Averaging over these instantaneous rates results in the decay rate of the activated complex in a thermal system. We find, that the temperature dependence of the activated complex decay in a thermal system can be linked to the distribution of the phase space resolved decay rates on the NHIM in the non-dissipative case. By adjusting the external driving of the reaction, we show that it is possible to influence how the decay rate of the activated complex changes with rising temperature.

physics.chem-ph↗

arXiv2020

Influence of external driving on decays in the geometry of the LiCN isomerization

The framework of transition state theory relies on the determination of a geometric structure identifying reactivity. It replaces the laborious exercise of following many trajectories for a long time to provide chemical reaction rates and pathways. In this paper, recent advances in constructing this geometry even in time-dependent systems are applied to the LiCN $\rightleftharpoons$ LiNC isomerization reaction, driven by an external field. We obtain decay rates of the reactant population close to the transition state by exploiting local properties of the dynamics of trajectories in and close to it. We find that the external driving has a large influence on these decay rates when compared to the non-driven isomerization reaction. This, in turn, provides renewed evidence for the possibility of controlling chemical reactions, like this one, through external time-dependent fields.

physics.chem-ph↗

arXiv2020

Dynamics and bifurcations on the normally hyperbolic invariant manifold of a periodically driven system with rank-1 saddle

In chemical reactions, trajectories typically turn from reactants to products when crossing a dividing surface close to the normally hyperbolic invariant manifold (NHIM) given by the intersection of the stable and unstable manifolds of a rank-1 saddle. Trajectories started exactly on the NHIM in principle never leave this manifold when propagated forward or backward in time. This still holds for driven systems when the NHIM itself becomes time dependent. We investigate the dynamics on the NHIM for a periodically driven model system with two degrees of freedom by numerically stabilizing the motion. Using Poincaré surfaces of section we demonstrate the occurrence of structural changes of the dynamics, \emph{viz.}, bifurcations of periodic transition state (TS) trajectories when changing the amplitude and frequency of the external driving. In particular, periodic TS trajectories with the same period as the external driving but significantly different parameters---such as mean energy---compared to the ordinary TS trajectory can be created in a saddle-node bifurcation.

physics.chem-ph↗

arXiv2020

Phase-space resolved rates in driven multidimensional chemical reactions

Chemical reactions in multidimensional driven systems are typically described by a time-dependent rank-1 saddle associated with one reaction and several orthogonal coordinates (including the solvent bath). To investigate reactions in such systems, we develop a fast and robust method ---viz., local manifold analysis (LMA)--- for computing the instantaneous decay rate of reactants. Specifically, it computes the instantaneous decay rates along saddle-bound trajectories near the activated complex by exploiting local properties of the stable and unstable manifold associated with the normally hyperbolic invariant manifold (NHIM). The LMA method offers substantial reduction of numerical effort and increased reliability in comparison to direct ensemble integration. It provides an instantaneous flux that can be assigned to every point on the NHIM and which is associated with a trajectory ---regardless of whether it is periodic, quasi-periodic, or chaotic--- that is bound on the NHIM. The time average of these fluxes in the driven system corresponds to the average rate through a given local section containing the corresponding point on the NHIM. We find good agreement between the results of the LMA and direct ensemble integration obtained using numerically constructed, recrossing-free dividing surfaces.

physics.chem-ph↗

arXiv2020

Neural network approach for the dynamics on the normally hyperbolic invariant manifold of periodically driven systems

Chemical reactions in multidimensional systems are often described by a rank-1 saddle, whose stable and unstable manifolds intersect in the normally hyperbolic invariant manifold (NHIM). Trajectories started on the NHIM in principle never leave this manifold when propagated forward or backward in time. However, the numerical investigation of the dynamics on the NHIM is difficult because of the instability of the motion. We apply a neural network to describe time-dependent NHIMs and use this network to stabilize the motion on the NHIM for a periodically driven model system with two degrees of freedom. The method allows us to analyze the dynamics on the NHIM via Poincaré surfaces of section (PSOS) and to determine the transition state (TS) trajectory as a periodic orbit with the same periodicity as the driving saddle, viz. a fixed point of the PSOS surrounded by near-integrable tori. Based on Transition State Theory and a Floquet analysis of a periodic TS trajectory we compute the rate constant of the reaction with significantly reduced numerical effort compared to the propagation of a large trajectory ensemble.

physics.chem-ph↗

arXiv2019

Invariant Manifolds and Rate Constants in Driven Chemical Reactions

Reaction rates of chemical reactions under nonequilibrium conditions can be determined through the construction of the normally hyperbolic invariant manifold (NHIM) [and moving dividing surface (DS)] associated with the transition state trajectory. Here, we extend our recent methods by constructing points on the NHIM accurately even for multidimensional cases. We also advance the implementation of machine learning approaches to construct smooth versions of the NHIM from a known high-accuracy set of its points. That is, we expand on our earlier use of neural nets, and introduce the use of Gaussian process regression for the determination of the NHIM. Finally, we compare and contrast all of these methods for a challenging two-dimensional model barrier case so as to illustrate their accuracy and general applicability.

physics.chem-ph↗