arXiv subjects

Jorge R. Espinosa

Publications and source records attributed to Jorge R. Espinosa.

14 recordsLinked to original sources

arXiv2026

Biophysical Considerations for Rational Antibody and ADC Design

Antibody-based therapeutics-including antibody-drug conjugates (ADCs), bispecific antibodies, and novel formats-are reshaping oncology, yet key determinants of efficacy, safety, and manufacturability frequently emerge after conjugation and formulation. We argue that computational biophysics provides an underexploited framework to address this gap by connecting molecular interactions to biological outcomes. We highlight how molecular dynamics, coarse-grained simulations, and free energy calculations reveal how conjugation site, linker chemistry, and drug-antibody ratio reshape conformational landscapes. We emphasize structural coupling between antibody, linker, and payload, with implications for antigen binding, internalization, and developability. We propose that integrating physics-based modeling into development pipelines-alongside experimental validation-can reduce empirical iteration and de-risk translation. As force fields, and hybrid physics-machine-learning methods improve, this field is poised to become a central driver of next-generation ADC design.

cond-mat.soft↗

arXiv2026

ChemFit: A framework for automated high-dimensional model parameter optimization

The parameterization of simulation-based models is a central yet laborious task in computational chemistry and physics, often driven by human intuition and manual iteration. Automating this task necessitates the definition of suitable objective functions, which tend to be expensive to evaluate, noisy, non-differentiable, or composed of heterogeneous contributions originating from separate sets of simulations. Gradient-free and black-box optimization algorithms are powerful tools which are particularly well-suited to minimizing such objective functions. Here, we introduce ChemFit, a flexible Python framework for the definition, composition, and massively concurrent evaluation of simulation-based objective functions, which is designed to operate in conjunction with these algorithms. We demonstrate the broad applicability of this approach by using ChemFit for three representative examples of increasing complexity and real-world relevance. First, we obtain the parameters of the Lennard-Jones potential for liquid argon from experimental measurements of the density. Second, we parameterize a polarizable and flexible potential energy function to reproduce the structure of small H$_2$O clusters obtained from density functional theory calculations. Finally, we tune a small subset of the parameters of a residue-level coarse-grained protein force-field, with the goal to reproduce the experimental critical solution temperature of the low complexity domain of the wild-type hnRNPA1 sequence and an arginine-enriched mutant of this protein. hnRNPA1 is an RNA-binding protein linked to amyotrophic lateral sclerosis. Together, these examples illustrate how ChemFit enables scalable, reproducible, and optimizer-agnostic parameter fitting for broadly applicable multiscale models.

physics.chem-ph↗

arXiv2026

Thermodynamic Descriptors from Molecular Dynamics as Machine Learning Features for Extrapolable Property Prediction

The limited extrapolative power of structure-based machine learning (ML) models is a critical bottleneck in chemical discovery, particularly for industrial R&D, where navigating uncharted chemical space to find next-generation materials or drugs is paramount. These models, reliant on structural descriptors or graph neural networks (GNNs), often fail when predicting properties for molecules with novel chemotypes. Here, we introduce a physics-augmented ML framework that overcomes this limitation. Our approach replaces conventional structural inputs with thermodynamic properties such as cohesive energy, heat of vaporization, and density, derived directly from molecular dynamics (MD) simulations. While performing comparably to structure-based models on known organic compounds, our method uniquely maintains low error when extrapolating to dissimilar chemical spaces. Crucially, it accurately predicts boiling points for entire chemical classes absent from the training set, including inorganic compounds, salts, and molecules with elements such as Si, B, and Te. By learning from the intermolecular forces that govern phase transitions, our framework provides a more fundamental and generalizable strategy for molecular property prediction, enabling chemical exploration beyond established structural domains.

physics.chem-ph↗

arXiv2026

Understanding How Synthetic Impurities Affect Glyphosate Solubility and Crystal Growth Using Free Energy Calculations and Molecular Dynamics Simulations

Glyphosate, the most widely used herbicide worldwide, crystallizes through complex intermolecular interactions that are strongly influenced by synthesis-derived impurities. Understanding this process at the molecular scale is critical for optimizing production, ensuring product quality, and assessing environmental impact. Here, we employ direct coexistence molecular dynamics simulations and free energy calculations to elucidate how glycine-a prevalent synthesis byproduct-modulates glyphosate solubility and crystal growth in aqueous solutions. Our simulations identify two major mechanisms by which glycine hinders crystallization. First, direct coexistence simulations show that glycine preferentially adsorbs at crystal surfaces, hindering glyphosate attachment and slowing growth. Second, free energy calculations demonstrate that glycine enhances glyphosate solubility, reducing the supersaturation driving force to incorporate into the crystal phase. Experimental measurements corroborate our predictions, confirming both enhanced solubility and reduced crystallization kinetics in glycine-bearing systems. These findings establish that glycine-typically considered an inert impurity-actively disrupts glyphosate crystallization by promoting its dissolution. More broadly, this integrated computational-experimental approach highlights the power of molecular simulations to disentangle impurity effects, interfacial phenomena, and solution thermodynamics in crystallization, providing molecular-level insights for optimizing industrial protocols and predicting agrochemical behavior under relevant environmental conditions.

physics.chem-ph↗

arXiv2026

Roadmap for Condensates in Cell Biology

Biomolecular condensates govern essential cellular processes yet elude description by traditional equilibrium models. This roadmap, distilled from structured discussions at a workshop and reflecting the consensus of its participants, clarifies key concepts for researchers, funding bodies, and journals. After unifying terminology that often separates disciplines, we outline the core physics of condensate formation, review their biological roles, and identify outstanding challenges in nonequilibrium theory, multiscale simulation, and quantitative in-cell measurements. We close with a forward-looking outlook to guide coordinated efforts toward predictive, experimentally anchored understanding and control of biomolecular condensates.

physics.bio-ph↗

arXiv2025

Interfacial Free Energy as the Key to the Pressure-Induced Deceleration of Ice Nucleation

The avoidance of water freezing is the holy grail in the cryopreservation of biological samples, food, and organs. Fast cooling rates are used to beat ice nucleation and avoid cell damage. This strategy can be enhanced by applying high pressures to decrease the nucleation rate, but the physics behind this procedure has not been fully understood yet. We perform computer experiments to investigate ice nucleation at high pressures consisting in embedding ice seeds in supercooled water. We find that the slowing down of the nucleation rate is mainly due to an increase of the ice I-water interfacial free energy with pressure. Our work also clarifies the molecular mechanism of ice nucleation for a wide pressure range. This study is not only relevant to cryopreservation, but also to water amorphization and climate change modeling.

cond-mat.stat-mech↗

arXiv2024

Predictions of the interfacial free energy along the coexistence line from single-state calculations

The calculation of the interfacial free energy between two thermodynamic phases is crucial across various fields, including materials science, chemistry, and condensed matter physics. In this study, we apply an existing thermodynamic approach, the Gibbs-Cahn integration method, to determine the interfacial free energy under different coexistence conditions, relying on data from a single-state calculation at specified pressure and temperature. This approach developed by Laird et al. [J. Chem. Phys. 131, 114110 (2009)] reduces computational demand and enhances efficiency compared to methods that require separate measurements at each thermodynamic state. The integration scheme computes the excess interfacial free energy using unbiased NVT simulations, where the two phases coexist, to provide input for the calculations. We apply this method to the Lennard-Jones and mW water models for liquid-solid interfaces, as well as the Lennard-Jones and TIP4P/2005 models for liquid-vapor interfaces. Our results demonstrate the accuracy and effectiveness of this integration route for estimating the interfacial free energy along a coexistence line.

cond-mat.soft↗

arXiv2024

Solid-liquid interfacial free energy from computer simulations: Challenges and recent advances

The theory of interfacial properties in liquid-liquid or liquid-vapour systems is nearly 200 years old. The advent of computational tools has greatly advanced the field, mainly through the use of Molecular Dynamics simulations. Despite the successes and advances in the theory of interfacial phenomena for liquid-liquid systems, the study of solid-liquid interfaces remains a challenge both theoretically and experimentally. The main reason why the treatment of solid-liquid systems has fallen behind that of liquid-liquid systems is that there are complications that arise whenever an interface involving solid systems is considered involving both theory of the solid-liquid interface and the calculations using MD simulations. An example of the former is that, contrary to the liquid-liquid case, the interfacial properties of solids depend on the lattice orientation. The main complications in these calculations arise from the fact that for solids the ``mechanical route'' cannot be used. To overcome this problem, several numerical approaches were proposed. The main purpose of this review is to provide an overview of these different methodologies and to discuss their strengths and weaknesses. We classify these methodologies into two main groups: direct and indirect methods. Direct methods are those that can calculate directly the properties of interfaces, while in indirect approaches the properties of the interface are not the primary result of the simulations. We also included a discussion on the origin of the difficulties in considering solid interfaces from a thermodynamic point of view. In the second part of the review, we discuss two key related topics: nucleation theory and curved interfaces. They both represent an important problem in the study of interfaces and in the context of solid-liquid ones for which the research is still extremely active.

cond-mat.soft↗

arXiv2024

Variational Umbrella Seeding for Calculating Nucleation Barriers

In this work, we introduce Variational Umbrella Seeding, a novel technique for computing nucleation barriers. This new method, a refinement of the original seeding approach, is far less sensitive to the choice of order parameter for measuring the size of a nucleus. Consequently, it surpasses seeding in accuracy, and Umbrella Sampling in computational speed. We test the method extensively and demonstrate excellent accuracy for crystal nucleation of nearly hard spheres and of two distinct models of water: mW and TIP4P/ICE. This method can easily be extended to calculate nucleation barriers for homogeneous melting, condensation, and cavitation.

cond-mat.soft↗

arXiv2023

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here we utilize the Deep Potential methodology -- a machine learning approach -- to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K, and evaluate the Deep Potential model performance against experimental results and the semi-empirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model, due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 $\pm$ 0.008) nm at 296.4 K. Lastly, we identify that water molecules display a preferential orientation in the liquid-vapor interface, in which H atoms tend to point towards the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behaviour is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid-vapor coexistence and water cavitation.

cond-mat.soft↗

arXiv2023

Direct calculation of the planar NaCl-aqueous solution interfacial free energy at the solubility limit

Salty water is the most abundant electrolyte aqueous mixture on Earth, however, very little is known about the NaCl-saturated solution interfacial free energy. Here, we provide the first direct estimation of this magnitude for several NaCl crystallographic planes by means of the Mold Integration technique, a highly efficient computational method to evaluate interfacial free energies with anisotropic crystal resolution. Making use of the JC-SPC/E model, one of the most benchmarked force fields for NaCl/water solutions, we measure the interfacial free energy of four different planes, (100), (110), (111), and (11-2) with the saturated solution at normal conditions. We find high anisotropy between the different crystal orientations with values ranging from 100 to 150 mJ/m2 and the average value of the distinct planes being 137(20) mJ/m2. This value for the coexistence interfacial free energy is in reasonable agreement with previous extrapolations from nucleation studies. Our work represents a milestone in the computational calculation of interfacial free energies between ionic crystals and aqueous solutions.

cond-mat.soft↗

arXiv2021

Heterogeneous versus homogeneous crystal nucleation in hard spheres

Hard-sphere model systems are well-suited in both experiment and simulations to investigate fundamental aspects of the crystallization of fluids. In experiments on colloidal models of hard-sphere fluids, the uid is unavoidably at contact with the walls of the sample cell, where heterogeneous crystallization may take place. In this work we use simulations to investigate the competition between homogeneous and heterogeneous crystallization. We report simulations of wall-induced nucleation for different confining walls. Combining the results of these simulations with earlier studies of homogeneous allows us to asses the competition between homogeneous and heterogeneous nucleation as a function of wall type, fluid density and the system size. On at walls, heterogeneous nucleation will typically overwhelm homogeneous nucleation. However, even for surfaces randomly coated with spheres with a diameter that was some three times larger than that of the fluid spheres - as has been used in some experiments - heterogeneous nucleation is likely to be dominant for volume fractions smaller than 0.535. Only for a disordered coating that has the same structure as the liquid holds promise did we find the nucleation was likely to occur in the bulk. Hence, such coatings might be used to suppress heterogeneous nucleation in experiments. Finally, we report the apparent homogeneous nucleation rate taking into account the formation of crystallites both in the bulk and at the walls. We find that the apparent overall nucleation rates coincides with those reported in "homogeneous nucleation" experiments. This suggests that heterogeneous nucleation at the walls could partly explain the large discrepancies found between experimental measurements and simulation estimates of the homogeneous nucleation rate.

cond-mat.soft↗

arXiv2020

Homogeneous Ice Nucleation Rate in Water Droplets

To predict the radiative forcing of clouds it is necessary to know the rate with which ice homogeneously nucleates in supercooled water. Such rate is often measured in drops to avoid the presence of impurities. At large supercooling small (nanoscopic) drops must be used to prevent simultaneous nucleation events. The pressure inside such drops is larger than the atmospheric one by virtue of the Laplace equation. In this work, we take into account such pressure raise in order to predict the nucleation rate in droplets using the TIP4P/Ice water model. We start from a recent estimate of the maximum drop size that can be used at each supercooling avoiding simultaneous nucleation events [Espinosa et al. J. Chem. Phys., 2016]. We then evaluate the pressure inside the drops with the Laplace equation. Finally, we obtain the rate as a function of the supercooling by interpolating our previous results for 1 and 2000 bar [Espinosa et al. Phys. Rev. Lett. 2016] using the Classical Nucleation Theory expression for the rate. This requires, in turn, interpolating the ice-water interfacial free energy and chemical potential difference. The TIP4P/Ice rate curve thus obtained is in good agreement with most droplet-based experiments. In particular, we find a good agreement with measurements performed using nanoscopic drops, that are currently under debate. The successful comparison between model and experiments suggests that TIP4P/Ice is a reliable model to study the water-to-ice transition and that Classical Nucleation Theory is a good framework to understand it.

cond-mat.soft↗

arXiv2017

A simulation study of homogeneous ice nucleation in supercooled salty water

We use computer simulations to investigate the effect of salt on homogeneous ice nucleation. The melting point of the employed solution model was obtained both by direct coexistence simulations and by thermodynamic integration from previous calculations of the water chemical potential. Using a Seeding approach, in which we simulate ice seeds embedded in a supercooled aqueous solution, we compute the nucleation rate as a function of temperature for a 1.85 NaCl mole per water kilogram solution at 1 bar. To improve the accuracy and reliability of our calculations we combine Seeding with the direct computation of the ice-solution interfacial free energy at coexistence using the Mold Integration method. We compare the results with previous simulation work on pure water to understand the effect caused by the solute. The model captures the experimental trend that the nucleation rate at a given supercooling decreases when adding salt. Despite the fact that the thermodynamic driving force for ice nucleation is higher for salty water for a given supercooling, the nucleation rate slows down with salt due to a significant increase of the ice-fluid interfacial free energy. The salty water model predicts an ice nucleation rate that is in good agreement with experimental measurements, bringing confidence in the predictive ability of the model.

cond-mat.soft↗