Extended Time Scales Research Articles

Probing the Molecular Interactions of Electrochemically Reduced Vitamin B2 with CO2.

The electrochemical reduction of riboflavin (vitamin B2) in a dimethyl sulfoxide solvent was examined under a CO2 atmosphere and compared with results under an argon atmosphere. Variable-scan-rate cyclic voltammetry combined with controlled potential electrolysis (CPE) and analysis by UV-vis and EPR spectroscopies provided insights into the nature of interactions of reduced flavins with dissolved CO2. Reductive exhaustive CPE experiments under CO2 indicated an overall two-electron stoichiometry, compared to one-electron reduction under an argon atmosphere, due to the lowering of the formal one-electron reduction potential of the flavin radical anion to form the dianion, which can be rationalized by riboflavin-CO2 molecular interactions. UV-vis spectroscopic measurements confirmed complete chemical reversibility of the redox transformations over extended time scales. Digital simulation modeling of the voltammetric data enabled extraction of thermodynamic and kinetic parameters for the proposed mechanism, comprising multiple proton-coupled electron transfer steps, diamagnetic anions, radical anions, and neutral radical intermediates enroute to the fully reduced state, as well as evidence of a long-lived solution phase complex of the reduced riboflavin with CO2.

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by com- mittee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

Potentiation of active locomotor state by spinal-projecting serotonergic neurons.

Animals produce diverse motor actions that enable expression of context-appropriate behaviors. Neuromodulators facilitate behavioral flexibility by altering the temporal dynamics and output of neural circuits. Discrete populations of serotonergic (5-HT) neurons target circuits in the brainstem and spinal cord, but their role in the control of motor behavior is unclear. Here we define the pre- and post-synaptic organization of the spinal-projecting serotonergic system and define a role in locomotor control. We show that while forebrain-targeting 5-HT neurons decrease their activity during locomotion, subpopulations of spinal projecting neurons increase their activity in a context-dependent manner. Optogenetic activation of ventrally projecting 5-HT neurons does not trigger initiation of movement, but rather enhances the speed and duration of ongoing locomotion over extended time scales. These findings indicate that the descending serotonergic system potentiates locomotor output and demonstrate a role for serotonergic neurons in modulating the temporal dynamics of motor circuits.

Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning.

Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they typically necessitate well-defined collective variables (CVs), posing a significant challenge. Machine learning offers promising solutions but typically requires rich training data encompassing the entire free energy surface (FES). In this work, we introduce an automated iterative pipeline designed to mitigate these limitations. Our protocol first utilizes a CV-free count-based adaptive sampling method to generate a data set rich in rare events. From this data set, slow modes are identified using Koopman-reweighted time-lagged independent component analysis (KTICA), which are subsequently leveraged by on-the-fly probability enhanced sampling (OPES) to efficiently explore the FES. The effectiveness of our pipeline is demonstrated and further compared with the common Markov State Model (MSM) approach on two model systems with increasing complexity: alanine dipeptide (Ala2) and deca-alanine (Ala10), underscoring its applicability across diverse biomolecular simulations.

OceanNet: a principled neural operator-based digital twin for regional oceans

While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for regional sea-suface height emulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill compared to a state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate initial steps for physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models.

Phonon excitations of Floquet-driven superfluids in a tilted optical lattice

Tilted lattice potentials with periodic driving play a crucial role in the study of artificial gauge fields and topological phases with ultracold quantum gases. However, driving-induced heating and the growth of phonon modes restrict their use for probing interacting many-body states. Here, we experimentally investigate phonon modes and interaction-driven instabilities of superfluids in the lowest band of a shaken optical lattice. We identify stable and unstable parameter regions and provide a general resonance condition. In contrast to the high-frequency approximation of a Floquet description, we use the micromotion of the superfluids to analyze the growth of phonon modes from slow- to fast-driving frequencies. The model describes phonon excitations in both resonantly and nonresonantly driven systems, with or without a tilted potential. Our observations enable the prediction of stable parameter regimes for quantum-simulation experiments aimed at studying driven systems with strong interactions over extended time scales. Published by the American Physical Society 2024

SedaDNA reveals mid-to late Holocene aquatic plant and algae changes in Luanhaizi Lake on the Tibetan Plateau

Aquatic plants and algae are the primary producers in lake ecosystems, providing the organic matter and oxygen necessary for the nutrients and energy of most organisms. Climatic and environmental changes may have irreversible impacts on aquatic plants and algae communities, while direct observation and experiments can only provide a short-term perspective. As a record of the long-term variation of the ecosystem, sedimentary ancient DNA (sedaDNA) could offer evidence about aquatic plants and algae change over extended time scales and improve the taxonomic resolution than the traditional morphology methods such as pollen and macrofossil. In this study, we employed metagenomics in sedaDNA from Luanhaizi Lake (northeastern Tibetan Plateau) since 8.2 cal kyr BP, to characterize the aquatic plant and algae communities' response to the climatic and environmental change over an extended historical timeline. The results demonstrate that during 8130–4620 cal yr BP, pondweed (Stuckenia) was the dominating aquatic plant in Luanhaizi Lake, with a generally low diversity of aquatic plants and algae. During 3670–2290 cal yr BP, Luanhaizi Lake had an increased diversity and triggered a shift from Stuckenia to Eurasian watermilfoil (Myriophyllum). During 1810–50 cal yr BP, with the warming process, cyanobacteria appeared and diversity peaked. Correlation and redundancy analyses show that this process might be related to climatic and environmental changes, such as decreased salinity, increased temperature, and higher water levels. Our results signify that aquatic plants and algae communities are sensitive to paleoclimatic and environmental change, in the context of global warming, the studies on the past dynamics of aquatic plants and algae will help predict the potential changes of the lake ecosystem on the northeastern Tibetan Plateau in the future.

Shape deformation, disintegration, and coalescence of suspension drops: Efficient simulation enabled by graph neural networks

Understanding the behaviors of suspension drops (particle swarms) as they settle in a viscous fluid holds significant importance across various applications. Due to hydrodynamic interactions (HIs), suspension drops would undergo a series of intricate behaviors, including shape deformation, disintegration, and coalescence. This work presents the hydrodynamic interaction graph neural network (HIGNN), developed in our prior work (Ma et al., 2022), as an efficient and accurate modeling framework for simulating the dynamics of suspension drops and investigating the various behaviors they exhibit. The HIGNN effectively incorporates the many-body nature of HIs, a feature lacking in most previous simulations that employ the Stokeslet assumption. In the meanwhile, the HIGNN achieves superior computational efficiency compared to traditional, high-fidelity numerical tools such as Stokesian dynamics and PDE solvers. Moreover, the HIGNN, once trained, is applicable to predicting suspension drops across a range of particle concentrations and under diverse forces (such as gravity and Coulombic interactions). Training the HIGNN only requires the data containing a small number of particles, leading to low training cost. Our results demonstrate that the HIGNN can effectively reproduce the various behaviors of suspension drops that were previously reported in literature. More specifically, a single, initially spherical drop slowly evolves into a torus-shaped drop, as particles escape from its rear and form a tail along the sedimenting direction. Subsequently, the torus breaks into secondary droplets, each undergoing a similar transition (deformation into a torus followed by disintegration), thereby leading to a repeating cascade. Further, we quantitatively analyze the correlation between the drop’s sedimentation velocity and volume fraction. We also propose new scaling laws for evaluating both the leakage rate of particles and the expansion rate of the horizontal radius of a suspension drop. For single suspension drops, we also systematically investigate how their dynamics is affected by their initial shapes and the formed tori’s aspect ratios, as well as with or without Coulombic interactions between particles. For a pair of suspension drops, we study the process of coalescence of two vertically aligned particles and examine the effect of introducing a horizontal offset on the subsequent breakup of the coalesced drop. All simulations were executed on a single GPU, with the computation of velocities for several thousand particles requiring less than five seconds per time step. This computational efficiency enables fast and resource-saving simulations of large suspension drops over extended time scales.

Altering Conformational States of Dynamic Ion Populations using Traveling Wave Structures for Lossless Ion Manipulations.

With its capacity to store and translate ions across considerable distances and times, traveling wave structures for lossless ion manipulations (TW-SLIM) provide the foundation to expand the scope of ion mobility spectrometry (IMS) experiments. While promising, the dynamic electric fields and consequential ion-neutral collisions used to realize extensive degrees of separation have a considerable impact on the empirical results and the fundamental interpretation of observed arrival time distributions. Using a custom-designed set of TW-SLIM boards (∼9 m) coupled with a time-of-flight mass spectrometer (SLIM-ToF), we detail the capacity to systematically alter the gas-phase distribution of select peptide conformers. In addition to discussing the role charge-transfer may play in TW-SLIM experiments that occur at extended time scales, the ability of the SLIM-ToF to perform tandem IMS was leveraged to confirm that both the compact and elongated conformers of bradykinin2+ undergo interconversion within the SLIM. Storage experiments in which ions are confined within SLIM using static potential wells suggest that factors aside from TW-induced ion motion contribute to interconversion. Further investigation into this matter suggests that the use of radio frequency (RF) fields to confine ions within SLIM may play a role in ion heating. Aside from interconversion, storage experiments also provide insight into charge transfer behavior over the course of extended periods. The results of the presented experiments suggest that considerations should be taken when analyzing labile species and inform strategies for the TW-SLIM design and method development.

Cosmological variation of the proton-to-electron mass ratio constrained by strong gravitational fields

Exploring the cosmological variations of fundamental dimensionless constants, such as the fine-structure constant, [Formula: see text], the proton-to-electron mass ratio, [Formula: see text], and the gravitational constant, G, is essential for testing new phenomena beyond the standard cosmological models. This study focuses on investigating the potential variation of constants by analyzing strong gravitational fields associated with white dwarf stars. Utilizing the observed spectrum of G191-B2B, we present a new constraint on the cosmological variation of [Formula: see text] over extended time scales, [Formula: see text], incorporating the gravitational redshift [Formula: see text]. This finding represents a new tool for checking the parameters for Grand Unified Theories (GUTs).

Plutonium mobility and reactivity in a heterogeneous clay rock barrier accented by synchrotron-based microscopic chemical imaging

The long-term safe disposal of radioactive waste corresponds to a challenging responsibility of present societies. Within deep geological waste disposal concepts, host rocks correspond to the ultimate safety barrier towards the environment. To assess the performance of such barriers over extended time scales, mechanistic information on the interaction between the radiotoxic, long-lived radionuclides like plutonium and the host rock is essential. Chemical imaging based on synchrotron microspectroscopic techniques was used to visualize undisturbed reactive transport patterns of Pu within pristine Opalinus Clay rock material. Pu+V is shown to be progressively reduced along its diffusion path to Pu+IV and Pu+III due to interaction with redox-active clay rock constituents. Experimental results and modeling emphasize the dominant role of electron-transfer reactions determining the mobility of Pu in reactive barrier systems. The effective migration velocity of Pu is controlled by the kinetic rates of the reduction to Pu+IV and Pu+III and the redox capacity of the involved electron donor pools. To advance our predictive capabilities further, an improved understanding of the nature and capacity of redox-active components of the reactive barrier material is fundamental. The findings represent an essential contribution to the evaluation of the long-term safety of potential nuclear waste repositories and have implications regarding the development of effective geological disposal strategies.

Effect of increased pCO2 and temperature on the phytoplankton community in the coastal of Yellow Sea

In order to study the dynamics of marine phytoplankton communities in response to anticipated in temperature and CO2, a shipboard continuous culture experiment (Ecostat) was conducted. The experiment involved simulations under current atmospheric CO2 concentrations (400 ppm) and projected year-2100 CO2 levels (1000 ppm), as well as varying temperature under present (22 °C) versus increased temperature (26 °C) in the Yellow Sea during the summer of 2020. The results showed that both the increased pCO2 and temperature had significant effects on microphytoplankton and picophytoplankton, with the warming effect proving to be more significant. The different responses of various species to acidification and warming and their coupling effect led to the changes in microphytoplankton and picophytoplankton community structure. Elevated temperature and greenhouse treatments promoted the growth of dominant diatoms and Synechococcus, such as Guinardia flaccida and Pseudo-nitzschia delicatissima. This phenomenons widened the ecological niche, and the changes in the growth patterns of dominant species consequently influenced the content of cellular elements. Mantel's analysis further demonstrated that both warming and greenhouse promoted the growth of diatoms and Synechococcus. Projections of marine phytoplankton community trends by the end of the century based on Growth Rate Ratio (GRR), indicated that not only would species with GRR < 1 decrease, but also numerous species with growth rates >1 at elevated pCO2 levels would be ousted from competition. This experiment demonstrates the need to investigate whether extended exposure to increased pCO2 and temperature over more extended time scales would similarly induce shifts in the biological and biogeochemical dynamics of the Yellow Sea.

Minute-scale oscillatory sequences in medial entorhinal cortex

The medial entorhinal cortex (MEC) hosts many of the brain’s circuit elements for spatial navigation and episodic memory, operations that require neural activity to be organized across long durations of experience1. Whereas location is known to be encoded by spatially tuned cell types in this brain region2,3, little is known about how the activity of entorhinal cells is tied together over time at behaviourally relevant time scales, in the second-to-minute regime. Here we show that MEC neuronal activity has the capacity to be organized into ultraslow oscillations, with periods ranging from tens of seconds to minutes. During these oscillations, the activity is further organized into periodic sequences. Oscillatory sequences manifested while mice ran at free pace on a rotating wheel in darkness, with no change in location or running direction and no scheduled rewards. The sequences involved nearly the entire cell population, and transcended epochs of immobility. Similar sequences were not observed in neighbouring parasubiculum or in visual cortex. Ultraslow oscillatory sequences in MEC may have the potential to couple neurons and circuits across extended time scales and serve as a template for new sequence formation during navigation and episodic memory formation.

Reduced Sediment Settling in Turbulent Flows Due To Basset History and Virtual Mass Effects

AbstractThe behavior of suspended particles in turbulent flows is a recalcitrant problem spanning wide‐ranging fields including geomorphology, hydrology, and dispersion of particulate matter in the atmosphere. One key mechanism underlying particle suspension is the difference between particle settling velocity (ws) in turbulence and its still water counterpart (wso). This difference is explored here for a range of particle‐to‐fluid densities (1–10) and particle diameter to Kolmogorov micro‐eddy sizes (0.1–10). Conventional models of particle fluxes that equate ws to wso result in eddy diffusivities and turbulent Schmidt numbers contradictory to laboratory experiments. Incorporating virtual mass and Basset history forces resolves these inconsistencies, providing clarity as to why ws/wso is sub‐unity for the aforementioned conditions. The proposed formulation can be imminently used to model particle settling in turbulence, especially when sediment distribution outcomes over extended time scales far surpassing turbulence time scales are sought.

Top-Down Machine Learning of Coarse-Grained Protein Force Fields.

Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov state models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.

Extended time scales of carbonaceous chondrite aqueous alteration evidenced by a xenolith in LaPaz Icefield 02239 (CM2)

AbstractLaPaz Icefield (LAP) 02239 is a mildly aqueously altered CM2 carbonaceous chondrite that hosts a xenolith from a primitive chondritic parent body. The xenolith contains chondrules and calcium‐ and aluminum‐rich inclusions (CAIs) in a very fine‐grained matrix. The chondrules are comparable in mineralogy and oxygen isotopic composition with those in the CMs, and its CAIs are also mineralogically similar to the CM population apart for being unusually small and abundant. The presence of serpentine demonstrates that the xenolith has been aqueously altered, and its phyllosilicate‐rich matrix has a comparable oxygen isotopic composition to the matrices of CM meteorites. The xenolith's chondrules lack fine‐grained rims, whereas the xenolith itself has a fine‐grained rim that is petrographically and chemically comparable with the rims on coarse grained objects in LAP 02239 and other CM meteorites. These properties show that the xenolith's parent body was formed from similar materials to the CM parent body(ies). Following its lithification by aqueous alteration, a piece of the xenolith's parent body was impact‐ejected, acquired a fine‐grained rim while free‐floating in the protoplanetary disc, then was accreted along with rimmed chondrules and other materials to make the LAP 02239 parent body. Subsequent aqueous processing of the LAP 02239 parent body altered the fine‐grained rims on the xenolith, chondrules, and CAIs. The xenolith shows that the timespan of geological evolution of carbonaceous chondrite parent bodies was sufficiently long for some of them to have been aqueously altered before others had formed.

Douglas Fir Multiproxy Tree-Ring Data Glimpse MIS 5 Environment in the U.S. Pacific Northwest

Proxy records from the late Quaternary help in understanding climate variability on extended time scales. An ancient landslide deposit in Oregon U.S.A. preserved large logs from Douglas fir trees (Pseudotsuga menziesii (Mirb.) Franco) and afforded an opportunity to explore the response of tree growth to climate on annual and decadal scales. High-precision radiocarbon dating indicates an age exceeding 63 ka, i.e., the trees grew within the generally cool Marine Isotope Stage 5 (MIS 5), likely during a warmer interval optimal for Douglas fir establishment. This would include the prolonged warm MIS 5e (ca. 110–130 ka), corresponding approximately to the Eemian interglacial, which was warm like the current Holocene interglacial. A 297-year tree-ring width chronology from 12 Douglas fir logs and 227-year tree-ring δ13C and δ18O records are analyzed with spectral and wavelet analysis. Variance of the ancient rings is consistent with modern Douglas fir growth sensitive to moisture and ecological disturbances. Spectra of ancient and modern chronologies are dominated by low frequencies with significant spectral peaks appearing at high frequencies (2.1–4 years) and cyclic behavior transient over centuries. It is conceivable that the O-isotopes track moisture and that C-isotopes track temperature or sunlight. The findings illustrate the challenges in assessing the response of ancient tree-ring properties to late Quaternary climate variability.

GCS site selection in saline Miocene formations in South Louisiana

One of the key requirements of a commercial scale geologic carbon sequestration (GCS) site selection process is identifying injection sites to safely inject and retain injected CO2 over extended time scales. This requires thorough evaluation of site-specific surface and subsurface features. A site screening and selection framework was developed and used to identify potential sites in South Louisiana, incorporating surface data including site proximity to large CO2 stationary sources, vicinity of large population centres and state lands, land usage, legacy oil and gas wells, and local/regional geologic trends. Regionally the Miocene sediments thicken and dip towards the south, reflecting a prograding deltaic environment during the Miocene which resulted in the deposition of thick packages of fine-grained sandstones suitable for CO2 storage. The results of three example selected sites that are spatially separated with varying subsurface and surface features are presented in this study. The multiple stacked sand zones at these sites provide ample amount of pore volume for CO2 storage with varying degree of stratigraphic/structural traps for long terms storage containment. The screening framework presented in this study and the detailed evaluation of three specific sites in South Louisiana provides a process to screen and characterize sites for GCS projects and the suitability of these Miocene sediments for commercial scale CO2 storage projects. It is envisioned that the analysis described in this paper will be beneficial for a large number of audiences involved in GCS work and aid in the site selection process.

Recent Advances in First-Principles Based Molecular Dynamics.

First-principles molecular dynamics (FPMD) and its quantum mechanical-molecular mechanical (QM/MM) extensions are powerful tools to follow the real-time dynamics of a broad variety of systems in their ground as well as electronically excited states. The continued advances in computational power have enabled simulations of QM regions of larger sizes for more extended time scales. In addition, development of the parallel algorithms has boosted the performance of QM/MM methods even on existing computer architectures. In the case of density functional-based FPMD, systems of several hundreds to thousands of atoms can now be customarily simulated for tens to hundreds of picoseconds. In spite of this progress, the time scale limitations remain severe, especially when high-rung exchange-correlation functionals or high-level wave function based quantum mechanical methods are used. To ameliorate this, a large number of enhanced sampling methods have been introduced but most of the approaches that have been developed to increase the efficiency of FPMD based simulations sacrifice the real-time dynamics in favor of enhancing sampling. Here, we present some recent advances in boosting the efficiency of FPMD based simulations while keeping the full dynamic information. These include a highly efficient recent implementation of FPMD-based QM/MM simulations that not only enables fully flexible combinations of different electronic structure methods and force fields via a highly efficient communication library, it also fully exploits parallelism for both quantum and classical descriptions. The second type of acceleration methods we discuss is a large family of specially devised multiple-time-step algorithms that make use of suitable breakups of the total nuclear forces into fast components that can be calculated via lower level methods and slowly varying correction forces evaluated with a high-level method at long time intervals. The computational gain of this scheme mostly depends on the cost difference between the two methods and advantageous combinations can yield large speedups without compromising the accuracy of the high-level method. And finally, the third class of FPMD acceleration methods presented here are machine learning models to accelerated FPMD and their powerful combinations with multiple-time-step techniques. The combination of all the approaches enables substantial speedups of FPMD simulations of several orders of magnitude while fully preserving the real-time dynamics and accuracy.

Evaluation of grain boundary energy, structure and stiffness from phase field crystal simulations

A two-mode phase field crystal (PFC) model is employed to investigate the equilibrium configurations of a range of grain boundaries in fcc-structured materials. A total of 80 different symmetrical tilt grain boundaries are evaluated by PFC simulations in 3D and the results are shown to agree well with data taken from the literature, both regarding the variation of grain boundary energy and also in terms of the resulting grain boundary structures. This verification complements existing PFC studies which are almost exclusively focused either on grain boundaries found in 2D systems or in bcc lattices in 3D. The present work facilitates application of PFC in the analysis of grain boundary mechanics in an extended range of materials, in particular such mechanics that take place at extended time scales not tractable for molecular dynamics (MD) simulations. In addition to the verification of predicted grain boundary energies and structures, wavelet transforms of the density field are used in the present work to obtain phase fields from which it is possible to identify grain boundary fluctuations that provide the means to evaluate grain boundary stiffness based on the capillarity fluctuation method. It is discussed how PFC provides benefits compared to alternative methods, such as MD simulations, for this type of investigations.