© Paesani Research Group. All rights reserved.

188. Molecular insights into the influence of ions on water structure. I. Alkali metal ions in solution.

        R. Savoj, H. Agnew, R. Zhou, F. Paesani, J. Phys. Chem. B 128,1953 (2024). [link]

In this study, we explore the impact of alkali metal ions (Li+, Na+, K+, Rb+, and Cs+) on the hydration

structure of water using molecular dynamics simulations carried out with the MB-nrg potential energy

functions (PEFs). Our analyses include radial distribution functions, coordination numbers, dipole moments,

and infrared spectra of water molecules, calculated as a function of solvation shells. The results collectively

indicate a highly local influence of all the alkali metal ions on the hydrogen-bond network established by

the surrounding water molecules, with the smallest and most densely charged Li+ ion exerting the most

pronounced effect. Remarkably, the MB-nrg PEFs demonstrate excellent agreement with available

experimental data for the position and size of the first solvation shells, underscoring their potential as

predictive models for realistic simulations of ionic aqueous solutions across various thermodynamic

conditions and environments.

187. Balance between physical interpretability and energetic predictability in widely used dispersion-corrected

        density functionals. S. Dasgupta, E. Palos, Y. Pan, F. Paesani, J. Chem. Theory Comput. 20, 49 (2024). [link]

We assess the performance of different dispersion models for several popular density functionals across a diverse

set of non-covalent systems, ranging from the benzene dimer to molecular crystals. By analyzing the interaction

energies and their individual components, we demonstrate that there exists variability across different systems for

empirical dispersion models that are calibrated for reproducing the interaction energies of specific systems. Thus,

parameter fitting may undermine the underlying physics, as dispersion models rely on error compensation among

the different components of the interaction energy. Energy decomposition analyses reveal that, the accuracy of

several dispersion-corrected functionals originates from significant compensation between dispersion and charge

transfer energies. These results highlight the propensity for unpredictable behavior in various dispersion-corrected

density functionals across a wide range of molecular systems, akin to the behavior of force fields. Future development

of dispersion models should prioritize the faithful description of the dispersion energy, a shift that promises greater

accuracy in capturing the underlying physics across diverse molecular and extended systems.

189. Molecular driving forces for water adsorption in MOF-808: A comparative analysis with UiO-66.

        H.O. Frank, F. Paesani, J. Chem. Phys. 160, 094703 (2024). [link]

In this study, we investigate water adsorption in MOF-808, a prototypical MOF that shares the same

secondary building unit (SBU) as UiO-66, and elucidate how differences in topology and connectivity

between the two MOFs influence the adsorption mechanism. To this end, molecular dynamics simulations

were performed to calculate several thermodynamic and dynamical properties of water in MOF-808 as

a function of relative humidity (RH), from the initial adsorption step to full pore filling. At low RH, the

μ3-OH groups of the SBUs form hydrogen bonds with the initial water molecules entering the pores,

which triggers the filling of these pores before the μ3-OH groups in other pores become engaged in

hydrogen bonding with water molecules. Our analyses indicate that the pores of MOF-808 become

filled by water sequentially as the RH increases. A similar mechanism has been reported for water

adsorption in UiO-66. Despite this similarity, our study highlights distinct thermodynamic properties

and framework characteristics that influence the adsorption process differently in MOF-808 and UiO-66.

191. Many-body interactions and deep neural network potentials for water.

        Y. Zhai, R. Rashmi, E. Palos, F. Paesani, J. Chem. Phys. 160, 144501 (2024). [link]

We present a detailed assessment of deep neural network potentials developed within the DeePMD framework and

trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of

DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of

bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations

in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an

incomplete implementation of the “nearsightedness of electronic matter” principle, which may be common throughout

machine learning potentials that do not include a proper representation of self-consistently determined long-range

electric fields. These findings provide further support for the “short-blanket dilemma” faced by DeePMD-based

potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous,

physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing

discourse on the development and application of machine learning models in simulating water systems.

190. Excited-state rotational freedom impacts viscosity sensitivity in arylcyanoamide fluorescent molecular rotor

        dyes. R.S. Ehrlich, S. Dasgupta, R.E. Jessup, K.L. Teppang, A.L. Shiao, Kun Yong Jeoung, X. Su, A. Shivkumar,

        E.A. Theodorakis, F. Paesani, J. Yang, J. Phys. Chem. B 128, 3946 (2024). [link]

The microviscosity of intracellular environments plays an important role in monitoring cellular function. Thus,

the capability to detect changes in viscosity can be utilized for the detection of different disease states. Viscosity

sensitive fluorescent molecular rotors are potentially excellent probes for these applications; however, predictable

relationships between chemical structural features and viscosity sensitivity are poorly understood. Here, we

investigate a set of arylcyanoamide-based fluorescent probes and the effect of small aliphatic substituents on their

viscosity sensitivity. We found that the location of the substituents as well as the type of π-network of the fluorophore

can significantly affect the viscosity sensitivity of these fluorophores. Computational analysis supported that the

excited state rotational energy barrier plays a dominant role in the relative viscosity sensitivity of these fluorophores.

These findings provide valuable insight in the design of molecular rotor-based fluorophores for viscosity measurement.

192. Cooperative interactions with water drive hysteresis in a hydrophilic metal-organic framework. J.J. Oppenheim,

        C.-H. Ho, D. Alezi, J.L. Andrews, T. Chen, B. Dinakar, F. Paesani, M. Dincă, Chem. Mater. 36, 3395 (2024). [link]

Devices that utilize the reversible capture of water vapor provide solutions to water insecurity, increasing energy demand,

and sustainability. Herein, we synthesize a new hexagonal microporous framework, Ni2Cl2BBTQ, to elucidate these

principles. Uniquely among its known isoreticular analogues, Ni2Cl2BBTQ presents unusually high hysteresis caused by

strong wetting seeded by a particularly strong zero-coverage interaction with water. A combination of vibrational spectra

and molecular dynamics simulations reveals that this hysteretic behavior is the result of an intricate hydrogen-bonding

network, in which the monolayer consists of water simultaneously binding to open nickel sites and hydrogen bonding to

quinone sites. This latter hydrogen-bonding interaction does not exist in other isoreticular analogues: it prevents facile

water dynamics and drives hysteresis. Our results highlight an important design criterion for water sorbents: in order to

drive water uptake in progressively dry conditions, the common strategy of increasing hydrophilicity can cause strong

wetting and the formation of superclusters, which lead to undesirable hysteresis. Instead, hysteresis-free water uptake

at extremely low humidity is best promoted by decreasing the pore size, rather than increasing hydrophilicity..

193. Monitoring water harvesting in metal–organic frameworks, one water molecule at a time.

        K.M. Hunter, F. Paesani, Chem. Sci. 15, 5303 (2024). [link]

MOFs have gained prominence as potential materials for atmospheric water harvesting, a vital solution for arid regions and areas

experiencing severe water shortages. Among all MOFs, Ni2X2BTDD (X = F, Cl, Br) stands out as a promising water harvester due

to its ability to adsorb substantial amounts of water at low relative humidity (RH). Here, we use advanced molecular dynamics

simulations carried out with the state-of-the-art MB-pol data-driven many-body potential to monitor water adsorption in the three

Ni2X2BTDD variants as a function of RH. Our simulations reveal that the type of halide atom in the three Ni2X2BTDD frameworks

significantly influences the corresponding molecular mechanisms of water adsorption: while water molecules form strong

hydrogen bonds with the fluoride atoms in Ni2F2BTDD, they tend to form hydrogen bonds with the nitrogen atoms of the triazolate

linkers in Ni2Cl2BTDD and Ni2Br2BTDD. Importantly, the large size of the bromide atoms reduces the void volume in the

Ni2Br2BTDD pores, which enable water molecules to initiate an extended hydrogen-bond network at lower RH. These findings

not only underscore the prospect for precisely tuning structural and chemical modifications of the frameworks to optimize their

interaction with water, but also highlight the predictive power of simulations with the MB-pol data-driven many-body potential.

194. Entropy of liquid water as predicted by the two-phase thermodynamic model and data-driven many-body

        potentials. C.H. Ho, F. Paesani, J. Phys. Chem. B 128, 6885 (2024). [link]

We investigate the entropy of liquid water at ambient conditions using the two-phase thermodynamic (2PT) model, applied

to both common pairwise-additive water models and the MB-pol and MB-pol(2023) data-driven many-body potentials. Our

simulations demonstrate that the 2PT model yields entropy values in semiquantitative agreement with experimental data

when using MB-pol and MB-pol(2023). Additionally, our analyses indicate that the entropy values predicted by

pairwise-additive water models may benefit from error compensation between the inherent approximations of the 2PT

model and the known limitations of these water models in describing many-body interactions. Despite its approximate

nature, the simplicity of the 2PT model makes it a valuable tool for estimating relative entropy changes of liquid water

across various environ- ments, especially when combined with water models that provide a consistently robust

representation of the structural, thermodynamic, and dynamical properties of liquid water.

195. Pinpointing the location of the elusive liquid-liquid critical point in water.

        F. Sciortino, Y. Zhai, S.L. Bore, F. Paesani, under review. [link]

Over the past three decades, advancements in computational modeling – particularly through the advent of data-driven

many-body potentials rigorously derived from “first principles” and augmented by the efficiency of neural networks – have

significantly enhanced the accuracy of molecular simulations, enabling the exploration of the phase behavior of water with

unprecedented realism. Our study leverages these computational advances to probe the elusive liquid-liquid transition in

supercooled water. For the first time, microsecond-long simulations with chemical accuracy, conducted over several years,

provide compelling evidence that water indeed exists in two discernibly distinct liquid states at low temperature and high

pressure. By pinpointing a realistic estimate for the location of the liquid-liquid critical point at ~200 K and ~1050 atm, our

study not only advances current understanding of water’s anomalous behavior but also establishes a basis for experimental

validation. Importantly, our simulations indicate that the liquid-liquid critical point falls within tem- perature and pressure

ranges that could potentially be experimentally probed in water nanodroplets, opening the possibility for direct measurements.

196. Nuclear quantum effects and the Grotthuss mechanism dictate the pH of liquid water.

        S. Dasgupta, G. Cassone, F. Paesani, under review. [link]

Water’s ability to autoionize into hydronium (H3O+) and hydroxide (OH−) ions dictates the acidity or basicity of aqueous

solutions, influencing the reaction pathways of many chemical and biochemical processes. In this study, we determine the

molecular mechanism of the autoionization process by leveraging both the computational efficiency of a deep neural

network potential trained on highly accurate data calculated within density-corrected density functional theory and the

ability of enhanced sampling techniques to ensure a comprehensive exploration of the underlying multidimensional

free-energy landscape. By properly accounting for nuclear quantum effects, our simulations provide an accurate estimate

of autoionization constant of liquid water (pKw = 13.71 ± 0.16), offering a real- istic molecular-level picture of the

autoionization process and emphasizing its quantum-mechanical nature. Importantly, our simulations highlight the central

role played by the Grotthuss mechanism in stabilizing solvent-separated ion pair configurations, revealing its profound

impact on acid-base equilibria in aqueous environments.

197. Current status of the MB-pol data-driven many-body potential for predictive simulations of water across

        different phases. E. Palos, E. Bull-Vulpe, X. Zhu, H. Agnew, S. Gupta, F. Paesani, under review. [link]

Accurately modeling water through computer simulations has been a significant challenge due to the complex nature of

the hydrogen- bonding network that water molecules form under different thermodynamic conditions. This complexity has

led to over five decades of research and many modeling attempts. The introduction of the MB-pol data-driven many-body

potential energy function marked a significant advancement toward a universal molecular model capable of predicting the

structural, thermodynamic, dynamical, and spectroscopic properties of water across all phases. By integrating physics-based

and data-driven (i.e., machine-learned) components, which correctly capture the delicate balance among different

many-body interactions, MB-pol achieves chemical and spectroscopic accuracy, enabling realistic molecular simulations of

water, from gas-phase clusters to liquid water and ice. In this review, we present a comprehensive overview of the

data-driven many-body formalism adopted by MB-pol, highlight the main results and predictions made from computer

simulations with MB-pol to date, and discuss the prospects for future extensions to data-driven many-body potentials of

generic and reactive molecular systems.