Paesani Research Group

Laboratory for Theoretical and Computational Chemistry at UC San Diego  

171. Connecting the dots for fundamental understanding of structure-photophysics-property relationships of

        COFs, MOFs, and perovskites using a multiparticle Holstein formalism.

        R. Ghosh, F. Paesani, Chem. Sci. 14, 1040 (2023). [link]

Elucidating structure-photophysics-property relationships in functional materials is nontrivial and requires

a fundamental understanding of the intricate interplay among excitons, polarons, bipolarons, phonons,

inter-layer stacking interactions, and different forms of structural and conformational defects. Here, we describe

a unified theoretical framework in which the electronic coupling as well as the local coupling between

the electronic and nuclear degrees of freedom can be efficiently described for a wide range of quasiparticles

with similarly structured Holstein-style Hamiltonians. We then discuss excitonic and polaronic photophysical

signatures in polymers, COFs, MOFs, and perovskites, and bridge the gap between different research fields

using a Multiparticle Holstein Formalism. We envision that the synergistic integration of state-of-the-art

computational approaches with the Multiparticle Holstein Formalism will help establish new design strategies

for the development of next-generation materials optimized for a broad range of energy-related applications.

© Paesani Research Group. All rights reserved.

Publications 2023

170. Data-driven many-body potential energy functions for generic molecules: Linear alkanes as a proof-of-

        concept application. E.F. Bull-Vulpe, M. Riera, S.L. Bore, F. Paesani, J. Chem. Theory Comput. 19, 4494 (2023). [link]

We present a generalization of the many-body energy (MB-nrg) theoretical/computational framework that

enables the development of data-driven potential energy functions (PEFs) for generic covalently bonded

molecules, with arbitrary quantum mechanical accuracy. Exploiting the "nearsightedness" of electronic matter,

the energy of generic molecules is expressed as a sum of individual many-body energies of incrementally

larger subsystems which are represented by a combination of data-driven permutationally invariant polynomials

optimized to reproduce electronic structure data at an arbitrary level of theory and classical many-body

polarization. As a proof-of-concept application of the general MB-nrg framework, we present MB-nrg PEFs

that accurately reproduce reference energies, harmonic frequencies, and potential energy scans of alkanes,

independently of their length. Since, by construction, the MB-nrg framework can be applied to generic

covalently bonded molecules, we envision future computer simulations of complex molecular systems using

data-driven MB-nrg PEFs, with arbitrary quantum mechanical accuracy.

173. A highly sensitive fluorescent reporter for adenine base editing reveals DNA base editing by the wild-type

        tRNA adenosine deaminase enzyme TadA. B.L. Ranzau, K.L. Rallapalli, M. Evanoff, F. Paesani, A.C. Komor,

        ChemBioChem e202200788 (2023). [link]

Adenine base editors (ABEs) convert A•T to G•C base pairs in DNA by utilizing an adenosine deaminase enzyme

to modify target adenosines to inosine intermediates. Due to the lack of a naturally occurring adenosine deaminase

that can modify DNA, ABEs were evolved from a tRNA-deaminating enzyme, TadA. Previous experiments utilizing

an ABE comprised of a wild-type (wt) TadA showed no detectable activity on DNA, and directed evolution was

therefore required to enable this enzyme to accept DNA as a substrate. Here we show that wtTadA can perform

base editing in DNA in both bacterial and mammalian cells, with a strict sequence motif requirement of TAC.

We leverage this discovery to optimize a reporter assay to detect base editing levels as low as 0.01%. Finally, we use

this assay along with molecular dynamics simulations of full ABE:DNA complexes to better understand how the

sequence recognition of mutant TadA variants change as they accumulate mutations to better edit DNA substrates. 

172. Data-driven many-body potentials from density functional theory for aqueous phase chemistry.

        E. Palos, S. Dasgupta, E. Lambros, F. Paesani, Chem. Phys. Rev. 4, 011301 (2023). [link]

The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the

single-molecule to the thermodynamic limit and everything in between. Recent advances in the development of

data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT

accuracy. However, the anomalous properties of water in the condensed phase, where a rigorous treatment of both

local and non-local many-body interactions is in order, is often unsatisfactory or partially missing in DFT models of water.

Here, we discuss the modeling of water and aqueous systems based on DFT, and provide a comprehensive description

of a general theoretical/computational framework, coined MB-DFT, for the development of data-driven many-body

potentials from DFT reference data. Theoretical considerations are emphasized, including the role that the delocalization

error plays in MB-DFT potentials of water, and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy

through a density-corrected formalism. Finally, we identify open challenges and discuss future directions for MB-DFT

simulations in condensed phases.

175. A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental

        properties or the physics of the underlying physics? Y. Zhai, A. Caruso, S.L. Bore, Z. Luo, F. Paesani,

        J. Chem. Phys. 158, 084111 (2023). [link]

We explore the possibility of combining the computational efficiency of DeePMD and the demonstrated accuracy

of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across

its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water

but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced

back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly

include information about many-body effects results in a new DNN potential that exhibits opposite performance,

being able to correctly reproduce the MB-pol vapor-liquid equilibrium but losing accuracy in the description of the

liquid properties. These results suggest that DeePMD DNN potentials are not able to correctly “learn” and,

consequently, represent many-body interactions. The computational efficiency of DeePMD can still be exploited

to train DNN potentials on data-driven many-body potentials to enable large-scale, chemically accurate simulations.

174. Structure and thermodynamics of water adsorption in NU-1500-Cr. C.-H. Ho, M.L. Valentine, Z. Chen, H. Xie,

        O.K. Farha, W. Xiong, F. Paesani, Commun. Chem. 6, 70 (2023). [link]

The mechanism of water adsorption in NU-1500-Cr is investigated using a combination of molecular dynamics

simulations and infrared spectroscopy. Calculations of thermodynamic and dynamical properties of water as a function

of relative humidity allow for following the adsorption process from the initial hydration stage to complete filling of the

MOF pores. Initial hydration begins at the water molecules that saturate the open Cr3+ sites of the framework, which is

then followed by the formation of water chains that extend along the channels connecting the hexagonal pores of the

framework. Water present in these channels gradually coalesces and fills the hexagonal pores sequentially after the

channels are completely hydrated. The development of hydrogen-bond networks inside the MOF pores as a function of

relative humidity is characterized at the molecular level using experimental and computational infrared spectroscopy.

A detailed analysis of the OH-stretch vibrational band indicates that the low-frequency tail, which is common in the

experimental infrared spectra of water in MOFs, stems from strongly polarized hydrogen-bonded water molecules,

suggesting the presence of some structural dis- order in the experimental samples.

176. Realistic phase diagram of water from "first principles" data-driven quantum simulations. S.L. Bore, F. Paesani,

        Nat. Commun. 14, 3349 (2023). [link]

Since the experimental characterization of the low-pressure region of the phase diagram of

water in the early 1900s, scientists have been on a quest to understand the thermodynamic

stability of ice polymorphs. In this study, we demonstrate that combining the MB-pol data-driven

many-body potential for water, which was rigorously derived from “first principles” and exhibits

chemical accuracy, with advanced enhanced-sampling algorithms, which correctly describe the

quantum nature of molecular motion and thermodynamic equilibria, enables simulations of the

phase diagram of water with an unprecedented level of realism. Besides providing unique

insights into how enthalpic, entropic, and nuclear quantum effects shape the free-energy

landscape of water, we demonstrate that recent progress in data-driven many-body potentials

and simulation algorithms has effectively opened the door to realistic computational studies of

complex molecular systems, thus bridging the gap between experiments and simulations.

177. Unveiling unexpected modulator-CO2 dynamics within a zirconium metal-organic framework. T.M. Rayder,

        F. Formalik, S.M. Vornholt, H. Frank, S. Lee, M. Alzayer, Z. Chen, T. Islamoglu, F. Paesani, K.W. Chapman,

        R.Q. Snurr, O.K. Farha, J. Am. Chem. Soc. 145, 11195 (2023). [link]

Carbon capture, storage, and utilization (CCSU) represents an opportunity to mitigate carbon emissions that

drive global anthropogenic climate change. Promising materials for CCSU through gas adsorption have been

developed by leveraging the porosity, stability, and tunability of extended crystalline coordination polymers

called metal-organic frameworks (MOFs). Herein, we report a multifaceted in situ analysis following the

adsorption of CO2 in MOF-808 variants with different capping agents (formate, acetate, and trifluoroacetate

– FA, AA, and TFA, respectively). In situ DRIFTS analysis paired with in situ powder X-ray diffraction revealed

unexpected CO2 interactions at the node associated with dynamic behavior of node-capping modulators in

the pores of MOF-808, which had previously been assumed to be static. MOF-808-TFA displays two binding

modes for CO2, resulting in higher binding affinity and gas uptake of CO2. Computational analyses further

support these dynamic observations.

178. Engineering flat and dispersive bands in 2D layered COFs via interlayer stacking and donor-acceptor strategy.

        Y. Pan, C.H. Ho, F. Paesani, R. Ghosh, Chem. Mater. 16, 6235 (2023). [link]

Covalent organic frameworks (COFs) are an emergent class of two-dimensional (2D) crystalline organic materials that exhibit unique electronic, optical, and transport prop- erties. In this study, we employ density functional theory (DFT) and the multiparticle Holstein

formalism (MHF) to investigate the electronic structure and two-dimensional coherence of polarons in

donor-acceptor COFs as a function of interlayer stacking arrangement. We show that simple modifications in

the interlayer stacking arrangement have a profound impact on the transport properties, which can range from

metallic behavior with vanishing band gap to highly localized states having completely flat bands. The extent of

charge delocalization is found to be sensitive to the type of stacking arrangement and the precise arrangement

of the donor and acceptor fragments within the COF structure. The results from the DFT calculations are

consistent with MHF-based simulations, demonstrating that stacking-induced interlayer interactions facilitate

better in-plane charge delocalization. As a consequence, we find that interlayer interactions help circumvent

defect-induced trap states to enhance overall charge delocalization.

179. Towards data-driven many-body simulations of biomolecules in solution: N-methyl acetamide as a proxy for

        the protein backbone. R. Zhou, M. Riera, F. Paesani, J. Chem. Theory Comput. 19, 4308 (2023). [link]

The development of molecular models with quantum-mechanical accuracy for predictive simulations of biomolecular systems

has been a long standing goal in the field of computational biophysics and biochemistry. As a first step towards a transferable

force field for biomolecules entirely derived from “first principles”, we introduce a data-driven many-body energy (MB-nrg)

potential energy function (PEF) for N-methyl acetamide (NMA), a peptide bond capped by two methyl groups that is commonly

used as a proxy for the protein backbone. The MB- nrg PEF is shown to accurately describe the energetics and structural

properties of an isolated NMA molecule, including the normal modes of both cis and trans isomers and the energies along the

isomerization path, as well as the multidimensional potential energy landscape of the NMA–H2O dimer in the gas phase.

Importantly, we show that the MB-nrg PEF is fully transferable, enabling molecular dynamics simulations of NMA in solution

with chemical accuracy. Comparisons with results obtained with a popular pairwise-additive force field for biomolecules and a

classical polarizable PEF demonstrate the ability of the MB-nrg PEF to accurately represent many-body effects in NMA–H2O

interactions at both short and long distances, which is key to guaranteeing full transferability from the gas to the liquid phase.

180. MB-pol(2023): Sub-chemical accuracy for water simulations from the gas to the liquid phase. X. Zhu, M. Riera,

        E.F. Bull-Vulpe, F. Paesani, J. Chem. Theory Comput. 19, 3551 (2023). [link]

We use the MB-pol theoretical/computational framework to introduce a new family of data-driven many-body

potential energy functions (PEFs) for water, named MB-pol(2023). By employing larger 2-body and 3-body

training sets, including an explicit machine-learned representation of 4-body energies, and adopting more

sophisticated machine-learned representations of 2-body and 3-body energies, we demonstrate that the

MB-pol(2023) PEFs achieve sub-chemical accuracy in modeling the energetics of the hexamer isomers,

outperforming both the original MB-pol and q-AQUA PEFs, which currently provide the most accurate description

of water clusters in the gas phase. Importantly, the MB-pol(2023) PEFs provide remarkable agreement with

the experimental results for various properties of liquid water, improving upon the original MB-pol PEF and

effectively closing the gap with experimental measurements. We also demonstratte that our MB-pol(2023) PEFs

can be trivially improved, without changing the underlying functional form, by training the current 2-body,

3-body, and 4-body terms on more “complete” datasets and/or adding explicit n-body terms with n > 4.

181. Many-body potential for simulating the self-assembly of polymer-grafted nanoparticles in a polymer matrix.

        Y. Zhou, S.L. Bore, A.R. Tao, F. Paesani, G. Arya. Under review. [link]

In this study, we develop a many- body potential for describing the effective interactions between spherical

polymer-grafted NPs in a polymer matrix through a machine learning (ML) approach. The approach involves using

permutationally invariant polynomials to fit two- and three-body interactions derived from potential of mean force

(PMF) calculations. This potential developed here reduces the computational cost by several orders of magnitude,

thereby allowing us to explore assembly behavior over large length and time scales. We show that the potential not

only reproduces previously known assembled phases, such as 1D strings and 2D hexagonal sheets, which cannot

be achieved using two-body potentials, but can also help discover interesting new phases, such as networks,

clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition

between two- and three-body interactions. Our approach for deriving many- body effective potentials can be

readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior

and dissect the role of individual interaction energy terms in the overall potential in the observed behavior.

182. Computational insights into the interaction of water with the UiO-66 metal-organic framework and its

        functionalized derivatives. J. Zhang, F. Paesani, M. Lessio, J. Mater. Chem. C 11, 10247 (2023). [link]

We provide computational insights into the adsorption of water in UiO-66 and its functionalized derivatives

to reveal the role played by different adsorption sites and functional groups in the adsorption mechanism.

To this end, we developed molecular models for UiO-66, UiO-66-NH2, UiO-66-OH, and UiO-66-(OH)2

compatible with the MB-pol data-driven many-body potential of water, which were used to perform MD

simulations and calculate radial distribution functions, IR spectra, and two-dimensional density distributions

of water in the the various MOFs. Our results consistently show that the μ3-OH sites are the preferential

interaction sites for water in UiO-66 and all its variants, and the formation of localized water clusters inside

the octahedral pores is responsible for the abrupt step in the experimental adsorption isotherms.

Furthermore, the presence of different functional groups allows water to cluster in the octahedral pores at

lower RH, thus making UiO-66 a more efficient water harvester. Our study provides molecular-level

insights into the pore filling process, which are key to the design of more efficient water harvesting MOFs.

183. MBX: A many-body energy and force calculator for data-driven many-body simulations. M. Riera, C. Knight,

        E.F. Bull-Vulpe, X. Zhu, H. Agnew, D.G.A. Smith, A.C. Simmonett, F. Paesani, J. Chem. Phys. 159, 054802 (2023).


MBX is a C++ library that implements many-body potential energy functions (PEFs) within the “many-body energy”

(MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned

representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases.

MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated

with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using

OpenMP, and can utilize MPI when available in interfaced molecular simulation software. MBX enables classical

and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional

force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions

and molecular fluids to biomolecular systems and metal-organic frameworks.

184. Elucidating the competitive adsorption of H2O and CO2 in CALF-20: New insights for enhanced carbon

        capture metal-organic frameworks. C.-H. Ho, F. Paesani, ACS Appl. Mater. Interfaces 15, 49287 (2023). [link]

In this study, we investigates the simultaneous adsorption of water (H2O) and carbon dioxide (CO2) as a function of RH

in CALF-20. Advanced computer simulations reveal that, due to their similar interactions with the framework, H2O and

CO2 molecules compete for the same binding sites, occupying similar void regions within the CALF-20 pores. This

competition results in distinct thermodynamic and dynamical behavior of H2O and CO2 molecules, depending on whether

one or both guest species are present. Notably, the presence of CO2 molecules forces the H2O molecules to form more

connected hydrogen-bond networks within smaller regions, slowing water reorientation dynamics and decreasing water

entropy. Conversely, the presence of water speeds up the reorientation of CO2 molecules, decreases CO2 entropy, and

increases the propensity for CO2 to be adsorbed within the framework due to stronger water-mediated interactions. Due

to the competition for the same void spaces, both H2O and CO2 molecules exhibit slower diffusion when molecules of the

other guest species are present. These findings offer valuable strategies and insights to enhance the differential

affinity of H2O and CO2 for MOFs specifically designed for carbon capture applications.

187. Consistent density functional theory-based description of ion hydration through density-corrected many-body

        representations. E. Palos, A. Caruso, F. Paesani. In review. [link]

Delocalization error constrains the accuracy of density functional theory (DFT) in describing molecular interactions in

ion–water systems. Using Na+ and Cl in water as model systems, we calculate the effects of delocalization error in

the SCAN functional for describing ion–water and water–water interactions in hydrated ions, and demonstrate that

density-corrected SCAN (DC-SCAN) predicts n-body and interaction energies with an accuracy approaching coupled

cluster theory. The performance of DC-SCAN is size-consistent, maintaining an accurate description of molecular

in- teractions well beyond the first solvation shell. Molecular dynamics simulations at ambient conditions with

MB-SCAN(DC) potentials, derived from the many-body expansion, predict the solvation structure of Na+ and Cl

in quantitative agreement with reference data, while simultaneously reproducing the structure of liquid water.

Beyond rationalizing the accuracy of density-corrected models of ion hydration, our findings suggest that our unified

density-corrected many-body formalism holds great promise for efficient DFT-based simulations of condensed-phase

systems with chemical accuracy.

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

        density functionals. S. Dasgupta, E. Palos, Y. Pan, F. Paesani. In review. [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.

185. Humidity-responsive polymorphism in CALF-20: A resilient MOF physisorbent for CO2 capture.

        Z. Chen, C.-H. Ho, X. Wang, S.M. Vornholt, T.M. Rayder, T. Islamoglu, O.K. Farha, F. Paesani, K.W. Chapman,

        ACS Materials Lett. 5, 2942 (2023). [link]

We find that the originally reported CALF-20 phase (now denoted α-CALF-20) undergoes a structure

transformation following exposure to humid environments to generate a new polymorph, β-CALF-20,

identified here through combined synchrotron powder X-ray diffraction and pair distribution function (PDF)

analysis. This α-to-β transformation is fully reversible with α-CALF-20 being regenerated by treating

β-CALF-20 under dry air or vacuum. The rapid reversion of β- to α-CALF-20 under conditions relevant to

measuring the gas adsorption isotherm required the CO2 adsorption properties for β-CALF-20 to be

evaluated computationally. Experimental evaluation of the adsorption behavior of β-CALF-20 is not

practical. These analyses suggest that β-CALF-20 has a higher CO2 heat of adsorption than α-CALF-20,

which may be advantageous to CO2 sorption selectivity at low partial pressures.