170. 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. In press.
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.
169. Accurate modeling of bromide and iodide hydration with data-driven many-body potentials.
A. Caruso, X. Zhu, J.L. Fulton, F. Paesani. J. Phys. Chem. B. 126, 8266 (2022).
A quantitative understanding of how the hydration properties of ions evolve from small aqueous clusters
to bulk solutions and interfaces remains elusive. Here, we introduce the second generation of data-driven
many-body energy (MB-nrg) potential energy functions (PEFs) representing bromide–water and
iodide–water interactions. The MB-nrg PEFs use permutationally invariant polynomials to reproduce
2-body and 3-body coupled cluster energies, and implicitly represent all higher-body energies using
classical many-body polarization. A systematic analysis of the hydration structure of small Br−(H2O)n and
I−(H2O)n clusters demonstrates that the MB-nrg PEFs predict interaction energies in quantitative agreement
with “gold standard” coupled cluster reference values. Importantly, when used in molecular dynamics
simulations carried out in the isothermal-isobaric ensemble for single bromide and iodide ions in liquid
water, the MB-nrg PEFs predict EXAFS spectra that accurately reproduce the experimental spectra, which
thus allows for characterizing the hydration structure of the two ions with high level of confidence.
167. Phase diagram of the TIP4P/Ice water model by enhanced sampling simulations.
S.L. Bore, P.M. Piaggi, R. Car, F. Paesani. J. Chem. Phys. 157, 054504 (2022).
In this study, we calculate the phase diagram for the TIP4P/Ice water model using enhanced sampling
molecular dynamics simulations. Our approach is based on the calculation of ice-liquid free energy
differences from biased coexistence simulations that sample reversibly the melting and growth of layers
of ice. We compute a total of 19 melting points for five different ice polymorphs which are in excellent
agreement with the melting lines obtained from the integration of the Clausius-Clapeyron equation.
For proton-ordered and fully proton-disordered ice phases, the results are in very good agreement with
previous calcu- lations based on thermodynamic integration. For the partially-proton-disordered ice III,
we find a large increase in stability that is in line with previous observations using direct coexistence
simulations for the TIP4P/2005 model. This issue highlights the robustness of the approach employed
here for ice polymorphs with diverse degrees of proton disorder. Our approach is general and can be
applied to the calculation of other complex phase diagrams.
159. Combined theoretical, bioinformatic, and biochemical analyses of RNA editing by adenine base editors.
K.L. Rallapalli, B.L. Ranzau, K.R. Ganapathy, F. Paesani, A.C. Komor. CRISPR J. 5, 294 (2022).
Adenine base editors (ABEs) have been subjected to multiple rounds of mutagenesis with the goal of optimizing
their function as efficient and precise genome editing agents. Despite this ever-increasing data accumulation of
the effects that these mutations have on the activity of ABEs, the molecular mechanisms defining these changes
in activity remain to be elucidated. In this study, we provide a systematic interpretation of the nature of these
mutations using an entropy-based classification model that relies on evolutionary data from extant protein
sequences. Using this model in conjunction with experimental analyses, we identify two previously reported
mutations that form an epistatic pair in the RNA-editing functional landscape of ABEs. Molecular dynamics
simulations reveal the atomistic details of how these two mutations affect substrate-binding and catalytic activity,
via both individual and cooperative effects, hence providing insights into the mechanisms through which
these two mutations are epistatically coupled.
164. The behavior of methane-water mixtures under elevated pressures using many-body potentials.
V. Naden Robinson, R. Ghosh, C.K. Egan, M. Riera, C. Knight, F. Paesani, A. Hassanali.
J. Chem. Phys. 156, 194504 (2022).
Non-polarizable empirical potentials have been shown not to be able to capture the mixing of CH4-H2O mixtures
at elevated pressures. Here we show that the many-body MB-nrg potential, designed to reproduce CH4-H2O
interactions with coupled cluster accuracy, successfully captures this phenomenon up to 3 GPa and 500 K with
varying methane concentration. Two-phase simulations and long time scales that are required to fully capture
the mixing, affordable due to the speed and accuracy of the MBX software, are assessed. Constructing the
methane–water equation of state across the phase diagram shows that the stable mixtures are denser than
the sum of their parts at a given pressure and temperature. We find that many-body polarization plays a central role,
enhancing the induced dipole moments of methane by 0.20 D during mixing under pressure. Overall, the mixed
system adopts a denser state, which involves a significant enthalpic driving force as elucidated by a systematic
many-body energy decomposition analysis.
163. Assessing the interplay between functional-driven and density-driven errors in DFT models of water.
E. Palos, E. Lambros, S. Swee, J. Hu, S. Dasgupta, F. Paesani. J. Chem. Theory Comput. 18, 3410 (2022).
We investigate the interplay between functional-driven and density-driven errors in different density functional
theory (DFT) approximations, and the implications of these errors for simulations of water with DFT-based
data-driven many-body potentials. Our analyses demonstrate that functional-driven errors are strongly correlated
with the nature of the interactions. We discuss cases where density-corrected DFT (DC-DFT) models display
higher accuracy than the original DFT models, and cases where reducing the density-driven errors leads to larger
deviations from the reference energies due to the presence of large functional-driven errors. Finally, MD simulations
are performed with data-driven many-body potentials derived from DFT and DC-DFT data to determine the effect
that minimizing density-driven errors has on the description of liquid water. Besides rationalizing the performance
of widely used DFT models of water, our findings unveil fundamental relations between the shortcomings of some
common DFT models and the requirements for accurate descriptions of molecular interactions, which will aid the
development of a consistent, DFT-based framework for data-driven simulations of condensed-phase systems.
© Paesani Research Group. All rights reserved.
160. Static and dynamic statistical correlations in water: Comparison of classical ab initio molecular dynamics at
elevated temperature with path integral simulations at ambient temperature.
C. Li, F. Paesani, G.A. Voth. J. Chem. Theory. Comput. 18, 2124 (2022).
It is a common practice in ab initio molecular dynamics (AIMD) simulations of water to use an elevated
temperature to overcome the over-structuring and slow diffusion predicted by most current density functional
theory (DFT) models. The simulation results obtained in this distinct thermodynamic state are then compared
with experimental data at ambient temperature based on the rationale that a higher temperature effectively
recovers nuclear quantum effects (NQEs) that are missing in the classical AIMD simulations. In this work,
we systematically examine the foundation of this assumption for several DFT models as well as for the
many-body MB-pol model. We find for the cases studied that a higher temperature does not correctly
mimic NQEs at room temperature, which is especially manifest in significantly different three-molecule
correlations as well as hydrogen bond dynamics. In many of these cases, the effects of NQEs are
the opposite of the effects of carrying out the simulations at an elevated temperature.
161. The anomalies and local structure of liquid water from many-body molecular dynamics simulations.
T.E. Gartner III, K.M. Hunter, E. Lambros, A. Caruso, M. Riera, G.R. Medders, A.Z. Panagiotopoulos,
P.G. Debenedetti, F. Paesani. J. Phys. Chem. Lett. 13, 3652 (2022).
For the last 50 years, researchers have sought molecular models that can accurately reproduce water’s microscopic
structure and thermophysical properties across broad ranges of its complex phase diagram. Herein, molecular
dynamics simulations with the many-body MB-pol model are performed to monitor the thermodynamic response
functions and local structure of liquid water from the boiling point down to deeply supercooled temperatures at
ambient pressure. The isothermal compressibility and isobaric heat capacity show maxima at ~223 K, in excellent
agreement with recent experiments, and the liquid density exhibits a minimum at ~208 K. Furthermore, a local
tetrahedral arrangement, where each water molecule accepts and donates two hydrogen bonds, is the most
probable hydrogen-bonding topology at all temperatures. This work suggests that MB-pol may provide predictive
capability for studies of liquid water’s physical properties across broad ranges of thermodynamic states.
162. Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases.
S. Yue, M. Riera, R. Ghosh, A.Z. Panagiotopoulos, F. Paesani. J. Chem. Phys. 156, 104503 (2022).
Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting 1-body
and 2-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer
training sets. Advancing from the first generation models, we employ the Charge Model 5 scheme to determine
the atomic charges and systematically scale the 2-body energies to obtain more accurate descriptions of vapor,
liquid, and vapor-liquid equilibrium properties. Comparisons with the polarizable TTM-nrg model, which is
constructed from the same training sets as the MB-nrg models but using a simpler representation of short-range
interactions based on conventional Born-Mayer functions, showcase the necessity of high dimensional
functional forms for an accurate description of the multidimensional energy landscape of liquid CO2. These
findings emphasize the key role played by the training set quality and flexibility of the fitting functions in
the development of transferable, data-driven models which, accurately representing high-dimensional
many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.
165. Density functional theory of water with the machine-learned DM21 functional.
E. Palos, E. Lambros, S. Dasgupta, F. Paesani. J. Chem. Phys. 156, 161103 (2022).
The interplay between functional-driven and density-driven errors in density functional theory has hindered
traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years.
Recently, the deep-learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional
DFAs as it is free of delocalization error. Here, we assess the accuracy of the DM21 functional for neutral, protonated,
and deprotonated water clusters. We find that the ability of DM21 to accurately predict the energetics of aqueous clusters
varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21
data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature
at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics
of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating
the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature.
166. How good is the density-corrected SCAN functional for neutral and ionic aqueous systems, and what is so
right about the Hartree-Fock density? S. Dasgupta, C. Shahi, P. Bhetwal, J.P. Perdew, F. Paesani.
J. Chem. Theory Comput. 18, 4745 (2022).
We present extensive calculations aimed at determining the accuracy of the DC-SCAN functional for various
aqueous systems. DC-SCAN shows remarkable consistency in reproducing coupled cluster reference data. By
comparison with the orbital-optimized CCSD density in the water dimer, we find that the self-consistent SCAN
density transfers a spurious fraction of an electron across the hydrogen bond to the hydrogen atom from the
acceptor and donor oxygen atoms, while HF makes a much smaller spurious transfer in the opposite direction,
consistent with DC-SCAN reduction of SCAN over-binding due to delocalization error. While LDA seems to be
the conventional extreme of density delocalization error, and HF the conventional extreme of (usually smaller)
density localization error, these two densities do not quite yield the conventional range of density-driven error in
energy differences. Comparisons of the DC-SCAN results with those obtained with the FLOSIC method show
that DC-SCAN represents a more accurate approach to reducing density-driven errors.
168. 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. In press.
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.
171. A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or
learning the underlying physics? Y. Zhai, A. Caruso, S.L. Bore, F. Paesani. Under review.
We explore the possibility of combining the computational efficiency of the DeePMD framework 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 properties but losing accuracy in the description of the liquid properties.
These results suggest that DeePMD-based DNN potentials are not able to correctly “learn” and, consequently, represent
many-body interactions. The computational efficiency of the DeePMD framework can still be exploited to train DNN
potentials on data-driven many-body potentials, which can thus enable large-scale, chemically accurate simulations of
various molecular systems.
172. Hydration structure of Na+ and K+ ions in solution predicted by data-driven many-body potentials.
D. Zhuang, M. Riera, R. Zhou, A. Deary, F. Paesani. J. Phys. Chem. B. 126, 9349 (2022).
The hydration structure of Na+ and K+ ions in solution is systematically investigated using a hierarchy of molecular models
that progressively include more accurate representations of many-body interactions. We found that conventional empirical
pairwise additive force field that is commonly used in biomolecular simulations is unable to reproduce EXAFS spectra
for both ions. In contrast, progressive inclusion of many-body effects rigorously derived from the many-body expansion
of the energy allows the MB-nrg potential energy functions (PEFs) to achieve nearly quantitative agreement with the
experimental EXAFS spectra, thus enabling the development of a molecular-level picture of the hydration structure of
both Na+ and K+ in solution. Since the MB-nrg PEFs have already been shown to accurately describe isomeric equilibria
and vibrational spectra of small ion–water clusters in the gas phase, the present study demonstrates that the MB-nrg PEFs
effectively represent the long-sought-after models able to correctly predict the properties of ionic aqueous systems
from the gas to the liquid phase, which has so far remained elusive.
173. Data-driven many-body potentials from density functional theory for aqueous phase chemistry.
E. Palos, S. Dasgupta, E. Lambros, F. Paesani. Under review.
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.