Ab Initio Force Field Research Articles

The role of the water contact layer on hydration and transport at solid/liquid interfaces

Understanding the structure in the nanoscopic region of water that is in direct contact with solid surfaces, so-called contact layer, is key to quantifying macroscopic properties that are of interest to e.g. catalysis, ice nucleation, nanofluidics, gas adsorption, and sensing. We explore the structure of the water contact layer on various technologically relevant solid surfaces, namely graphene, MoS[Formula: see text], Au(111), Au(100), Pt(111), and Pt(100), which have been previously hampered by time and length scale limitations of ab initio approaches or force field inaccuracies, by means of molecular dynamics simulations based on ab initio machine learning potentials built using an active learning scheme. Our results reveal that the in-plane intermolecular correlations of the water contact layer vary greatly among different systems: Whereas the contact layer on graphene and on Au(111) is predominantly homogeneous and isotropic, it is inhomogeneous and anisotropic on MoS[Formula: see text], on Au(100), and on the Pt surfaces, where it additionally forms two distinct sublayers. We apply hydrodynamics and the theory of the hydrophobic effect, to relate the energy corrugation and the characteristic length-scales of the contact layer with wetting, slippage, the hydration of small hydrophobic solutes and diffusio-osmotic transport. Thus, this work provides a microscopic picture of the water contact layer and links it to macroscopic properties of liquid/solid interfaces that are measured experimentally and that are relevant to wetting, hydrophobic solvation, nanofluidics, and osmotic transport.

Interfacial polymerization mechanisms assisted flame retardancy process of low-flammable electrolytes on lithium anode

Thermal runaway is a hazardous risk, occurring more readily in high-energy–density lithium-ion batteries (LIBs), which leads to a rapid temperature rise and even combustion or explosion when using flammable electrolyte systems. Flame retardants (FRs), such as trimethyl phosphate (TMPa) and triethyl phosphate (TEP), are commonly utilized due to their effective flame suppression, low toxicity, and excellent thermal stability. However, the lack of in-depth understanding of the flame retardancy mechanism and solid electrolyte interphase (SEI) formation process has made the development of functional electrolytes difficult at present. In this study, we clarified the flame retardancy and interfacial reaction mechanisms of low-flammable TMPa localized high-concentration electrolytes (LHCE) using hybrid ab initio and reactive force field (HAIR) schemes. Long-term HAIR simulation reveals that phosphorous radicals produced by the decomposition of TMPa capture carbon radicals, encouraging their polymerization into low-flammable oligomers, while fluorine-containing solvents in the electrolyte capture hydrogen radicals and produce nonflammable hydrofluoric acid (HF). This synergistic flame retardancy mechanism provides essential atomic-level insights for the rational design of high-safety electrolytes in the future.

PhyNEO: A Neural-Network-Enhanced Physics-Driven Force Field Development Workflow for Bulk Organic Molecule and Polymer Simulations.

An accurate, generalizable, and transferable force field plays a crucial role in the molecular dynamics simulations of organic polymers and biomolecules. Conventional empirical force fields often fail to capture precise intermolecular interactions due to their negligence of important physics, such as polarization, charge penetration, many-body dispersion, etc. Moreover, the parameterization of these force fields relies heavily on top-down fittings, limiting their transferabilities to new systems where the experimental data are often unavailable. To address these challenges, we introduce a general and fully ab initio force field construction strategy, named PhyNEO. It features a hybrid approach that combines both the physics-driven and the data-driven methods and is able to generate a bulk potential with chemical accuracy using only quantum chemistry data of very small clusters. Careful separations of long-/short-range interactions and nonbonding/bonding interactions are the key to the success of PhyNEO. By such a strategy, we mitigate the limitations of pure data-driven methods in long-range interactions, thus largely increasing the data efficiency and the scalability of machine learning models. The new approach is thoroughly tested on poly(ethylene oxide) and polyethylene glycol systems, giving superior accuracies in both microscopic and bulk properties compared to conventional force fields. This work thus offers a promising framework for the development of advanced force fields in a wide range of organic molecular systems.

Impact of lithium nitrate additives on the solid electrolyte interphase in lithium metal batteries

Lithium metal batteries (LMBs) represent a promising frontier in energy storage technology, offering high energy density but facing significant challenges. In this work, we address the critical challenge of lithium dendrite formation in LMBs, a key barrier to their efficiency and safety. Focusing on the potential of electrolyte additives, specifically lithium nitrate, to inhibit dendritic growth, we employ advanced multi-scale simulation techniques to explore the formation and properties of the solid electrolyte interphase (SEI) on the anode surface. Our study introduces a novel hybrid simulation methodology, HAIR (Hybrid ab initio and Reactive force field Molecular Dynamics), which combines ab initio molecular dynamics (AIMD) and reactive force field molecular dynamics (RMD). This approach allows for a more precise and reliable examination of the interaction mechanisms of nitrate additives within LMBs. Our findings demonstrate that lithium nitrate contributes to the formation of a stable and fast ionic conductor interface, effectively suppressing dendrite growth. These insights not only advance our understanding of dendrite formation and mitigation strategies in lithium metal batteries, but also highlight the efficacy of HAIR as a pioneering tool for simulating complex chemical interactions in battery materials, offering significant implications for the broader field of energy storage technology.

Rotational spectroscopic characterisation of the [D2,C,S] system: an update from the laboratory and theory

The synergy between high-resolution rotational spectroscopy and quantum-chemical calculations is essential for exploring future detection of molecules, especially when spectroscopy parameters are not available yet. By using highly correlated ab initio quartic force fields (QFFs) from explicitly correlated coupled cluster theory, a complete set of rotational constants and centrifugal distortion constants for D 2 CS and cis/trans-DCSD isomers have been produced. Comparing our new ab initio results for D 2 CS with new rotational spectroscopy laboratory data for the same species, the accuracy of the computed B and C rotational constants is within 0.1% while the A constant is only slightly higher. Additionally, quantum chemical vibrational frequencies are also provided, and these spectral reference data and new experimental rotational lines will provide additional references for potential observation of these deuterated sulfur species with either ground-based radio telescopes or space-based infrared observatories.

Stiffness and Atomic-Scale Friction in Superlubricant MoS2 Bilayers.

Molecular dynamics simulations, performed with chemically accurate ab initio machine-learning force fields, are used to demonstrate that layer stiffness has profound effects on the superlubricant state of two-dimensional van der Waals heterostructures. We engineer bilayers of different rigidity but identical interlayer sliding energy surface and show that a 2-fold increase in the intralayer stiffness reduces the friction by a factor of ∼6. Two sliding regimes as a function of the sliding velocity are found. At a low velocity, the heat generated by the motion is efficiently exchanged between the layers and the friction is independent of the layer order. In contrast, at a high velocity, the friction heat flux cannot be exchanged fast enough and a buildup of significant temperature gradients between the layers is observed. In this situation, the temperature profile depends on whether the slider is softer than the substrate.

Development and Implementation of Atomically Anisotropic First-Principles Force Fields: A Benzene Case Study.

π-interactions are an important motif in chemical and biochemical systems. However, due to their anisotropic electron densities and complex balance of intermolecular interactions, aromatic molecules represent an ongoing challenge for accurate and transferable force field development. Historically, ab initio force fields for aromatics have not exhibited good accuracy with respect to bulk properties or have only been used to study gas-phase dimers. Using benzene as a proof of concept, herein we show how our own ab initio MASTIFF force field incorporates an atomically anisotropic description of intermolecular interactions to yield an accurate and robust model for aromatic interactions irrespective of phase. Compared to existing models, the MASTIFF benzene force field not only is accurate for liquid phase properties but also offers transferability to the gas and solid phases. Additionally, we introduce a computationally efficient OpenMM plugin which enables customizable anisotropic intermolecular functional forms and which can be generically used in any MD simulation where a model for nonspherical atomic features is required. Overall, our results demonstrate the importance of atomic-level anisotropy in enabling next-generation ab initio force field development.

Complete, Theoretical Rovibronic Spectral Characterization of the Carbon Monoxide, Water, and Formaldehyde Cations

New high-level ab initio quartic force field (QFF) methods are explored which provide spectroscopic data for the electronically excited states of the carbon monoxide, water, and formaldehyde cations, sentinel species for expanded, recent cometary spectral analysis. QFFs based on equation-of-motion ionization potential (EOM-IP) with a complete basis set extrapolation and core correlation corrections provide assignment for the fundamental vibrational frequencies of the A˜2B1 and B˜2A1 states of the formaldehyde cation; only three of these frequencies have experimental assignment available. Rotational constants corresponding to these vibrational excitations are also provided for the first time for all electronically excited states of both of these molecules. EOM-IP-CCSDT/CcC computations support tentative re-assignment of the ν1 and ν3 frequencies of the B˜2B2 state of the water cation to approximately 2409.3 cm-1 and 1785.7 cm-1, respectively, due to significant disagreement between experimental assignment and all levels of theory computed herein, as well as work by previous authors. The EOM-IP-CCSDT/CcC QFF achieves agreement to within 12 cm-1 for the fundamental vibrational frequencies of the electronic ground state of the water cation compared to experimental values and to the high-level theoretical benchmarks for variationally-accessible states. Less costly EOM-IP based approaches are also explored using approximate triples coupled cluster methods, as well as electronically excited state QFFs based on EOM-CC3 and the previous (T)+EOM approach. The novel data, including vibrationally corrected rotational constants for all states studied herein, provided by these computations should be useful in clarifying comet evolution or other remote sensing applications in addition to fundamental spectroscopy.

Structure and Surface Relaxation of CeO2 Nanoparticles Unveiled by Combining Real and Reciprocal Space Total Scattering Analysis.

We present a combined real and reciprocal space structural and microstructural characterization of CeO2 nanoparticles (NPs) exhibiting different crystallite sizes; ~3 nm CeO2 NPs were produced by an inverse micellae wet synthetic path and then annealed at different temperatures. X-ray total scattering data were analyzed by combining real-space-based Pair Distribution Function analysis and the reciprocal-space-based Debye Scattering Equation method with atomistic models. Subtle atomic-scale relaxations occur at the nanocrystal surface. The structural analysis was corroborated by ab initio DFT and force field calculations; micro-Raman and electron spin resonance added important insights to the NPs’ defective structure. The combination of the above techniques suggests a core-shell like structure of ultrasmall NPs. These exhibit an expanded outer shell having a defective fluorite structure, while the inner shell is similar to the bulk structure. The presence of partially reduced species testifies to the high surface activity of the NPs. On increasing the annealing temperature, the particle dimensions increase, limiting disorder as a consequence of the progressive surface-to-volume ratio reduction.

Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach.

Molecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the application of ab initio potential energy surface, the nuclear quantum effect (NQE) is becoming increasingly important for the robustness of the simulation. However, the state-of-the-art path-integral molecular dynamics simulation, which incorporates NQE in MM, is still too expensive to conduct for most biological and material systems. In this work, we analyze the locality of NQE, using both analytical and numerical approaches, and conclude that NQE is an extremely localized phenomenon in nonreactive molecular systems. Therefore, we can use localized machine learning (ML) models to predict quantum force corrections both accurately and efficiently. Using liquid water as example, we show that the ML facilitated centroid MD can reproduce the NQEs in both the thermodynamical and the dynamical properties, with a minimal increase in computational time compared to classical molecular dynamics. This simple approach thus largely decreases the computational cost of quantum simulations, making it really accessible to the studies of large-scale molecular systems.

Formation of Linear Oligomers in Solid Electrolyte Interphase via Two‐Electron Reduction of Ethylene Carbonate

AbstractSolid electrolyte interphase (SEI) plays a significant role in enhancing the stability and durability of lithium metal batteries (LMBs) by separating highly reactive lithium metal anode (LMA) from the electrolyte to avoid continuous degradation. However, the underlying reaction mechanism is still far from clear. Herein, a hybrid ab initio and reactive force field (HAIR) method is employed to extend the ab initio molecular dynamics (AIMD) to 1 ns, which provides crystal information about the reaction mechanism of elementary reactions that can explain the components and morphology evolution of SEI formation. Specifically, HAIR simulation confirms the two‐electron (2e–) reduction of ethylene carbonate (EC) by releasing CO and CO2, agreeing with phenomenal experiment observation. As the unsaturated intermediates accumulate, polymerization reactions occur, producing linear polyethylene oxide (PEO), Li2OCO2CH2CH2, Li2OCO2(CH2)4, etc., which regulate the formation of outer organic layer (OOL) that consists of linear polyethylene oxide (PEO), Li2OCO2CH2CH2, Li2OCO2(CH2)4, etc., and the inner inorganic layer (IIL) mainly consists of LiF and Li2O. Simulations at low concentration (LC, 1M) and high concentration (HC, 5M) reveal significantly different reaction pathways when HC electrolyte can significantly promote the formation of homogenous LiF that has been regarded as an important component to facilitate robust SEI.

Deep learning neural network potential for simulating gaseous adsorption in metal–organic frameworks

This study proposes ab initio neural network force fields with physically motivated features to offer superior accuracy in describing adsorbate–adsorbent interactions of nonpolar (CO2) and polar (H2O and CO) molecules in metal–organic frameworks with open-metal sites.

Reduction Mechanism of Solid Electrolyte Interphase Formation on Lithium Metal Anode: Fluorine-Rich Electrolyte

Metallic lithium is considered a promising anode that can significantly increase the energy density of rechargeable lithium-based batteries, but problems like uncontrollable growth of lithium dendrites and formation of dead lithium impede its application. Recently, a low-concentration single-salt two-solvent electrolyte, 1 M LiTFSI/FDMA/FEC, has attracted attention because a high coulombic efficiency can be achieved even after many cycles owing to the formation of a robust solid electrolyte interface (SEI). However, the reaction mechanism and SEI structure remain unclear, posing significant challenges for further improvement. Here, a hybrid ab initio and reactive force field (HAIR) method revealed the underlying reaction mechanisms and detailed formation pathway. 1 ns HAIR simulation provides critical information on the initial reduction mechanism of solvent (FDMA and FEC) and salt (LiTFSI). FDMA and FEC quickly decompose to provide F- that builds LiF as the major component of the inner layer of inorganic SEI, which has been demonstrated to protect Li anode. Decomposition of FDMA also leads to a significant nitrogen-containing composition, producing Li-N-C, LixN, and other organic components that increase the conductivity of SEI to increase performance. XPS analysis confirms evolution of SEI morphology consistent with available experiments. These results provide atomic insight into SEI formation, which should be beneficial for the rational design of advanced electrolytes.

A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules

An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accurate correlated wave function (CW) methods scale poorly with system size, so this poses a great challenge to the development of an extendible ab initio force field for large flexible organic molecules at the CW level of accuracy. In this work, we combine the physics-driven nonbonding potential with a data-driven subgraph neural network bonding model (named sGNN). Tests on polyethylene glycol, polyethene, and their block polymers show that our strategy is highly accurate and robust for molecules of different sizes and chemical compositions. Therefore, one can develop a parameter library of small molecular fragments (with sizes easily accessible to CW methods) and assemble them to predict the energy of large polymers, thus opening a new path to next-generation organic force fields.

Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles.

The message passing neural network (MPNN) framework is a promising tool for modeling atomic properties but is, until recently, incompatible with directional properties, such as Cartesian tensors. We propose a modified Cartesian MPNN (CMPNN) suitable for predicting atom-centered multipoles, an essential component of ab initio force fields. The efficacy of this model is demonstrated on a newly developed dataset consisting of 46 623 chemical structures and corresponding high-quality atomic multipoles, which was deposited into the publicly available Molecular Sciences Software Institute QCArchive server. We show that the CMPNN accurately predicts atom-centered charges, dipoles, and quadrupoles and that errors in the predicted atomic multipoles have a negligible effect on multipole-multipole electrostatic energies. The CMPNN is accurate enough to model conformational dependencies of a molecule's electronic structure. This opens up the possibility of recomputing atomic multipoles on the fly throughout a simulation in which they might exhibit strong conformational dependence.

Equilibrium molecular structure of orotic acid from gas-phase electron diffraction data and quantum-chemical calculations

The accurate equilibrium structures of planar anti and syn orotic acid conformers with different positions of the carboxyl group with respect to the C=C bond were determined from gas-phase electron diffraction data using the dynamic model and taking into account anharmonic vibrational corrections calculated with ab initio force field. The high accuracy of coupled-cluster computations was exploited in the analysis of structural effects due to electron-acceptor substituents.

Identifying the Bottleneck for Heat Transport in Metal–Organic Frameworks

AbstractControlling the transport of thermal energy is key to most applications of metal–organic frameworks (MOFs). Analyzing the evolution of the effective local temperature, the interfaces between the metal nodes and the organic linkers are identified as the primary bottlenecks for heat conduction. Consequently, changing the bonding strength at that node–linker interface and the mass of the metal atoms can be exploited to tune the thermal conductivity. This insight is generated employing molecular dynamics simulations in conjunction with advanced, ab initio parameterized force fields. The focus of the present study is on MOF‐5 as a prototypical example of an isoreticular MOF. However, the key findings prevail for different node structures and node–linker bonding chemistries. The presented results lay the foundation for developing detailed structure‐to‐property relationships for thermal transport in MOFs with the goal of devising strategies for the application‐specific optimization of heat conduction.

Molecular structure of 5-fluorouracil from gas-phase electron diffraction data and quantum-chemical calculations

The accurate equilibrium molecular structure of a canonical tautomer of 5-fluorouracil was determined by electron diffraction taking into account anharmonic vibrational corrections calculated with ab initio force field. This structure was applied to benchmark the results of CCSD(T) computations used for the analysis of substitution effects in uracil derivatives.

Regularized ab initio molecular force fields for key biological molecules: melatonin and pyridoxal-5′-phosphate methylamine Shiff base (Vitamin B6)

The main mathematical results on the data processing in vibrational spectroscopy are presented. The approaches and algorithms proposed for molecular force field calculations have been constructed on a base of regularizing methods for solving nonlinear ill-posed problems and have been implemented in the software package SPECTRUM. These algorithms were used for constructing the regularized ab initio force fields of important biological molecules including the melatonin and Vitamin B6 derivatives.

Adsorption of H2S from Hydrocarbon Gas Using Doped Bentonite: A Molecular Simulation Study.

Hydrogen sulfide is a commonly occurring impurity in hydrocarbon gases such as natural gas or landfill gas. Apart from its toxicity, H2S can cause problems in downstream processing because of corrosion of piping in the presence of moisture. Removing this contaminant using a cost-effective and energy-efficient technique such as adsorption using commonly occurring adsorbents would be beneficial both for processing and refinement of hydrocarbon gases and for their use as an energy source. In this work, grand canonical Monte Carlo simulations were performed using an ab initio forcefield to predict adsorption isotherms for methane, hydrogen sulfide, and nitrogen in bentonite doped with K+, Li+, and Na+ cations with a view to aiding the development of low-cost pressure-swing adsorption systems for the targeted removal of H2S from landfill gas or natural gas. Pure species simulations were done, in addition to considering mixtures at conditions approximating real-world natural gas fields. Highly selective targeted adsorption of hydrogen sulfide was achieved for all three doped bentonites, with the adsorbed phase consisting of almost pure H2S, although the volume of gas adsorbed differed between adsorbents. The results suggest the following ranking for the three doped bentonite adsorbents in terms of their overall performance: K+ > Li+ > Na+. By considering both the composition of the adsorbed phase and the total quantity of adsorbed gas, there may be an interplay between the gas–gas and gas–solid interactions that becomes somewhat noticeable at low pressures.