Energy Density Functional Theory Research Articles

Systematic structural study in praseodymium compressed in a neon pressure medium up to 185 GPa

Angle-dispersive x-ray powder diffraction experiments have been performed on praseodymium metal compressed in a soft pressure-transmitting medium at ambient temperature up to 185 GPa. We observe the previously reported high-pressure structural transition sequence up to 20 GPa and the coexistence of body-centered orthorhombic (bco) Pr and \ensuremath{\alpha}-U Pr from $\ensuremath{\sim}20$ up to $\ensuremath{\sim}38\phantom{\rule{0.16em}{0ex}}\mathrm{GPa}$. The \ensuremath{\alpha}-U structure of Pr is stable from 20 to 185 GPa, and no evidence of the proposed transition to a primitive orthorhombic phase $>147\phantom{\rule{0.16em}{0ex}}\mathrm{GPa}$ was observed. With density functional theory (DFT), we calculated the lattice parameters and $y$ coordinate for \ensuremath{\alpha}-U Pr and found good agreement between our calculations and experimental measurements. The obtained DFT energies of the proposed primitive orthorhombic $(P{2}_{1}{2}_{1}{2}_{1})$ and the \ensuremath{\alpha}-uranium phases at $\ensuremath{\sim}150\phantom{\rule{0.16em}{0ex}}\mathrm{GPa}$ show that the \ensuremath{\alpha}-uranium phase is lower in energy. Hence, neither our experimental data nor our DFT results support the transition to a primitive orthorhombic phase $>150\phantom{\rule{0.16em}{0ex}}\mathrm{GPa}$. DFT suggests, however, that Pr may transform to the $P{2}_{1}{2}_{1}{2}_{1}$ phase above $\ensuremath{\sim}220\phantom{\rule{0.16em}{0ex}}\mathrm{GPa}$. We also compare the axial ratios and lattice parameters of praseodymium to \ensuremath{\alpha}-uranium structured Nd, Ce, and U.

First steps towards achieving both ultranonlocality and a reliable description of electronic binding in a meta-generalized gradient approximation

It has been demonstrated that a meta-generalized gradient approximation (meta-GGA) to the exchange-correlation energy of density functional theory can show a pronounced derivative discontinuity and significant ultranonlocality similar to exact exchange, and can accurately predict the band gaps of many solids. We here investigate whether within the meta-GGA form these properties are compatible with a reasonable accuracy for electronic binding energies. With the help of two transparent and inexpensive correlation functional constructions we demonstrate that this is the case. We report atomization energies, show that reliable bond lengths are obtained for many systems, and find promising results for reaction barrier heights, while keeping the strong derivative discontinuity and ultranonlocality, and thus accuracy for band gaps.

Polarization energies in the fragment molecular orbital method

Using isolated and polarized states of fragments, a method for computing the polarization energies in density functional theory (DFT) and density-functional tight-binding (DFTB) is developed in the framework of the fragment molecular orbital method. For DFTB, the method is extended into the use of periodic boundary conditions (PBC), for which a new component, a periodic self-polarization energy, is derived. The couplings of the polarization to other components in the pair interaction energy analysis (PIEDA) are derived for DFT and DFTB, and compared to Hartree-Fock and second-order Møller-Plesset perturbation theory (MP2). The effect of the self-consistent (DFT) and perturbative (MP2) treatment of the electron correlation on the polarization is discussed. The difference in the polarization in the bulk (PBC) and micro (cluster) solvation is elucidated.

Achieving Theory-Experiment Parity for Activity and Selectivity in Heterogeneous Catalysis Using Microkinetic Modeling.

ConspectusMicrokinetic modeling based on density functional theory (DFT) energies plays an essential role in heterogeneous catalysis because it reveals the fundamental chemistry for catalytic reactions and bridges the microscopic understanding from theoretical calculations and experimental observations. Microkinetic modeling requires building a set of ordinary differential equations (ODEs) based on the calculation results of thermodynamic properties of adsorbates and kinetic parameters for the reaction elementary steps. Solving a microkinetic model can extract information on catalytic chemistry, including critical reaction intermediates, reaction pathways, the surface species distribution, activity, and selectivity, thus providing vital guidelines for altering catalysts.However, the quantitative reliability of traditional microkinetic models is often insufficient to conclusively extrapolate the mechanistic details of complex reaction systems. This can be attributed to several factors, the most important of which is the limitation of obtaining an accurate estimation of the energy inputs via traditional calculation methods. These limitations include the difficulty of using static DFT methods to calculate reaction energies of adsorption/desorption processes, often rate-controlling or selectivity-determining steps, and the inadequate consideration of surface coverage effects. In addition, the robust microkinetic software is rare, which also complicates the resolution of complex catalytic systems.In this Account, we review our recent works toward refining the predictions of microkinetic modeling in heterogeneous catalysis and achieving theory–experiment parity for activity and selectivity. First, we introduce CATKINAS, a microkinetic software developed in our group, and show how it disentangles the problem that traditional microkinetic software has and how it can now be applied to obtain kinetic results for more sophisticated reaction systems. Second, we describe a molecular dynamics method developed recently to obtain the free-energy changes for the adsorption/desorption process to fill in the missing energy inputs. Third, we show that a rigorous consideration of surface coverage effects is pivotal for building more realistic models and obtaining accurate kinetic results. Following a series of studies on acetylene hydrogenation reactions on Pd catalysts, we demonstrate how this new approach can provide an improved quantitative understanding of the mechanism, active site, and intrinsic structural sensitivity. Finally, we conclude with a brief outlook and the remaining challenges in this field.

Bond-Valence Parameterization for the Accurate Description of DFT Energetics.

We report a bond-valence method (BVM) parameterization framework that captures density functional theory (DFT)-computed relative stabilities using the BVM global instability index (GII). We benchmarked our framework against a dataset of 188 experimentally observed ABO3 perovskite oxides, each of which was generated in 11 unique Glazer octahedral tilt systems and optimized using DFT. Our constrained minimization procedure minimizes the GIIs of the 188 perovskite ground state structures predicted by DFT while enforcing a linear correlation between the GIIs and DFT energies of all 2068 competing structures. GIIs based on BVM parameters determined using our framework correctly identified the DFT ground state perovskite structure in 135 of 188 compositions or one of the two lowest energy structures in 152 of 188 compositions. Using the most common approach to parameterize BVM, which minimizes the root-mean-square deviation of the BVM site discrepancy factors, GIIs correctly identified the DFT ground state perovskite structure in only 41 of 188 compositions. Our new parameterization framework is therefore a marked improvement over the existing procedure and an important first step toward BVM-based structure generation protocols that reproduce DFT.

Assessing the persistence of chalcogen bonds in solution with neural network potentials.

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on density functional theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what the most prevalent non-covalent interactions occurring in a solute-Cl--THF mixture are. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution.

Approximate bounds and temperature dependence of adiabatic connection integrands for the uniform electron gas.

Thermal density functional theory is commonly used in simulations of warm dense matter, a highly energetic phase characterized by substantial thermal effects and by correlated electrons demanding quantum mechanical treatment. Methods that account for temperature dependence, such as Mermin-Kohn-Sham finite-temperature density functional theory and free energy density functional theory, are now employed with more regularity and available in many standard code packages. However, approximations from zero-temperature density functional theory are still often used in temperature-dependent simulations using thermally weighted electronic densities as an input to exchange-correlation functional approximations, a practice known to miss temperature-dependent effects in the exchange-correlation free energy of these systems. In this work, the temperature-dependent adiabatic connection is demonstrated and analyzed using a well-known parameterization of the uniform electron gas free energy. Useful tools based on this formalism for analyzing and constraining approximations of the exchange-correlation at zero temperature are leveraged for the finite-temperature case. Inspired by the Lieb-Oxford inequality, which provides a lower bound for the ground-state exchange-correlation energy, bounds for the exchange-correlation at finite temperatures are approximated for various degrees of electronic correlation.

Fermi-Löwdin orbital self-interaction correction of adsorption energies on transition metal ions.

Density functional theory (DFT)-based descriptions of the adsorption of small molecules on transition metal ions are prone to self-interaction errors. Here, we show that such errors lead to a large over-estimation of adsorption energies of small molecules on Cu+, Zn+, Zn2+, and Mn+ in local spin density approximation (LSDA) and Perdew, Burke, Ernzerhof (PBE) generalized gradient approximation calculations compared to reference values computed using the coupled-cluster with single, doubles, and perturbative triple excitations method. These errors are significantly reduced by removing self-interaction using the Perdew-Zunger self-interaction correction (PZ-SIC) in the Fermi-Löwdin Orbital (FLO) SIC framework. In the case of FLO-PBE, typical errors are reduced to less than 0.1eV. Analysis of the results using DFT energies evaluated on self-interaction-corrected densities [DFT(@FLO)] indicates that the density-driven contributions to the FLO-DFT adsorption energy corrections are roughly the same size in DFT = LSDA and PBE, but the total corrections due to removing self-interaction are larger in LSDA.

Two-body weak currents in heavy nuclei

In light and medium-mass nuclei, two-body weak currents from chiral effective field theory account for a significant portion of the phenomenological quenching of Gamow-Teller transition matrix elements. Here we examine the systematic effects of two-body axial currents on Gamow-Teller strength and $\beta$-decay rates in heavy nuclei within energy-density functional theory. Using a Skyrme functional and the charge-changing finite amplitude method, we add the contributions of two-body currents to the usual one-body linear response in the Gamow-Teller channel, both exactly and though a density-matrix expansion. The two-body currents, as expected, usually quench both summed Gamow-Teller strength and decay rates, but by an amount that decreases as the neutron excess grows. In addition, they can enhance individual low-lying transitions, leading to decay rates that are quite different from those that an energy-independent quenching would produce, particularly in neutron-rich nuclei. We show that both these unexpected effects are related to changes in the total nucleon density as the number of neutrons increases.

Self-Immolative Hydroxybenzylamine Linkers for Traceless Protein Modification

Traceless self-immolative linkers are widely used for the reversible modification of proteins and peptides. This article describes a new class of traceless linkers based on ortho- or para-hydroxybenzylamines. The introduction of electron-donating substituents on the aromatic core stabilizes the quinone methide intermediate, thus providing a platform for payload release that can be modulated. To determine the extent to which the electronics affect the rate of release, we prepared a small library of hydroxybenzylamine linkers with varied electronics in the aromatic core, resulting in half-lives ranging from 20 to 144 h. Optimization of the linker design was carried out with mechanistic insights from density functional theory (DFT) and the in silico design of an intramolecular trapping agent through the use of DFT and intramolecular distortion energy calculations. This resulted in the development of a faster self-immolative linker with a half-life of 4.6 h. To demonstrate their effectiveness as traceless linkers for bioconjugation, reversible protein-polyethylene glycol conjugates with a model protein lysozyme were prepared, which had reduced protein activity but recovered ≥94% activity upon traceless release of the polymer. This new class of linkers with tunable release rates expands the traceless linkers toolbox for a variety of bioconjugation applications.

Crystal structure, Hirshfeld surface analysis, inter-action energy and DFT calculations and energy frameworks of methyl 6-chloro-1-methyl-2-oxo-1,2-di-hydro-quinoline-4-carboxyl-ate.

In the title compound, C12H10ClNO3, the di-hydro-quinoline moiety is not planar with a dihedral angle between the two ring planes of 1.61 (6)°. An intra-molecular C-H⋯O hydrogen bond helps to establish the rotational orientation of the carboxyl group. In the crystal, sheets of mol-ecules parallel to (10) are generated by C-H⋯O and C-H⋯Cl hydrogen bonds, and are stacked through slipped π-stacking inter-actions between inversion-related di-hydro-quinoline units. A Hirshfeld surface analysis of the crystal structure indicates that the most important contributions for the crystal packing are from H⋯H (34.2%), H⋯O/O⋯H (19.9%), H⋯Cl/Cl⋯H (12.8%), H⋯C/C⋯H (10.3%) and C⋯C (9.7%) inter-actions. Computational chemistry indicates that in the crystal, the C-H⋯Cl hydrogen-bond energy is -37.4 kJ mol-1, while the C-H⋯O hydrogen-bond energies are -45.4 and -29.2 kJ mol-1. An evaluation of the electrostatic, dispersion and total energy frameworks revealed that the stabilization is dominated via the dispersion energy contribution. Density functional theory (DFT) optimized structures at the B3LYP/6-311 G(d,p) level are compared with the experimentally determined mol-ecular structure in the solid state, and the HOMO-LUMO behaviour was elucidated to determine the energy gap.

Tension between predicting accurate ground state correlation energies and excitation energies from adiabatic approximations in TDDFT.

The connection between the adiabatic excitation energy of time-dependent density functional theory and the ground state correlation energy from the adiabatic connection fluctuation-dissipation theorem (ACFDT) is explored in the limiting case of one excited state. An exact expression is derived for any adiabatic Hartree-exchange-correlation kernel that connects the excitation energy and the potential contribution to correlation. The resulting formula is applied to the asymmetric Hubbard dimer, a system where this limit is exact. Results from a hierarchy of approximations to the kernel, including the random phase approximation (RPA) with and without exchange and the adiabatically exact (AE) approximation, are compared to the exact ones. At full coupling, the numerical results indicate a tension between predicting an accurate excitation energy and an accurate potential contribution to correlation. The AE approximation is capable of making accurate predictions of both quantities, but only in parts of the parameter space that classify as weakly correlated, while RPA tends to be unable to accurately predict these properties simultaneously anywhere. For a strongly correlated dimer, the AE approximation greatly overestimates the excitation energy yet continues to yield an accurate ground state correlation energy due to its accurate prediction of the adiabatic connection integrand. If similar trends hold for real systems, the development of correlation kernels will be important for applications of the ACFDT in systems with large potential contributions to correlation.

Anti-corrosion investigation of a new nitro veratraldehyde substituted imidazopyridine derivative Schiff base on mild steel surface in hydrochloric acid medium: Experimental, computational, surface morphological analysis

Intensive research has recently been directed toward synthesizing novel, non-toxic, and cost-effective organic inhibitors against metallic corrosion. In the present investigation, a non-toxic, novel Schiff base inhibitor, 6-bromo-(4,5-dimethoxy-2-nitrophenyl) methylidene] imidazo[1,2-a] pyridine-2-carbohydrazide (NVAIP) was synthesized and tested for its corrosion inhibition performance on Mild Steel (MS) in 1 M HCl at 303–323 K using potentiodynamic polarization study, electrochemical impedance spectroscopy (EIS) measurements, Scanning Electron Microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDX), Atomic Force Microscopy (AFM) and X-Ray Photoelectron Spectroscopy (XPS) analyses. The electrochemical results stated the inhibition effectiveness (ƞ%) of NVAIP was dependent on concentration and temperature, with the maximum efficiency (92.3%) recorded at 303 K for 500 ppm. The mixed-type inhibitory effect of NVAIP was substantiated by the polarization test results. The Langmuir adsorption isotherm model accorded with the metal surface evaluated, and Gibbs free energy of adsorption values ranged from - 35.05 to-36.05 kJ/mol, implying a physical and chemical adsorption mechanism. Surface morphological analysis was carried out to characterize the chemical composition of the adsorbed inhibitor on the MS surface, and these techniques confirmed that the inhibitive layer is composed of an iron oxide/hydroxide mixture where NVAIP molecules are incorporated. Further, the physicochemical and electronic properties of the NVAIP were investigated using Density Functional Theory (DFT) and electrostatic potential energy mapping (ESP). ΔEads value of −57.21 kcal/mol obtained from Molecular Dynamic (MD) simulations correlates well with the experimental results. Moreover, the relevance of the molecular structure of NVAIP and its inhibition act was validated by quantum chemical calculations and molecular dynamic (MD) simulation studies. A possible inhibition mechanism was proposed based on the experimental, theoretical, and surface analysis results. The outcomes of all the techniques show consistent agreement with each other.

TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.

Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy. Importantly, our method also provides an uncertainty estimate for the predicted profiles without any additional calculations. In this report, we show that TorsionNet can accurately identify the preferred dihedral geometries observed in crystal structures. Our TorsionNet-based analysis of a diverse set of protein-ligand complexes with measured binding affinity shows a strong association between high ligand strain and low potency. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. TorsionNet500, a benchmark data set comprising 500 chemically diverse fragments with DFT torsion profiles (12k MM- and DFT-optimized geometries and energies), has been created and is made publicly available.

Influence of supercell size on Gas-Surface Scattering: A case study of CO scattering from Au(1 1 1)

We examine energy dissipation of impinging molecules (CO) on Au(1 1 1) using molecular dynamics on a machine learned high-dimensional potential energy surface (PES) describing both the molecular and surface degrees of freedom. The PES was trained using a neural network method from density functional theory energies and gradients obtained with a relatively small supercell size, but it is capable of providing an accurate description of the molecule-surface interaction for larger supercells. This property allowed us to investigate the dependence of the dissipation dynamics on the supercell size. Our simulations indicated that the supercell size has essentially no effect on the direct scattered molecules, but the energy dissipation of the trapping molecules is significantly influenced by the size of the simulation cell. This observation has important implications in understanding dissipation at the gas-surface interface and their effects on various dynamics processes.

Tamoxifen charge transfer complexes with 2,3-dichloro-5,6-dicyano-1,4-benzoquinone and 7,7,8,8-tetracyanoquinodimethan: Synthesis, spectroscopic characterization and theoretical study

To understand bioactive molecule-receptor interactions it is important to understand the molecular complexation and structural recognition properties of the materials in question. To this aim, the electron donating bioactive molecule tamoxifen (TAM) was combined with the electron accepting molecules 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) and 7,7,8,8-tetracyanoquinodimethane (TCNQ) to form TAM–DDQ and TAM–TCNQ charge transfer (CT) complexes. The properties of the complexes in solution and solid, their donor–acceptor interactions were investigated, and their stability was assessed in acetonitrile. Solid complexes of TAM–DDQ and TAM–TCNQ were characterized using nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopies to confirm their formation. Job’s and modified Benesi–Hildebrand methods were used to study the stoichiometries and association constants of TAM–DDQ and TAM–TCNQ, from which their stoichiometries were found to be 1:1. The physical parameters of the CT complexes in terms of their molar extension constants, dipole moments, and formation constants were determined to study their stability in solution. The results obtained in this study indicate that the complexes are suitable for assessing TAM in pharmaceutical preparations. The experimental results were complemented by density functional theory (geometry optimization, energy transition, and molecular electrostatic potential maps) at DFT/B3LYPlevel of theory.

DFT studies of the redox behavior of oligo(aza)pyridines and experimental CVs of 4′-substituted terpyridines

The cyclic voltammograms of a series of substituted terpyridine ligands are presented, showing that reduction occur generally below −2 V versus the redox potential of ferrocene. Density functional theory (DFT) calculated energies and the theoretically calculated reduction potentials of a large series of substituted oligo(aza)pyridine ligands (terpyridine, bipyridine and phenanthroline) are determined and related to experimentally measured reduction potentials. A linear relationship is observed for the reduction potential of tpy ligands and the sum of the Hammett constant of the substituents on the ligands. It was found that the reduction potential of terpyridines relate linearly with various DFT calculated energies. It was further demonstrated that different oligo(aza)pyridines, namely phenanthrolines, bipyridines and the terpyridine ligands of this study, follow the same “reduction potentials – DFT energies” relationships.

Network metrics, structural dynamics and density functional theory calculations identified a novel Ursodeoxycholic Acid derivative against therapeutic target Parkin for Parkinson's disease

Parkinson's disease (PD) has been designated as one of the priority neurodegenerative disorders worldwide. Although diagnostic biomarkers have been identified, early onset detection and targeted therapy are still limited. An integrated systems and structural biology approach were adopted to identify therapeutic targets for PD. From a set of 49 PD associated genes, a densely connected interactome was constructed. Based on centrality indices, degree of interaction and functional enrichments, LRRK2, PARK2, PARK7, PINK1 and SNCA were identified as the hub-genes. PARK2 (Parkin) was finalized as a potent theranostic candidate marker due to its strong association (score > 0.99) with α-synuclein (SNCA), which directly regulates PD progression. Besides, modeling and validation of Parkin structure, an extensive virtual-screening revealed small (commercially available) inhibitors against Parkin. Molecule-258 (ZINC5022267) was selected as a potent candidate based on pharmacokinetic profiles, Density Functional Theory (DFT) energy calculations (ΔE = 6.93 eV) and high binding affinity (Binding energy = -6.57 ± 0.1 kcal/mol; Inhibition constant = 15.35 µM) against Parkin. Molecular dynamics simulation of protein-inhibitor complexes further strengthened the therapeutic propositions with stable trajectories (low structural fluctuations), hydrogen bonding patterns and interactive energies (>0kJ/mol). Our study encourages experimental validations of the novel drug candidate to prevent the auto-inhibition of Parkin mediated ubiquitination in PD.

NablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset.

Electronic wave function calculation is a fundamental task of computational quantum chemistry. Knowledge of the wave function parameters allows one to compute physical and chemical properties of molecules and materials. Unfortunately, it is infeasible to compute the wave functions analytically even for simple molecules. Classical quantum chemistry approaches such as the Hartree-Fock method or density functional theory (DFT) allow to compute an approximation of the wave function but are very computationally expensive. One way to lower the computational complexity is to use machine learning models that can provide sufficiently good approximations at a much lower computational cost. In this work we: (1) introduce a new curated large-scale dataset of electron structures of drug-like molecules, (2) establish a novel benchmark for the estimation of molecular properties in the multi-molecule setting, and (3) evaluate a wide range of methods with this benchmark. We show that the accuracy of recently developed machine learning models deteriorates significantly when switching from the single-molecule to the multi-molecule setting. We also show that these models lack generalization over different chemistry classes. In addition, we provide experimental evidence that larger datasets lead to better ML models in the field of quantum chemistry.

Extremely High Thermal Conductivity of Aligned Polyacetylene Predicted using First-Principles-Informed United-Atom Force Field

Molecular simulations of polymer rely on accurate force fields to describe the inter-atomic interactions. In this work, we use first-principles density functional theory (DFT) calculations to parameterize a united-atom force field for polyacetylene (PA), a conjugated polymer potentially of high thermal conductivity. Different electron correlation functionals in DFT have been tested. Bonding interactions for the alternating single and double bonds in the conjugated polymer backbone are explicitly described and Class II anharmonic functions are separately parameterized. Bond angle and dihedral interactions are also anharmonic and parameterized against DFT energy surfaces. The established force field is then used in molecular dynamics (MD) simulations to calculate the thermal conductivity of single PA chains and PA crystals with different simulation domain lengths. It is found that the thermal conductivity values of both PA single chains and crystals are very high and are length-dependent. At 790 nm, their respective thermal conductivity values are ~480 Wm-1K-1 and ~320 Wm-1K-1, which are comparatively higher than those of polyethylene (PE), the most thermally conductive polymer fiber measured up-to-date.