The advent of computational techniques, particularly atomistic simulations, has lessened the dependency on physical experiments in various scientific fields. Yet, the preparation complexity for simulations using platforms like LAMMPS and GROMACS persists. We introduce SMI2PDB, a Python tool that automates molecular systems assembly from SMILES to PDB format, easing molecular dynamics simulation setups. SMI2PDB manages molecule configuration and quantification effortlessly, establishes stable conformers, applies random rotations, and positions them in a simulation box with a Sobol sequence to reduce overlaps. This script facilitates the rapid preparation of complex organic mixtures for use in simulations, enhancing the exploration of novel materials.@en

Atomistic simulations are crucial for understanding material properties at the molecular level but are limited by high computational costs, especially for large, complex systems like bituminous materials. Our team developed a Force-matched United Atom (UA) Coarse Graining (CG) force field to enhance computational efficiency while retaining atomic detail. However, converting all-atom models to CG models is complex, requiring detailed atom-to-bead mapping and compatibility with molecular dynamics (MD) engines like LAMMPS. To address this, we introduce AA2UA, an open-source software that simplifies the conversion of PDB files into LAMMPS-readable structure topology files, facilitating broader use of the developed UA force field.@en

This study enhances the molecular analysis of bitumen by transitioning from traditional chemical descriptors, such as SARA (Saturates, Aromatics, Resins, and Asphaltenes) fractions and elemental compositions, to specific force field atom types in Molecular Dynamics (MD) models. This shift improves the precision in predicting material properties critical for bituminous material characterization. Machine Learning Models (MLMs) were developed to use these atom types as input features, inherently reflecting fundamental chemical characteristics. Trained on data from over 1,770 LAMMPS simulations of diverse bitumen types and conditions, these MLMs enable the prediction of properties like density, heat capacity, solubility parameters, and thermal expansion coefficients without the need for additional MD simulations. The models utilize 30 chemical descriptors corresponding to specific atom types in the PCFF force field, which collectively account for over 95% of the influence on these properties. By accurately predicting fundamental, thermodynamic, and kinetic properties, the use of MLMs and force field atom types allows researchers to efficiently tweak the chemical nature of organic molecules and mixtures to achieve desired properties. With near-instantaneous prediction times, these MLMs offer valuable insights for advancing bitumen research in the construction and petroleum industries, reducing the need for more intensive simulation techniques.@en

Pdb2dat, developed in Python, is an open-source, self-contained utility that facilitates the conversion of PDB files into LAMMPS data files, catering to the need of initializing atomistic simulation from initial atomic configurations. It extracts molecular details from PDB files, uses Rdkit and Xyz2mol for bonding analysis and 3D conformer generation, and uses Pysimm for assigning force field types and charges. Designed to be lightweight and fully Pythonic, pdb2dat is suitable for use in privilege-limited high-throughput environments. The output details system topologies for use in MD simulations, significantly simplifying the preparatory steps needed by researchers to explore materials phenomena through LAMMPS.@en

This paper presents a United Atom (UA) force field for simulating hydrocarbon molecules in bituminous materials, integrating explicit hydrogens into beads with their parent atom. This method simplifies all-atom molecular models, significantly accelerating Molecular Dynamics (MD) simulations of bitumen by 10 to 100 times. Key advantages include halving the particle count, eliminating complex hydrogen interactions, and decreasing the degrees of freedom of the molecules. Developed by mapping forces from an all-atom model to the centers of mass of UA model beads, the force field ensures accurate replication of energies, forces, and molecular conformations, mirroring properties like pressure and density. It features 17 bead types and 287 interaction types, encompassing various hydrocarbon molecules. The UA force field's stability, surpassing all-atom models, is a notable achievement. This stability, stemming from smoother potential energy surfaces, leads to consistent property measurements and improved stress tensor accuracy. It enables the extension of MD simulations to larger spatiotemporal scales, crucial for understanding complex phenomena such as phase separation in bituminous materials. This foundational work sets the stage for future developments, including refining parameters and introducing new bead types, to enhance the modeling capabilities of the force field, thereby advancing the application and understanding of bituminous materials.

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Bitumen fatigue resistance is critical to determine the overall fatigue performance and service life of asphalt pavements. However, the mechanisms responsible for fatigue damage of bitumen have previously not been well understood. Molecular dynamics (MD) simulation has recently emerged as a powerful computer-aided numerical technique to model the microscopic failure behaviours in materials. This study aims to use the MD method to investigate the molecular origin of bitumen fatigue damage. The molecular models of the virgin and aged PEN70/100 bitumen were firstly constructed based on their saturate, aromatic, resin and asphaltene (SARA) four fractions. An MD equilibrium was run on the developed bitumen models with the assigned interatomic potentials. Following an MD-based tensile simulation, a strain-controlled fatigue simulation was performed to study the nanostructure and damage behaviours of the virgin and aged bitumen under fatigue loading by calculating the stress-strain response, potential energy, molecular structure and nanovoid volumes. Furthermore, a rheometer measurement was also conducted to characterise the fatigue damage of the bitumen directly by a crack length at the macroscale. Results indicate that the bitumen molecules become unfolded and tend to align along the loading direction when fatigue loading was applied. The change in the molecular configuration helped the molecular chains move closer together and thus contributed to the reduction of the intermolecular interactions including the van der Waals and Coulombic energies. With the increasing load cycles, nanovoids were formed and grew in the bitumen through molecular rearrangement and movement, leading to microscopic fatigue damage of the bitumen. It was found that the aged bitumen produced more severe fatigue damage than the virgin bitumen, which was indicated by the MD-based nanovoid volume at the molecular scale and the DSR-based crack length at the macroscale. The findings from MD simulation provide a fundamental understanding of the molecular origin of fatigue damage, that cannot be experimentally detected for bitumen materials.@en

This study explores the use of chemical descriptors derived from force field atom types to predict Fickian diffusion coefficients of rejuvenators in bitumen, utilizing machine learning models trained on data from 240 non-equilibrium molecular dynamics simulations. The simulations cover three bitumen types (NO, TO, FO), five aging degrees, and four temperatures (60 °C, 120 °C, 160 °C, 200 °C), capturing diffusion coefficients ranging from 0.0068e-10 m2/s in highly aged bitumens at 60 °C to 4.35e-10 m2/s in fresher samples at 200 °C. The MLM, built with 18 chemical descriptors for bitumen and rejuvenator sides, achieves an R2 of 0.97, accurately predicting diffusion across varied conditions. This approach abstracts away from the need for repeated MD simulations, enabling diffusion predictions even for systems outside the original dataset. The manuscript presents three case studies to illustrate how the model can be used for the iterative design of rejuvenators by optimizing molecular structures based on critical chemical features, such as rejuvenator oxygen content, bitumen sulfur content, and molecular weights. It also demonstrates how the model offers a practical framework for understanding the diffusion and performance of rejuvenators by linking time-dependent factors—such as concentration, depth, and rejuvenation time—with the bulk properties of bitumen-rejuvenator systems, facilitating industrial applications.@en

Conventional Molecular Dynamics (MD) models of bitumen are built by homogeneously mixing molecules in a volume without considering that the molecules in bitumen are known to exhibit phase behavior and form distinctive molecular arrangements. These are known to have a significant impact in the behavior of bitumen, and considering their existence is paramount in producing improved representations of bitumen using computational models. This study explores whether MD models of bitumen that are conventionally assumed to be in equilibrium can still undergo significant phase separation over considerably long simulation times. It also aims to establish a more formal pathway to build and study models with highly heterogeneous arrangements of their molecules. Moreover, it aims to evaluate whether the presence of distinct morphologies have a significant impact in numerous physical properties of bitumen. The study shows that conventional and widely used models of bitumen exhibit significant molecular rearrangements over long times (>360 ns). It also shows that building heterogeneous morphologies is possible and result in energetically favorable conformations. Moreover, it proves that studying properties regularly used to validate MD models of bitumen (e.g., density) are insufficient in assessing the impact of different morphologies; more thorough methods are required to evaluate them.

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