Eli I. Assaf

Copper indium diselenide (CuInSe2) is a prototype ternary compound and group I–III–VI semiconductor with useful optoelectronic properties. CuInSe2 nanocrystals have been of significant interest because of their size-tunable optical properties and lack of toxic heavy metals. Because of the particular vacancy and antisite substitutional point defects in CuInSe2, large stoichiometric deviations can be tolerated, sometimes leading to the so-called ordered vacancy compounds (OVCs). Here, we use Raman spectroscopy of oleylamine-capped CuInSe2 nanocrystals and ab initio lattice dynamics modeling to study the concentration and arrangements of (2vCu– + InCu2+) defect pairs in the nanocrystals. The nanocrystals have randomly distributed defect pairs that become mobile under light excitation and accumulate, as in OVCs, along the [100] direction. Because the high concentration of vacancies in CuInSe2 nanocrystals is compensated by InCu2+ antisite defects, these nanocrystals do not exhibit an optical plasmon resonance like many other copper chalcogenide nanocrystals. Annealing the nanocrystals at a high temperature (600 °C) was found to significantly reduce the defect concentration.

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. • The molecular origin of bitumen fatigue damage is revealed by a novel MD-based model. • The MD-based model can be applied to characterise bitumen's viscoelasticity and nanostructure. • Bitumen molecules become unfolded and aligned during initial fatigue loading. • Nanovoid formation and growth are quantified for the bitumen under fatigue loading.

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.

• Force field atom types accurately characterize chemical impact on bitumen properties, surpassing conventional methods. • Machine Learning Models (MLMs) predict bitumen properties, reducing the need for extensive molecular simulations. • Examined 30 chemical descriptors: 10 key features influence over 95% of bitumen material properties. • Near instantaneous property estimation allows for rapid evaluation of bitumen quality and performance. 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.

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. • Pdb2dat converts PDB files into LAMMPS data files, preparing molecular models for use in atomistic simulations. • It integrates Rdkit and XYZ2MOL to generate molecular conformers, and Pysimm for assigning force field types and charges. • Self-contained and lightweight, requiring only standard Python libraries, making it well-suited for HPC environments. • Supports multiple force fields including CHARMM, Gaff, Gaff2, Pcff, and Tip3p, catering to diverse simulation needs. • Automatically parses atomic configurations into LAMMPS, enabling the rapid preparation of multiple MD systems effortlessly.