Calculations of the electron-impact ionization cross-section of pyrene, anthracene, benzoyl chloride, benzophenone, and phthalonitrile are reported over a wide energy range. A comparison of theoretical models, viz. spherical complex optical potential (SCOP), pixel counting method (PCM), and the binary encounter Bethe (BEB) model is carried out. SCOP calculations provide the inelastic cross-section, and the ionization cross-section is extracted from it. This result is modified with PCM, a model previously applied to ion collisions which takes geometric screening corrections into account. The BEB model is used as an independent approach to calculate the ionization cross-section. It is demonstrated that all model results are in reasonable agreement with each other. Comparison is also made with other theoretical data where available.
Melting-like transitions in clusters are normally identified by a peak in the heat capacity curve C(T ) at T = Tc. Computing C(T ) requires costly simulations with millions of steps. We discuss four easily calculated functions of temperature that help detect and characterize melting-like transitions. The first, f1 (or WU ), is the width of the potential energy distribution which shows an abrupt increase near Tc. The other three are statistics of the ordered set of N(N − 1)/2
interatomic distances rij: (i) f2 is a measure of dissimilarity to the lowest energy configuration, or global minimum; (ii) f3 is the effective number of rij’s found in a small interval centered around (r1 + r2)/2 where r1, r2 are the positions of the first two peaks in the pair distribution function; and (iii) f4 is a measure of non-uniformity in the distribution of the
ri j’s. Numerical tests with empirical potentials that model three types of bonding (van der Waals, covalent, and metallic) show that f1, f2, f3, and f4 produce estimates for the middle of the melting region in general agreement with Tc. An Artificial Neural Network (ANN) classifier that takes, as inputs, f2, f3, and many variants of f4, is used to calculate the solid fraction FS(T) and find the solid-liquid coexistence region between freezing and melting temperatures, [Tf , Tm]. Inflection points in f3(T ) and FS(T ) are very sensitive indicators of phase transitions. Estimates of Tc obtained from them converge one to three orders of magnitude faster, in simulation time, than those obtained with C(T ).
Melting Transitions in Small Aluminum Clusters Simulated with Energies Approaching DFT Accuracy
(2025)
We describe a computational framework for modelling melting-like transitions in atomic clusters that combines first-principles energy calculations, global optimization, and machine-learned interatomic potentials. A diverse set of configurations is generated by global optimization, and energies are calculated by Density Functional Theory. The energies are fitted to an accuracy of 10 meV/atom or better with an Allegro E(3)-equivariant neural network potential. The model allows efficient parallel tempering Monte Carlo simulations with near DFT-level accuracy. This methodology was validated by simulating Na_20 and comparing it to earlier experimental and computational results. We used it to study melting-like transitions in Al_n+ clusters (n=9 to 16), and Al_n and Al_n^- (n=12, 13,14). The simulated heat capacity of these clusters, in particular Al_16+, are in qualitative agreement with experiments.
The melting point of Al_n+ clusters with n=11-16 are well above the bulk melting point (934 K). The closed-shell Al_13- species has an exceptionally high melting point, close to 2100 K.
Spectral Quantum Chemistry and Infrared Resonance Library for Data-Driven Molecular Spectroscopy
(2026)
Infrared (IR) spectroscopy is a fundamental tool for molecular identification and characterization, yet comprehensive IR spectral databases remain limited, particularly for small organic molecules with well-defined theoretical baselines. Here we introduce SQuIRL, the Spectral Quantum Chemistry and Infrared Resonance Library, a collection of computed IR spectra for 133,885 organic molecules. Each entry provides vibrational frequencies and intensities with near-benchmark accuracy, thereby extending the QM9 dataset by incorporating vibrational fingerprints alongside its structural and electronic descriptors. The resulting dataset enables data-driven spectrum prediction, machine-learning model training, and automated molecular identification. SQuIRL establishes a new foundation for high-fidelity, quantum-accurate infrared spectroscopy in computational chemistry. Distributed in structured HDF5 format with an accessible API, it integrates seamlessly into AI-based spectroscopy workflows and molecular discovery pipelines, offering a widely accessible resource for data-driven approaches in vibrational spectroscopy.
Superatoms are clusters with physical or chemical properties that bear certain similarities to atoms. We calculated equilibrium geometries and properties using spin-polarized density functional theory (DFT) within the generalized gradient approximation (PBE). We also performed Ab Initio Molecular Dynamics (AIMD) simulations in the NVT ensemble with a Nosé-Hoover thermostat. In addition, Monte Carlo simulations with a potential fitted to DFT were carried out to assess a number of clusters as possible superatoms. The clusters we studied have a chemical composition AB2C10, where A, B, and C are metals, and a structure derived from that of a 13-atom icosahedron. We adopt an operational definition of superatom to assess clusters. According to this definition, superatoms should have (i) a quasispherical equilibrium geometry and (ii) a set of valence orbitals delocalized over the cluster; (iii) they should maintain these two attributes at elevated temperatures (mechanical and thermal stability); and (iv) they should be thermodynamically favored relative to similar clusters. By these criteria, Sn3Y10, CrSn2Zr10, and possibly CrSn2Ti10 can be classified as “superatoms”. We show that cluster stability arises from (i) the intrinsic stability of the C10 fragment; (ii) its interaction energy with the three “dopant atoms”; and (iii) compatible atomic sizes of the three elements. Clusters such as CrSn2Ti10 and CrSn2Zr10 exhibit an optimal balance among these contributions. They have a high effective coordination, an absence of very low vibrational frequencies, a favorable ratio of their elements’ cohesive energy, and exceptional thermal resilience, with melting temperatures that are well above the weighted average of their elements’ bulk melting points. Electronic analysis shows that CrSn2Ti10 and CrSn2Zr10 display discrete density-of-states features and partial electron delocalization, consistent with superatomic behavior. These electronic properties vanish as the clusters approach the melting point and undergo structural distortion. We show the importance of looking at several, often interrelated, properties in assessing possible superatoms.
