Maliheh Shaban Tameh

The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800-1250 cm^-1/8-12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely on costly inorganic materials, limiting their broader deployment. Here, we present a machine learning (ML)-driven framework for identifying highly LWIR-transparent hydrocarbons as candidate monomers for sulfur-organic hybrid polymers. Infrared (IR) spectra are predicted by using a communicative message-passing neural network (CMPNN), and the effect of spectral broadening (γ = 4-10) is systematically analyzed to guide flexible, use-driven γ selection. LWIR window transparency (wT) is estimated from spectra and directly predicted using regression models trained on CMPNN-derived fingerprints. These interpretable models enable the extraction of structure-property relationships, revealing chemically meaningful descriptors and substructural motifs linked to high transparency and supporting the formulation of design rules for IR-transparent materials. The approach demonstrates strong predictive accuracy and generalizability across training, test, and external data sets. Notably, several ML-prioritized motifs match experimentally validated comonomers used in IR polymer fabrication, and the model further proposes new, chemically plausible scaffolds. By bypassing the cost of density functional theory (DFT), the framework enables scalable, data-driven screening and provides chemically grounded design insights for next-generation LWIR-transparent materials.

A detailed understanding of the composition and polymerization mechanism of elemental sulfur remains a decades long unresolved question for modern chemistry. However, the dynamic nature of molten sulfur significantly complicates its accurate characterization. To overcome this challenge, we performed the first comprehensive molecular dynamics (MD) simulations using a ReaxFF reactive force field specifically parameterized to capture the complex ring‐opening polymerization dynamics of elemental sulfur. Rigorous development of the force field parameters, trained against extensive quantum mechanical datasets, was key to enabling these large‐scale (>10 000 atoms) reactive MD simulations at polymerization‐relevant temperatures. This study provides the first detailed atomic‐level description of liquid sulfur, elucidating temperature‐dependent molecular composition and offering unprecedented insights into sulfur polymerization mechanisms. These are the first simulations to reveal the formation of remarkably large macrocyclic sulfur rings at the onset of polymerization—a discovery that challenges longstanding mechanistic misconceptions, thus reshaping our understanding of sulfur polymerization. Our findings highlight the power of molecular dynamics in exploring complex polymerization processes, with broad impact in dynamic covalent chemistry and covalent adaptable polymers.

A detailed understanding of the composition and polymerization mechanism of elemental sulfur remains a decades long unresolved question for modern chemistry. However, the dynamic nature of molten sulfur significantly complicates its accurate characterization. To overcome this challenge, we performed the first comprehensive molecular dynamics (MD) simulations using a ReaxFF reactive force field specifically parameterized to capture the complex ring‐opening polymerization dynamics of elemental sulfur. Rigorous development of the force field parameters, trained against extensive quantum mechanical datasets, was key to enabling these large‐scale (>10 000 atoms) reactive MD simulations at polymerization‐relevant temperatures. This study provides the first detailed atomic‐level description of liquid sulfur, elucidating temperature‐dependent molecular composition and offering unprecedented insights into sulfur polymerization mechanisms. These are the first simulations to reveal the formation of remarkably large macrocyclic sulfur rings at the onset of polymerization—a discovery that challenges longstanding mechanistic misconceptions, thus reshaping our understanding of sulfur polymerization. Our findings highlight the power of molecular dynamics in exploring complex polymerization processes, with broad impact in dynamic covalent chemistry and covalent adaptable polymers.

Chalcogenide Hybrid Inorganic/Organic Polymers (CHIPs) have the potential to revolutionize infrared (IR) optics and create sustainable and recyclable devices. CHIPs combine elemental sulfur with organic comonomers via inverse vulcanization to create a high-sulfur content polymer, with optical properties that rival state-of-the art inorganic solids with the processability and recyclability of plastic materials. However, the optimal comonomer for these applications remains unknown. This manuscript presents a gradient boosted tree model that determines which comonomers merit further consideration as high-performing CHIPs materials. After training models on previously calculated IR-absorption data we apply them to a larger set 960,966 molecules from the GDB dataset and validate the predictions for both highly transparent molecules and a set of one thousand randomly selected molecules. We then look at the 199,511 molecule subset of the expanded search space with chemical moieties eligible for inverse vulcanization and found 2,942 possible comonomers predicted to have better optical properties than the state-of-the-art comonomer Stillene. Finally, we calculate optical properties of all 2,942 comonomers in the gas phase and in a configuration to approximate the polymer films to find a new set of target comonomers.

The ability of SnO₂ surfaces to adsorb and activate oxygen species is essential for designing high‐performance gas sensors and catalytic materials. In this study, density functional theory (DFT) calculations are employed to unravel the mechanisms governing oxygen adsorption on SnO₂ (110) surfaces under varying surface conditions, including reduced, defective, and stoichiometric configurations. Our findings indicate various forms of charged oxygen species on the surface. The study reveals the presence of , , and on the reduced surface and , , and on the defective and the oxidized surfaces. These charged species are directly linked to the reactivity and sensitivity of SnO₂‐based materials, as they interact with oxygen vacancies to stabilize adsorption configurations and influence electronic states within the band gap. PDOS analyses highlight the electronic interactions between adsorbed oxygen species and surface tin atoms, revealing the formation of hybridized orbitals that contribute to defect states near the Fermi level. These states play an important role in determining the surface reactivity and electronic properties of the material. The interplay between oxygen adsorption and vacancy‐induced charge redistribution provides critical insights into the mechanisms driving surface reactivity and performance. By providing a detailed understanding of the electronic and energetic properties of adsorbed oxygen species, this work establishes a theoretical framework for the design and optimization of metal oxide‐based materials. The findings are particularly valuable for tailoring the properties of SnO₂, In₂O3, and ZnO for applications in gas sensing, catalysis, and related technologies.

Accurately and efficiently predicting the infrared (IR) spectra of a molecule can provide insights into the structure-properties relationships of molecular species, which has led to a proliferation of machine learning tools designed for this purpose. However, earlier studies have focused primarily on obtaining normalized IR spectra, which limits their potential for a comprehensive analysis of molecular behavior in the IR range. For instance, to fully understand and predict the optical properties, such as the transparency characteristics, it is necessary to predict the molar absorptivity IR spectra instead. Here, we propose a graph-based communicative message passing neural network algorithm that can predict both the peak positions and absolute intensities corresponding to density functional theory calculated molar absorptivities in the IR domain. By modifying existing spectral loss functions, we show that our method is able to predict with DFT-accuracy level the IR molar absorptivities of a series of hydrocarbons containing up to ten carbon atoms and apply the model to a set of larger molecules. We also compare the predicted spectra with those generated by the direct message passing neural network. The results suggest that both algorithms demonstrate similar predictive capabilities for hydrocarbons, indicating that either model could be effectively used in future research on spectral prediction for such systems.

We study the gap energy of the semiconducting oxide SnO2 through ab initio calculations including both density functional theory (DFT) and coupled cluster methods. The effectiveness of twist averaging in reducing finite-size errors is evaluated across different functionals. We report an overestimation of gap energy when applying finite-size scaling to reach the thermodynamic limit in equation-of-motion (EOM) CCSD calculations. To mitigate one-body and many-body errors, we integrate twist averaging with a post-processing correction mechanism that compares finite-size and infinite-size DFT calculations using hybrid functionals. While inspired by the Kwee, Zhang, and Krakauer approach, our method is specifically tailored to hybrid functionals for a more accurate treatment of exchange-correlation effects. Our approach ensures that the many-body interactions are accurately captured in the estimated gap for an infinite system. We introduce unique single twist angles that provide cost-effective and accurate energies compared to to full twist averaging in EOM-CCSD calculations. Applying this approach to SnO2, we calculate a fundamental gap of 3.46 eV, which closely matches the 3.59 eV gap obtained from two-photon spectroscopy experiments, demonstrating the accuracy of this method.

We study the gap energy of the semiconducting oxide SnO2 through ab-initio calculations including both DFT and coupled cluster methods. The effectiveness of twist-averaging in reducing finite-size errors is evaluated across different functionals. We report an overestimation of gap energy when applying finite-size scaling at the thermodynamic limit in equation-of-motion (EOM) CCSD calculations. To mitigate one-body and many-body errors, we integrate twist averaging with a post-processing correction mechanism, comparing finite-size and infinite-size DFT calculations using hybrid functionals. While inspired by the Kwee, Zhang, and Krakauer (KZK) approach, our method is tailored to hybrid functionals for a more accurate treatment of exchange-correlation effects. Our approach ensures that the many-body interactions are accurately reflected in the estimated gap for an infinite system. We introduce unique single twist angles that yield cost- effective and accurate energies, in comparison to full twist averaging in the EOM-CCSD calculations. Applying this approach to SnO2, we calculate a fundamental gap of 3.46 eV, closely matching the 3.59 eV gap obtained from two-photon spectroscopy experiments, thereby demonstrating the accuracy of this method.