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We investigate the anisotropic thermal expansion behavior of a co- crystalline system composed of 4,40-azopyridine and trimesic acid (TMA-azo). Using variable-temperature single-crystal X-ray diffrac- tion (SC-XRD), low-frequency Raman spectroscopy, and terahertz time-domain spectroscopy (THz-TDS), we observe significant temperature-induced shifting and broadening of the vibrational absorption features, indicating changes in the intermolecular potential. Our findings reveal that thermal expansion is driven by anharmonic interactions and the potential energy topography, rather than increased molecular dynamics. Density functional the- ory (DFT) simulations support these results, highlighting significant softening of the potential energy surface (PES) with temperature. This comprehensive approach offers valuable insights into the relationship between structural dynamics and thermal properties, providing a robust framework for designing materials with tailored thermal expansion characteristics. 

The D³TaLES database and data infrastructure aim to offer readily accessible and uniform data of varying types for redox-active organic molecules targeting non-aqueous redox flow batteries. 

Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development. Here, we present a curated dataset containing 25k molecules with density functional theory (DFT) and time-dependent DFT (TDDFT) evaluated properties that include frontier molecular orbitals, ionization energies, relaxation energies, and low-lying optical excitation energies. Using the dataset, we train a hierarchy of ML models, ranging from classical models such as ridge regression to sophisticated graph neural networks, with molecular SMILES representation as input. We observe that graph neural networks augmented with contextual information allow for significantly better predictions across a wide array of properties. Our best-performing models also provide an uncertainty quantification for the predictions. To democratize access to the data and trained models, an interactive web platform has been developed and deployed.