Integrating Density Functional Theory, Finite Element Analysis & Machine Learning in the study of mechano-thermal properties of a Triclinic Pentafluorophenyl-Urea Derivative

The mechanical behavior of organic molecular crystals is a critical factor in their applicability for structural and functional materials. In this study, we present a first-principles computational framework for predicting the thermo-mechanical properties of PDPF (C ₁₉ ​H ₇ ​F ₁₀ ​N ₅ ​O ₂ ​), integrating Density Functional Theory (DFT), Finite Element Analysis (FEA) for elasticity simulations, and Machine Learning (ML) regression models. A major challenge in using commercial Finite Element Analysis (FEA) software for organic molecular crystals is the lack of direct compatibility with Crystallographic Information Files (CIF). Standard FEA tools rely on predefined material databases and user-defined stress-strain models, making it difficult—or even impossible—to directly simulate a molecular crystal structure from its CIF file. To address this, we developed a CIF → DFT → Python-based FEA workflow, where DFT-derived elastic stiffness tensors (C _ij ​) were used to define the material properties in a custom Python elasticity solver. This method eliminates the need for meshing and provides a self-consistent approach for simulating stress-strain behavior under uniaxial, shear, biaxial, triaxial, and hydrostatic loading conditions. The results reveal that PDPF exhibits high tensile strength (8.05 GPa) with brittle fracture, enhanced failure resistance in biaxial stress (9.57 GPa), and bulk collapse at -18.83 GPa under extreme compression. Furthermore, we implement Basquin’s Law for fatigue modeling and Norton’s Law for creep prediction, showing that PDPF maintains high endurance under cyclic stress but exhibits gradual softening under long-term deformation. This study provides a computationally self-consistent framework for predicting mechanical behavior in molecular crystals, while also identifying the limitations of molecular descriptors in mechanical modeling. Future work will focus on incorporating molecular dynamics (MD) simulations, defect modeling, and advanced ML architectures to improve predictive accuracy.