Beyond Black-Box Models: A Physics-Driven Computational Paradigm for Low-Temperature Flexible Organic Crystals.
Low-temperature flexible organic crystals represent a critical frontier in extreme-environment materials science, exhibiting remarkable macroscopic elastoplasticity and dynamic structural adaptability under deep cryogenic conditions. However, the thermodynamic suppression of thermal activation at these temperatures typically induces severe ductile-to-brittle transitions, rendering the underlying microscopic mechanisms of stress dissipation largely unresolved. While machine learning has emerged as an essential computational bridge to resolve the accuracy-scale dilemma inherent in traditional simulations, its application in deep cryogenic regimes remains fundamentally restricted. This Perspective critically examines the state-of-the-art computational methodologies driving the field, highlighting the necessary transition from empirical black-box algorithms to physics-informed sequential reasoning frameworks. We dissect the persistent algorithmic challenges that hinder current models, specifically the extreme scarcity of cryogenic mechanical data, the prevalence of unpredictable structural property cliffs, and the systemic out-of-distribution generalization failures caused by temperature-biased training data sets. To surmount these barriers, we outline a future research trajectory focused on advanced machine learning force fields, Δ-learning strategies, and multimodal generative artificial intelligence. Ultimately, we advocate for the establishment of a rigorous, closed-loop computation-experiment ecosystem to enable the autonomous inverse design of next-generation resilient organic materials.