Revise title and add post metadata for Uni-Mol (#332)

* Revise title and add post metadata for Uni-Mol

Updated the title and added metadata for the post.

* Update Uni-Mol post with OCNet framework details

Added details about the OCNet framework and its achievements in the context of organic optoelectronic materials.

Author: KashaDong

source/_posts/Uni-Mol_27_10_2025.md

@@ -1,4 +1,9 @@
-# Uni-Mol Can Do This Too: Full-Scale AI Design of Optoelectronic Materials from Molecules to Devices
+---
+title: "What Can Uni-Mol Do too? | Full-Scale AI Design of Optoelectronic Materials from Molecules to Devices"
+date: 2025-10-27
+categories: 
+- Uni-Mol
+---
 
 From the vivid colors of smartphone displays and the high efficiency of photovoltaic solar panels, to high–energy-density batteries and sharp bio-fluorescent imaging, organic optoelectronic molecules are indispensable. They serve as the “soul” and “modulator” of optoelectronic functions. With structural tunability at the molecular scale, they continuously enable the evolution of optoelectronic devices and their broad application scenarios.
 
@@ -8,7 +13,7 @@ Recently, the Functional Molecular Design Team of AI for Science Institute (AISI
 
 OCNet achieves, for the first time, a **unified virtual representation spanning molecules, mesoscale materials, and devices**: it surpasses existing SOTA models by **20%** on molecular-scale performance, enables **cross-material generalizable** mobility prediction in amorphous organic thin films for the first time, and delivers **near-real-time, high-accuracy** prediction of device-level photovoltaic efficiency. The work has been published in *npj Computational Materials* (doi: 10.1038/s41524-025-01788-y).
 
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 # Methodological Highlights