From ba4948746b08c5661f4125235429d6f46990fdb3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=9C=E5=A5=95=E6=B1=90?= <103583354+KashaDong@users.noreply.github.com> Date: Wed, 26 Nov 2025 16:18:11 +0800 Subject: [PATCH 1/2] Revise title and add post metadata for Uni-Mol Updated the title and added metadata for the post. --- source/_posts/Uni-Mol_27_10_2025.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/source/_posts/Uni-Mol_27_10_2025.md b/source/_posts/Uni-Mol_27_10_2025.md index f58aa48..9477e92 100644 --- a/source/_posts/Uni-Mol_27_10_2025.md +++ b/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. From d4ae9c13df40c2454666a4a7e0d58c629a8f2346 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=9C=E5=A5=95=E6=B1=90?= <103583354+KashaDong@users.noreply.github.com> Date: Wed, 26 Nov 2025 16:22:31 +0800 Subject: [PATCH 2/2] Update Uni-Mol post with OCNet framework details Added details about the OCNet framework and its achievements in the context of organic optoelectronic materials. --- source/_posts/Uni-Mol_27_10_2025.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/_posts/Uni-Mol_27_10_2025.md b/source/_posts/Uni-Mol_27_10_2025.md index 9477e92..d7a640d 100644 --- a/source/_posts/Uni-Mol_27_10_2025.md +++ b/source/_posts/Uni-Mol_27_10_2025.md @@ -13,7 +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). ---- + # Methodological Highlights