Dandan Song（宋丹丹）

宋丹丹 Dandan Song

北京交通大学，教授

Beijing Jiaotong University，Professor

人工智能赋能材料研发（AI4M）的方法在多个领域被证明是高效的材料研发模式，也是UDC等OLED材料巨头开展材料研发的模式；然而，AI4M在开发OLED材料时还面临一系列关键科学问题和技术瓶颈需要解决。宋丹丹的主要研究领域为AI4M在光电材料和器件中的应用，聚焦瓶颈问题开展创新研究，建立了其在面向不同领域应用时的分子结构——性能量化模型（包括与发光效率、色度、材料稳定性相关的模型），应用深度学习方法开展OLED材料的正向及逆向设计，并开发了有机光电材料性能预测平台，加速有机光电功能材料的高通量筛选和器件优化。在Advanced Materials、Journal of Energy Chemistry等期刊上以第一/通讯作者身份发表SCI检索研究论文60余篇。

The approach of Artificial Intelligence for Materials Research and Development (AI4M) has been proven to be a highly efficient materials R&D paradigm across multiple fields, and it is also the model adopted by OLED materials giants such as UDC for their materials R&D. However, AI4M still faces a series of critical scientific issues and technical bottlenecks that need to be addressed when applied to OLED materials development.

Dandan Song’s primary research focuses on the application of AI4M in optoelectronic materials and devices. She conducts innovative research targeting the aforementioned bottlenecks, and has established quantitative molecular structure-property models tailored for diverse application scenarios, including models related to luminous efficiency, chromaticity, and material stability. She has applied deep learning methods to carry out forward and inverse design of OLED materials, and developed a performance prediction platform for organic optoelectronic materials, which accelerates the high-throughput screening of organic optoelectronic functional materials and the optimization of related devices. She has published more than 60 SCI-indexed research papers as first or corresponding author in journals such as Advanced Materials and Journal of Energy Chemistry.