大学生创新创业训练计划智能管理平台
第二十九期上海交通大学大学生创新实践计划
A Data Driven Approach for Perovskites Materials Design
创新训练项目
工学
材料类
创新类
密西根学院
NILADRI SAHA SACCHA
学生
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The
proposed advisor for this project currently conducts research projects funded by governmental agencies such
as NSFC, MOST, and STCSM, and companies such as CATL, GCL, and Huawei.
The
proposed advisor will provide all necessary hardware and software to me, and
will arrange regular meetings to ensure the progress of the project.
The project aims to employ a data-driven approach for the design of
perovskite materials, focusing on both hybrid and halide perovskites.
Perovskite materials, particularly methylammonium lead halide (MAPI), have
garnered significant attention due to their remarkable optoelectronic
properties, making them promising candidates for various applications such as solar
cells. This research initiative seeks to leverage computational methodologies,
machine learning algorithms, and high-throughput computations to expedite the
exploration of perovskite chemical spaces. By integrating insights from
molecular dynamics simulations, density functional theory (DFT), and machine
learning techniques, the project aims to facilitate the discovery of novel
perovskite compositions with tailored properties for enhanced device
performance.