![[Case Study] Accelerating the Design of Organic EL Materials through Active Learning](https://image.mono.ipros.com/public/product/image/869/2000732775/IPROS32741695349602353319.jpeg?w=280&h=280)
[Case Study] Accelerating the Design of Organic EL Materials through Active Learning
High efficiency and cost performance! An active learning workflow that utilizes the synergy of physics-based simulations and machine learning for predicting optoelectronic properties.
Molecular modeling and simulation tools have been proven effective for materials discovery and are increasingly being adopted in industrial research and development. Digital simulation significantly reduces the time required in research and development workflows compared to traditional experimental approaches, but challenges remain. Schrödinger has made it easier to address these challenges. Recently, Schrödinger developed an active learning workflow that leverages the synergy between physics-based simulations and machine learning for predicting optoelectronic properties. Recent research by Schrödinger, published in Frontiers in Chemistry and presented at SID-Display Week 2022, demonstrates an active learning paradigm for the discovery of OLED materials. *For more details, please refer to the PDF document or feel free to contact us.*
- 企業：シュレーディンガー
- 価格：Other
![[Presentation of Data] Improvement of Formulation Design Optimization through Coarse-Grained Molecular Simulation](https://image.mono.ipros.com/public/product/image/d03/2001250978/IPROS86833762217239053830.jpeg?w=280&h=280)
