Ziheng Lu

Jan 16, 2025 | nature.com | Claudio Zeni |Robert Pinsler |Daniel Zügner |Andrew Fowler |Matthew Horton |Xiang Fu | +8 more

AbstractThe design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture1–3. Generative models provide a new paradigm for materials design by directly generating novel materials given desired property constraints, but current methods have low success rate in proposing stable crystals or can only satisfy a limited set of property constraints 4−11.

Dec 6, 2024 | dx.doi.org | Yukun Xia |Chao Wang |Erqin Guo |Ziheng Lu

Dec 6, 2024 | pubs.acs.org | Yukun Xia |Chao Wang |Ziheng Lu |Erqin Guo

1. IntroductionClick to copy section linkSection link copied!The relentless pursuit of renewable energy sources has propelled the development of organic solar cells (OSCs), positioning them as a compelling alternative to the established silicon-based photovoltaic technology. (1−4) OSCs offer several advantages, including lightweight, flexibility, and the potential for low-cost manufacturing processes.

May 13, 2024 | microsoft.com | Jielan Li |Hongxia Hao |Ziheng Lu |Brenda Potts

In the quest for groundbreaking materials crucial to nanoelectronics, energy storage, and healthcare, a critical challenge looms: predicting a material’s properties before it is even created. This is no small feat, with any combination of 118 elements in the periodic table, and the range of temperatures and pressures under which materials are synthesized and operated.

Jul 7, 2023 | microsoft.com | Shuxin Zheng |Chang Liu |Yu Shi |Ziheng Lu

Structure prediction is a fundamental problem in molecular science because the structure of a molecule determines its properties and functions. In recent years, deep learning methods have made remarkable progress and impact on predicting molecular structures, especially for protein molecules.