By Greg Gao

(JW Insights) Aug 21 -- China’s AI startup DPTechnology(深势科技) recently completed a new funding round, raising over RMB700 million(about $96 million).

Participating in this financing round are Zhongyuan Capital, MSA Capital, Loyal Valley Capital, Evergreen Scitech Delta, and several other industrial funds .

The funds raised from this round will be primarily used to continue attracting top talents in the industry, deepening the full-stack capabilities from algorithmic innovation to industrial software and solutions, and speeding the industrialization and commercialization of related technologies in sectors such as biomedicine, new energy, chemicals, and semiconductors.

Founded in 2018 and headquartered in Beijing, DPTechnology has established R&D centers in Shanghai and Shenzhen. Its research and technology team is led by academicians from the Chinese Academy of Sciences, bringing together over a hundred scientists and engineers from various fields including mathematics, physics, chemistry, biology, materials, and computer science, according to the company.

DPTechnology is the pioneer of the AI for Science scientific research paradigm. It is dedicated to using artificial intelligence and molecular simulation algorithms, combined with advanced computing methods, to solve important scientific problems and create a new generation of micro-scale industrial design and simulation platforms for human civilization’s most fundamental biomedical, energy, materials, and information science research.

Powered by AI for Science new paradigm, DPTechnology combined multi-scale modeling, machine learning with high-performance computing for scientific research. The company has introduced a series of computation engines and pre-trained models that address fundamental scientific challenges such as micro- and mesoscale simulation, molecular property prediction, and experimental characterization optimization.

Over the past year, DPTechnology and its partners have released a series of significant technological achievements, upgrading a range of AI for Science infrastructures, and propelling its conception into the era of pre-trained models.