Research and development of new energy materials

Pioneering the AI for Science paradigm, DP Technology fuses multi-scale modeling, high-throughput computing with state-of-the-art artificial intelligence methods to provide end-to-end full stack solution for the research and development of new energy materials and devices.

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Explore the "genes" of material to achieve precise material design

Deep Potential (DP), a collection of physics-informed deep neural network models, achieves ab initio accuracy while maintaining an efficiency comparable to that of classical molecular dynamics. Already widely adopted in the material science field, Deep Potential (DP) is an advanced technology which can provide the following services:

Predict properties of various battery materials such as cathode, anode, electrolyte, and SEI with first-principles accuracy.

Simulate complex electrochemical mechanisms and kinetic processes of battery devices with first-principles accuracy.

Explore the microscopic "gene" between the structure and performance of energy materials, which helps to rationally design new materials.

Optimizing the performance of materials and devices using high-throughput computing and multi-scale modeling

By combining multi-scale modeling and high-throughput computing, global screening and optimization of material and device performance can be achieved. DP's team has accumulated expertise in multi-scale modeling algorithms from electron to atom to mesoscopic scales. Our team can provide the following services:

We can complete the system development from materials to devices (atoms to mesoscopic particles to electrode to battery cells) through multi-scale computational simulation.

Combined with high-throughput calculation of multi-scale modeling, we can complete the rapid screening of materials in the whole space, and help to customize new materials.

Combined with high-precision experimental characterization technology, a new material design flow of "computational design to experimental verification" is formed.

AI accelerates material component screening and device optimization

The design of new energy materials and devices is a complex process. "AI + HPC + physical model" can break the gap between computational simulation and experimental requirements. As a basic application tool, AI participates in the daily design of batteries and other new energy materials:

With the help of Bohrium micro-scale scientific computing cloud platform, AI comprehensively improves the efficiency of material research and development.

Combined with high-throughput computing, AI accelerates material screening and promotes rational design of new energy materials such as lithium batteries and photovoltaics.

Beyond the limits of scale and computational simulation, AI helps complete the whole chain optimization of battery materials and devices.