AI-enabled Multi-Scale Simulation Technology Solution for New Energy Industry
-- Battery Design Automation (BDA) Platform
MattVerse has accumulated years of technical expertise, and its technology cluster is integrated into three modules to handle problems encountered at different scales in scenarios.
AI + Quantum Chemistry Module
This module integrates artificial intelligence technology with quantum chemistry computational methods to accurately predict the properties of battery materials. It has several features:
a. High computational accuracy, achieving a practical level that surpasses traditional quantum chemistry methods by an order of magnitude.
b. High computational efficiency, with computing speed improved by over 10,000 times compared to traditional DFT methods.
c. Minimal data requirement for AI model construction, achieving effective results with just a few experimental data points.
d. Capable of accurately describing open quantum systems such as electrochemical interfaces, allowing for the exchange of matter and energy with environment.
Electrochemical Module
This module consists of 3 coupled physical models: an electrochemical model for assessing battery performance, a thermodynamic model for evaluating safety, and an aging model for assessing battery health and lifespan. The module has the following features:
a. By deeply embedding AI methods to solve electrochemical equations and utilizing a smart mesh generation technique, the computational efficiency is improved by over two orders of magnitude compared to traditional electrochemical algorithms..
b. Comprehensive aging mechanism models accurately describe the kinetic processes of various aging factors.
Health & Safety Module
This module combines AI technology with battery aging mechanism simulations to establish a multidimensional digital twin that accurately describes the health status and aging process of battery cells. It enables accurate assessment of battery lifespan and effective early warning for battery anomalies.
