MindScience 0.1.0
MindScience 0.1.0 Release Notes
Major Features and Improvements
MindSpore Elec
- Provide physics-driven and data-driven neural network for electromagnetic simulation
- Support CSG geometry model construction and CAD format data processing
- Include multiple scale filtering and dynamic adaptive weighted loss for improving performance
- Provide visualization tools for electromagnetic fields and scattering parameters
MindSPONGE
- Provide basic toolkits for molecular simulation, including MSA dataset, molecular pre-trained model(service on HUAWEI CLOUD), molecular dynamics。
- MSA dataset:Multiple Sequence Alignment Dataset for protein structure and function research
- Molecular Pre-trained Model:Trained with 1.7 billion compounds and its downstream tasks achieve SOTA
- Molecular Dynamics:Support basic MD functions,such as NPT, NVT, NVE and Minimization
MindChemistry
- Provide a high-entropy alloy composition design approach: Based on generation model and ranking model generating high-entropy alloy composition candidates and candidates' ranks, this approach constructs an active learning workflow for enabling chemistry experts to accelerate design of novel materials.
- Provide molecular energy prediction models: Based on equivariant computing library, the property prediction models NequIP and Allegro are trained effectively and infer molecular energy with high accuracy given atomic information.
- Provide an electronic Structure Prediction model: We integrate the DeephE3nnn model, an equivariant neural network based on E3, to predict a Hamiltonian by using the structure of atoms.
- Provide a crystalline material properties prediction model: We integrate the Matformer model, based on graph neural networks and Transformer architectures, for predicting various properties of crystalline materials.
- Provide an equivariant computing library: We provide basic modules such as Irreps, Spherical Harmonics as well as user-friendly equivariant layers such as equivarant Activation and Linear layers for easy construction of equivariant neural networks.
Contributors
Thanks goes to these wonderful people:
yufan, gaoyiqin, wangzidong, yangkang, lujiale, shibeiji, liuhongsheng, liyang, wengbingya, chuhaotian, huangxiang, wangmin, niningxi, zhangxinfeng, yujialiang, qianjiahong, chenmengyun, yanglijiang, yangyi, huangyupeng, xiayijie, zhangjun, linxiaohan, chendiqing, gongyue, gengchenhua, linghejing, yanchaojie, suyun, wujian, caowenbin
Contributions of any kind are welcome!