https://github.com/a11155/airs

Artificial Intelligence Research for Science (AIRS)

https://github.com/a11155/airs

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Artificial Intelligence Research for Science (AIRS)

https://github.com/a11155/AIRS/blob/main/

    



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# Artificial Intelligence Research for Science (AIRS)

AIRS is a collection of open-source software tools, datasets, and benchmarks associated with our paper entitled Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Our focus here is on AI for Science, including AI for quantum, atomistic, and continuum systems. Our goal is to develop and maintain an integrated, open, reproducible, and sustainable set of resources in order to propel the emerging and rapidly growing field of AI for Science. The list of resources will grow as our research progresses. The current list includes:
- [**OpenQM**](https://github.com/divelab/AIRS/tree/main/OpenQM): AI for Quantum Mechanics
- [**OpenDFT**](https://github.com/divelab/AIRS/tree/main/OpenDFT): AI for Density Functional Theory
- [**OpenMol**](https://github.com/divelab/AIRS/tree/main/OpenMol): AI for Small Molecules
- [**OpenProt**](https://github.com/divelab/AIRS/tree/main/OpenProt): AI for Protein Science
- [**OpenMat**](https://github.com/divelab/AIRS/tree/main/OpenMat): AI for Materials Science
- [**OpenMI**](https://github.com/divelab/AIRS/tree/main/OpenMI): AI for Molecular Interactions
- [**OpenBio**](https://github.com/divelab/AIRS/tree/main/OpenBio): AI for Biological Science
- [**OpenPDE**](https://github.com/divelab/AIRS/tree/main/OpenPDE): AI for Partial Differential Equations





    



## Methods
Here is the summary of methods we have in AIRS. More methods will be included as our research progresses.


  

    

      
 Quantum
      
 Atomistic
      
 Continuum
    
  
  

    

      
 
           OpenQM
          

            
LCN
            
DiffVMC
          
           OpenDFT
          

            
QHNet
            
QHBench
          
      
      
 
           OpenMol
          

            
SphereNet
            
ComENet
            
GraphDF
            
G-SphereNet
            
PACE
          
           OpenProt
          

            
ProNet
            
LatentDiff
          
      
      

           OpenMat
          

            
Matformer
            
PotNet
            
SyMat
              
ComFormer
              
GMTNet
              
Mat2Seq
          
           OpenMI
          

            
GraphBP
            
Frag2Seq
          
      
      
 
           OpenPDE
          

            
G-FNO
            
CFRF
            
SineNet
            
GeoMPNN
          
      
    
  



## Survey paper

**[Paper](https://arxiv.org/abs/2307.08423)** | **[Website](https://www.air4.science/)**

We released our survey paper about artificial intelligence for science in quantum, atomistic, and continuum systems. Please check our paper and website for more details!

### Citation

To cite the survey paper, please use the BibTeX entry provided below.

```
@article{zhang2023artificial,
  title={Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems},
  author={Xuan Zhang and Limei Wang and Jacob Helwig and Youzhi Luo and Cong Fu and Yaochen Xie and Meng Liu and Yuchao Lin and Zhao Xu and Keqiang Yan and Keir Adams and Maurice Weiler and Xiner Li and Tianfan Fu and Yucheng Wang and Haiyang Yu and YuQing Xie and Xiang Fu and Alex Strasser and Shenglong Xu and Yi Liu and Yuanqi Du and Alexandra Saxton and Hongyi Ling and Hannah Lawrence and Hannes St{\"a}rk and Shurui Gui and Carl Edwards and Nicholas Gao and Adriana Ladera and Tailin Wu and Elyssa F. Hofgard and Aria Mansouri Tehrani and Rui Wang and Ameya Daigavane and Montgomery Bohde and Jerry Kurtin and Qian Huang and Tuong Phung and Minkai Xu and Chaitanya K. Joshi and Simon V. Mathis and Kamyar Azizzadenesheli and Ada Fang and Al{\'a}n Aspuru-Guzik and Erik Bekkers and Michael Bronstein and Marinka Zitnik and Anima Anandkumar and Stefano Ermon and Pietro Li{\`o} and Rose Yu and Stephan G{\"u}nnemann and Jure Leskovec and Heng Ji and Jimeng Sun and Regina Barzilay and Tommi Jaakkola and Connor W. Coley and Xiaoning Qian and Xiaofeng Qian and Tess Smidt and Shuiwang Ji},
  journal={arXiv preprint arXiv:2307.08423},
  year={2023}
}
```

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