
BattGen is a multimodal generative framework that accelerates virtual screening and characterization of battery materials. This framework uses latent diffusion model methodology to translate the data from characterization techniques such as atomic force microscopy to meaningful material information and screen battery materials based on battery functional properties such as average voltage, volume change, gravimetric and volumetric capacity, and working ion of required battery systems. This record contains the source data, model architecture, and training script used for the study. For access to machine learning tools defined in the training script, use https://gitlab.com/intelligent-analysis/cids/-/tree/v3.2a conda environment : requirements.txt
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
