Yu Shang

Stoichiometric cluster learning for few-shot property prediction of multi-ionic integrated energetic materials

It is shown how pretrained machine-learned interatomic potentials (MLIPs) can bypass full crystal-structure prediction and support pre-synthesis screening from stoichiometric ionic clusters using multi-ionic integrated explosives (MIXs) as a synthesis-facing example.