2 citations · 2 across the 5 of their papers we have counts for
4 papers
Precipitate phase selection and grain boundary morphology in Cu-Ni-Si-Mn alloys: A machine-learning interatomic potential study
Aadil Fayaz Wani, Il-Seok Jeong, Haekwan Jeon +6
Alloys inevitably contain interphase boundaries, whose energetics govern nucleation processes and precipitate morphology. In Cu-Ni-Si alloys, Mn addition markedly changes grain bou…
LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
Sangjun Bae, Yisak Park, Sanghyeon Lee +1
Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information excha…
Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
Sunwoo Lee, Mingu Kang, Yonghyeon Jo +1
Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter-agent interactions. Prior rob…
Asymmetric Electronic Band Alignment and Potentially Enhanced Thermoelectric Properties in Phase-Separated Mg2X (X=Si,Ge,Sn) Alloys
Byungki Ryu, Samuel Foster, Eun-Ae Choi +8
The Mg2X (X=Si, Ge, Sn) based alloy is an eco-friendly thermoelectric material for mid-temperature applications. The Mg2Si1-xSnx and Mg2Ge1-xSnx alloys can be phase-separated into…