研究人员开发了VibroML，一个开源Python工具包，旨在自动化晶体材料动态不稳定的修复。该工具包利用机器学习势能和能量引导的遗传算法来高效发现稳定的多晶型物，优于传统方法。VibroML还包含用于有限温度验证的自动化分子动力学，并可与结构预测引擎耦合，通过合金化稳定复杂的晶体拓扑结构。 AI
影响 自动化发现稳定的晶体材料，加速材料科学的研究和开发。
排序理由 该集群描述了一个新的开源材料科学研究工具包，已在arXiv上发布。
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arXiv:2604.27685v1 Announce Type: cross Abstract: While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is insufficient; automated pathways to resolve them …
While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is insufficient; automated pathways to resolve them are required. We introduce VibroML, an open-source…
While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is insufficient; automated pathways to resolve them are required. We introduce VibroML, an open-source…