用机器学习势自动修复晶体材料动态不稳定性，提升高通量筛选效率。
VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
- 基于能量引导的遗传算法搜索稳定晶相，优于传统软模追踪方法。
- 在0K和有限温条件下均验证结构稳定性，确保宏观可行性。
- 适合材料设计、高通量筛选与复杂合金体系结构修复的研究者使用。
虽然机器学习势（MLIPs）加速了声子色散计算，但仅识别计算预测材料中的动态不稳定性仍不足；亟需自动化修复路径。本文提出VibroML，一个由基础MLIP驱动的开源Python工具包，将研究范式从稳定性验证转向自动化结构修复。VibroML采用能量引导的遗传算法，显著优于传统软模追踪方法，能高效探索势能面，发现多种动态稳定的多型体。由于0K下的谐波稳定性无法保证宏观可用性，该工具还集成分子动力学流程，评估有限温度下的结构保持能力。VibroML还可与ProtoCSP组合使用，通过靶向合金化稳定受阻的晶体拓扑结构，成功恢复如Cs₂KInI₆和KTaSe₃等功能性钙钛矿网络。为验证普适性，我们从Alexandria数据库中挖掘——其中约50%四元、99.5%五元元素组合无任何结构条目——筛选出数千个被忽略的高对称化学计量比。对样本实施ProtoCSP的“冷启动”检索与VibroML的进化搜索后，成功识别出动态稳定的低对称候选结构。通过整合结构修复、热稳定性验证与系统性成分探索，VibroML实现全面深度筛选，产出远超常规高通量工作流的物理可信结构方案。
原文摘要 · Abstract (English)
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 Python toolkit driven by foundational MLIPs that shifts the paradigm from stability verification to automated structural remediation. VibroML employs an energy-guided genetic algorithm that vastly outperforms traditional soft-mode following, efficiently navigating the potential energy surface to uncover diverse, dynamically stable polymorphs. As 0 K harmonic stability does not guarantee macroscopic viability, an automated molecular dynamics workflow evaluates finite-temperature structural retention. VibroML also couples with ProtoCSP, our combinatorial structure prediction engine, to stabilize frustrated crystal topologies via targeted alloying, successfully rescuing functional perovskite networks like Cs$_2$KInI$_6$ and KTaSe$_3$. Demonstrating broader applicability, we mined the Alexandria database -- where ~50% of quaternary and 99.5% of quinary elemental combinations lack any structural entries -- to identify thousands of abandoned, high-symmetry stoichiometries. Deploying ProtoCSP's "cold start" retrieval and VibroML's evolutionary search on a sample, we successfully identified dynamically stable low-symmetry candidates. Through integrated structural remediation, thermal validation, and systematic compositional exploration, VibroML enables a comprehensive deep-screening approach, yielding physically sound structural propositions that far surpass standard high-throughput workflows.
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