AI 中文总结
研究多离子材料少样本性质预测问题，提出结合化学计量离子簇表示与多任务微调的方法，利用预训练原子间势，以多离子集成炸药为例，实现合成前筛选，扩展了已知材料化学，确立了新筛选策略。
AI 中文摘要
多离子材料在机器学习驱动的材料设计中提出了独特的表征挑战。与单分子或基于组成的材料不同，其性质源于带电构建块如何聚集成特定组件。本文展示了预训练的机器学习原子间势（MLIPs）如何绕过完整晶体结构预测，并以多离子集成炸药（MIXs）为例，支持从化学计量离子簇进行合成前筛选。该策略将化学计量离子簇表示（用非周期性、化学计量保留的公式单元簇表示每个候选材料）与多任务微调（MT-FT）相结合，MT-FT在保留能量-力目标作为稀疏爆速标签物理正则化的同时，调整预训练的原子骨架。通过MT-FT正则化的预训练骨架，该替代模型仅在25种结构精选的钙钛矿型含能材料（PEMs）上进行了交叉验证筛选，这些材料具有实验得出的Kamlet-Jacobs（K-J）爆速。表征探针表明，学习到的描述符隐含地保留了位点感知离子组织、密度信息和粗粒度堆积兼容性。该替代模型将已知的PEMs化学扩展到三种新合成的ABX₄材料，这些材料具有未见的ABX₄化学计量和未见的乙二胺铵B位阳离子，在不重新训练的情况下，与K-J参考速度有三点一致性，平均绝对误差（MAE）为92 m·s⁻¹。这些结果共同确立了化学计量保留簇学习作为数据稀缺的多离子材料面向合成的筛选策略。
英文摘要
Multi-ionic materials pose a distinct representational challenge in machine learning-driven materials design. Different from single-molecule or composition-based materials, their properties arise from how charged building blocks aggregate into specific assemblies. Here, we show 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. This strategy combines a stoichiometric ionic-cluster representation, which represents each candidate material by a non-periodic, stoichiometry-preserved formula-unit cluster, with multi-task fine-tuning (MT-FT), which adapts a pretrained atomistic backbone while retaining the energy--force objective as physical regularization for the sparse detonation-velocity labels. With the pretrained backbone regularized by MT-FT, this surrogate provides a cross-validated screen across only 25 structurally curated perovskite-type energetic materials (PEMs) with experimentally derived Kamlet--Jacobs (K--J) detonation velocities. Representation probes show that the learned descriptors implicitly retain site-aware ionic organization, density information, and coarse packing compatibility, implying why non-periodic clusters can remain predictive before full crystal structures are known. The surrogate extends known PEMs chemistry to three newly synthesized ABX$_4$ materials with both unseen ABX$_4$ stoichiometry and an unseen ethylenediammonium B-site cation, yielding three-point concordance with K--J reference velocities and a mean absolute error (MAE) of 92~m$\cdot$s$^{-1}$ without retraining. Together, these results establish stoichiometry-preserved cluster learning as a synthesis-facing screening strategy for data-scarce multi-ionic materials.
