Wei-Xiong Zhang | Searcharxiv

arXiv2026

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

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.

cond-mat.mtrl-sci↗

arXiv2025

Unraveling Antagonistic Collision-Controlled Reactivity in Energetic Molecular Perovskites with Deep Potential Molecular Dynamics

The precise regulation of chemical decompositions in energetic materials, whether towards rapid ignition or stable endurance, requires atomic-scale principles governing reactivity, which remain elusive yet. Herein, we resolve this challenge through deep potential molecular dynamics (DPMD) simulations, uncovering a universal collision-control principle in energetic molecular perovskites, $\text{(H}_{2}\text{dabco)B(ClO}_{4}\text{)}_{3}$, where $\text{H}_{2}\text{dabco}^{2+}$ = 1,4-diazabicyclo[2.2.2]octane-1,4-diium, B = $\text{Na}^{+}$, $\text{K}^{+}$, $\text{Rb}^{+}$, $\text{NH}_{4}^{+}$ for DAP-1, DAP-2, DAP-3 and DAP-4, respectively. Atomic-scale simulation with Arrhenius fitting for over 100-ps trajectories reveals that increasing B-site ionic radius ($\text{Na}^{+} < \text{K}^{+} < \text{Rb}^{+}$) simultaneously reduces both activation energy $E_{a}$, which enhances reactivity, and pre-exponential factor $A$ which suppresses collision probabilities for hydrogen transfer between site $X$ and site $A$, with sharply opposing kinetic consequences. This duality well explains the peak stability and insensitivity in $\text{K}^{+}$-based DAP-2, which optimally balance thermal endurance and collision dissipation. For ammonium-based DAP-4, though the radius of $\text{NH}_{4}^{+}$ is close to $\text{K}^{+}$, the reactive B-site cation triggers proton transfer that promotes $\text{C-H}$ bond rupture. By linking static cation radii to dynamic $E_{a}$-ln$(A)$ coupling, we rationalize non-monotonic decomposition temperatures ($\text{K}^{+} > \text{NH}_{4}^{+} > \text{Rb}^{+} > \text{Na}^{+}$) macroscopic stability and establishes cornerstones for universal energetic material design.

cond-mat.mtrl-sci↗