arXiv subjects

Jianhua Hao

Publications and source records attributed to Jianhua Hao.

3 recordsLinked to original sources

arXiv2026

Optically driven thermodynamic transition from free- to locked-epitaxy

Controlling crystallographic orientation in quasi-van der Waals (vdW) epitaxy remains a fundamental challenge, especially for material systems located near the boundary between weakly and strongly coupled growth regimes. In such marginal systems, epitaxial selection is governed by a delicate thermodynamic competition between surface-energy penalties and interfacial interaction gains, giving rise to two archetypal limits: vdW-dominated free-epitaxy and strong interfacial coupling dominated locked-epitaxy. However, dynamically driving transitions between these regimes has remained elusive. Here, we demonstrate that external light irradiation can deterministically induce such a transition. Using the thermodynamically frustrated Fe4N/mica interface as a model system, we show that photo-excited carriers act as a chemical potentiator, significantly enhancing the interfacial chemical affinity. Within a quantitative thermodynamic description, this optical modulation increases the locking criterion (I_lock)-defined as the ratio of interfacial energy gain to surface-energy cost-beyond its critical threshold. As a result, the system switches from vdW-dominated free-epitaxy with (001) orientation to chemically locked-epitaxy with (111) orientation. Our findings establish light as a non-invasive and switchable control knob to dynamically reconfigure the interfacial energy landscape in quasi-vdW epitaxy, enabling programmable access to distinct epitaxial states beyond intrinsic material limitations.

cond-mat.mtrl-sci↗

arXiv2025

Universal thermodynamic framework for quasi-van der Waals epitaxy

van der Waals (vdW) epitaxy is conventionally regarded as a rotation-free and strain-free growth mode driven by weak, isotropic interactions, yet many interfaces paradoxically exhibit strictly locked orientations that defy standard surface-energy models. We resolve this inconsistency by establishing a unified quantitative framework for 2D-3D systems, in which strong electrostatic and chemical interactions compete with entropic forces. We introduce a two-tier descriptor set-the predictive index (I_pre) and the thermodynamic locking criterion (I_lock)-to quantify the energetic sufficiency for locked epitaxy. Our theory accurately predicted the competitive interactions at the interface within the 2D-3D system, precisely characterized whether the epitaxial layer underwent free growth or was constrained in a locked growth mode, demonstrating robust consistency with diverse experimental observations. This framework unifies orientation selection in 3D-on-2D films and rotational locking in 2D-on-3D layers within a single-phase diagram. Our work provides a generalizable, predictive route to controlling epitaxial orientation across a broad spectrum of layered heterostructure

cond-mat.mtrl-sci↗

arXiv2025

Fingerprinting Organic Molecules for the Inverse Design of Two-Dimensional Hybrid Perovskites with Target Energetics

Artificial intelligence (AI)-assisted workflows have transformed materials discovery, enabling rapid exploration of chemical spaces of functional materials. Endowed with extraordinary optoelectronic properties, two-dimensional (2D) hybrid perovskites represent an exciting frontier, but current efforts to design 2D perovskites rely heavily on trial-and-error and expert intuition approaches, leaving most of the chemical space unexplored and compromising the design of hybrid materials with desired properties. Here, we introduce an inverse design workflow for Dion-Jacobson perovskites that is built on an invertible fingerprint representation for millions of conjugated diammonium organic spacers. By incorporating high-throughput density functional theory (DFT) calculations, interpretable machine learning, and synthesis feasibility screening, we identified new organic spacer candidates with deterministic energy level alignment between the organic and the inorganic motifs in the 2D hybrid perovskites. These results highlight the power of integrating invertible, physically meaningful molecular representations into AI-assisted design, streamlining the property-targeted design of hybrid materials.

cond-mat.mtrl-sci↗