Crystallization of the amorphous phases into metastable crystals plays a
fundamental role in the formation of new matter, from geological to biological
processes in nature to synthesis and development of new materials in the
laboratory. Predicting the outcome of such phase transitions reliably would
enable new research directions in these areas, but has remained beyond reach
with molecular modeling or ab-initio methods. Here, we show that
crystallization products of amorphous phases can be predicted in any inorganic
chemistry by sampling the crystallization pathways of their local structural
motifs at the atomistic level using universal deep learning potentials. We show
that this approach identifies the crystal structures of polymorphs that
initially nucleate from amorphous precursors with high accuracy across a
diverse set of material systems, including polymorphic oxides, nitrides,
carbides, fluorides, chlorides, chalcogenides, and metal alloys. Our results
demonstrate that Ostwald's rule of stages can be exploited mechanistically at
the molecular level to predictably access new metastable crystals from the
amorphous phase in material synthesis.

通过使用通用深度学习模型在微观层面对无机化学中的非晶态结构进行结晶途径采样，我们展示了可以高精度地预测非晶态前体起始生成的多态结晶体结构，跨包括多态氧化物、氮化物、碳化物、氟化物、氯化物、硫化物和金属合金在内的多样化材料体系，从而表明奥斯瓦尔德阶段定律可以在分子层面上机械化地用于材料合成中可预测地获得新的亚稳相晶体。

利用深度学习预测非晶物质中的晶体生成

Predicting emergence of crystals from amorphous matter with deep  learning

Creating high quality and realistic materials in computer graphics is a
challenging and time-consuming task, which requires great expertise. In this
paper, we present MatFuse, a novel unified approach that harnesses the
generative power of diffusion models (DM) to simplify the creation of SVBRDF
maps. Our DM-based pipeline integrates multiple sources of conditioning, such
as color palettes, sketches, and pictures, enabling fine-grained control and
flexibility in material synthesis. This design allows for the combination of
diverse information sources (e.g., sketch + image embedding), enhancing
creative possibilities in line with the principle of compositionality. We
demonstrate the generative capabilities of the proposed method under various
conditioning settings; on the SVBRDF estimation task, we show that our method
yields performance comparable to state-of-the-art approaches, both
qualitatively and quantitatively.

MatFuse 是一种基于扩散模型的新型统一方法，用于简化计算机图形学中高质量逼真材料的创建，通过集成多种条件来源实现细粒度控制和材料合成的灵活性，该方法展示出与最先进方法相当的性能，同时丰富了创作可能性。