A Padding Method for Enhanced Encoding of Inorganic Structures with Varying Chemical Compositions
Designing novel inorganic materials through generative models remains an important challenge for material science, driven by the complexity and diversity of inorganic structures across expansive chemical compositions and structural landscape. The vast combinatorial space of inorganic compounds demands innovative, AI-driven approaches to overcome limitations in generative accuracy and efficiency. To address this, we introduce a novel method that redefines the encoding and generation of inorganic materials by utilizing domain-specific symmetry-aware representation. Our approach not only refines the representation of intricate inorganic structures but also contributes to the field of materia...
| Authors | Thang Dang, Haderbache Amir, Tzanakakis Alexandros et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 29 May 2026 |
| Last updated at source | 1 June 2026 |
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