Inverse Design of Amorphous Materials With Targeted Properties
目標特性を持つアモルファス材料の逆設計 (AI 翻訳)
Jonas A. Finkler, Yan Lin, Tao Du, Jilin Hu, Morten M. Smedskjær
🤖 gxceed AI 要約
日本語
本研究では、アモルファス材料の逆設計手法として拡散モデルベースのフレームワークAMDEnを提案・検証。エネルギー貯蔵や触媒への応用が期待される。エネルギーに基づくバリアントとデータセットも導入。
English
This work proposes AMDEN, a diffusion model-based framework for inverse design of amorphous materials with targeted properties, relevant for energy storage and catalysis. It introduces an energy-based variant with Hamiltonian Monte Carlo refinement and new datasets.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では蓄電池材料の効率的開発がGX戦略の鍵であり、本手法は新材料探索の加速に寄与する可能性がある。ただし現時点では実用化段階ではなく、基礎研究としての位置づけ。
In the global GX context
The framework addresses the challenge of designing amorphous materials for energy applications, aligning with global GX needs for advanced energy storage. The method contributes to computational materials design, which is crucial for accelerating innovation in clean energy technologies.
👥 読者別の含意
🔬研究者:A new inverse design method for amorphous materials using diffusion models, with potential for energy storage and catalysis applications.
📄 Abstract（原文）
Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Their lack of long-range order and complex short- and medium-range order, which depend on composition as well as thermal and pressure history, offer a vast materials design space. To this end, relying on machine learning methods instead of trial and error is promising, and among these, inverse design has emerged as a tool for generating materials with desired properties. Although inverse design methods based on diffusion models have shown success for crystalline materials and molecules, similar methods targeting amorphous materials remain less developed, mainly because of the limited availability of large-scale datasets and the requirement for larger simulation cells. In this work, we propose and validate an inverse design method for amorphous materials, introducing AMDEN (Amorphous Material DEnoising Network), a diffusion model-based framework that generates structures of amorphous materials. First, we demonstrate the inherent challenges for diffusion models to generate relaxed structures. These low-energy configurations are typically obtained through a thermal motion-driven random search-like process that cannot be replicated by standard denoising procedures. We therefore introduce an energy-based AMDEN variant that implements Hamiltonian Monte Carlo refinement for generating these relaxed structures. We further introduce several amorphous material datasets with diverse properties and compositions to evaluate our framework and support future development.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/adma.202522493first seen 2026-06-12 04:55:35 · last seen 2026-06-16 04:47:44
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