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人工智能:材料科学:chemeleon2:chemeleon2-基本概况 [2026/01/30 03:40] – [应用场景] ctbots人工智能:材料科学:chemeleon2:chemeleon2-基本概况 [2026/01/30 03:41] (当前版本) – ctbots
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 ==== 应用场景 ==== ==== 应用场景 ====
 该框架专为定向材料发现而设计，用户可以通过定义自定义奖励函数将晶体生成引导到具有特定材料性质的方向。 该框架专为定向材料发现而设计，用户可以通过定义自定义奖励函数将晶体生成引导到具有特定材料性质的方向。
-==== Notes ==== 
-- 项目采用 MIT 许可证开源 
-- 有相关的学术论文发表在 arXiv（2511.07158） 
-- 提供了完整的文档、教程和基准测试数据集 
-- 支持使用 PyTorch 和 CUDA 进行加速训练 
  
-===== Citations ===== 
-**File:** README.md (L4-4) 
-<code markdown>A reinforcement learning framework in latent diffusion models for crystal structure generation using group relative policy optimization.</code> 
  
-**File:** README.md (L19-23) 
-<code markdown>Chemeleon2 implements a three-stage pipeline for crystal structure generation: 
- 
-1. **VAE Module**: Encodes crystal structures into latent space representations 
-2. **LDM Module**: Samples crystal structures in latent space using diffusion Transformer 
-3. **RL Module**: Fine-tunes the LDM with **custom reward functions** to optimize for specific material properties</code> 
- 
-**File:** README.md (L25-25) 
-<code markdown>**Key Feature:** Design custom reward functions to guide generation toward desired properties (band gap, density, stability, etc.) using a simple Python interface.</code> 
- 
-**File:** README.md (L76-76) 
-<code markdown>Chemeleon2's RL module enables you to guide crystal generation toward specific material properties by defining custom reward functions. This is the framework's key differentiator for targeted materials discovery.</code> 
- 
-**File:** README.md (L112-119) 
-<code markdown> 
- 
-| Component | Purpose | 
-|-----------|---------| 
-| **CreativityReward** | Reward unique and novel structures | 
-| **EnergyReward** | Penalize high energy above convex hull | 
-| **StructureDiversityReward** | Encourage diverse crystal geometries | 
-| **CompositionDiversityReward** | Encourage diverse chemical compositions | 
-| **PredictorReward** | Use trained ML models as reward functions | 
-</code>