游戏自相一致的场理论:生成块聚合物阶段的发现
Pengyu Chen1, Kevin D Dorfman1
1Department of Chemical Engineering and Materials Science, University of Minnesota-Twin Cities, Minneapolis, MN 55455.
概括
一种新的计算方法使用人工智能来发现新的区块聚合物形态. 这种方法训练了一个具有自相一致的场理论 (SCFT) 数据的生成对抗网络 (GAN),使其能够预测新的软物质结构.
科学领域:
- 软物质物理学 软物质物理学
- 聚合物科学 聚合物科学
- 计算材料科学科学 计算材料科学
背景情况:
- 块聚合物是研究软物质自组合的模型系统,因为它们可以通过自相一致的场理论 (SCFT) 描述热力学.
- SCFT对于解释实验数据和构建相位图是有效的,这引起了人们对其用于指导实验设计的兴趣.
- 对于阶段发现的SCFT的一个关键限制是要求预先指定候选阶段,这阻碍了新型形态的识别.
研究的目的:
- 为了克服使用SCFT在区块聚合物中发现新形态的挑战.
- 开发一条用于加速块聚合物相位发现的计算管道.
- 展示一种新的方法,用于产生超出已知的结构的候选阶段.
主要方法:
- 训练了一个深度卷积生成对抗网络 (GAN),使用从融合SCFT解决方案的轨迹.
- 部署训练好的GAN来生成用于后续SCFT计算的输入字段.
- 应用了计算管道来研究整洁的双块共聚合物融中的网络相位形成.
主要成果:
- 在一个由五个网络组成的小型培训组中,GAN成功地生成了一组多样化的349个候选阶段.
- 发现的阶段包括已知的形态和以前未经探索的结构.
- 发现了一种新的性网络形态学,展示了该方法在发现复杂结构方面的能力.
结论:
- 开发的计算管道,集成GAN和SCFT,有效地解决了区块聚合物相位发现的挑战.
- 这种人工智能驱动的方法显著扩大了软物质形态学的探索.
- 开源管道有望在块聚合物研究和其他软物质系统中得到广泛应用.
相关概念视频
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