一个通用图表深度学习原子间潜力周期表的周期表
1Department of NanoEngineering, University of California, San Diego, CA, USA. chenc273@outlook.com.
Nature computational science
|January 4, 2024
概括
一个名为M3GNet的新型通用原子间潜力 (IAP),利用图形神经网络,准确地预测材料特性. 这种机器学习模型加速了新型,稳定和可合成材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 原子间潜能 (IAP) 对于原子模拟至关重要,但现有的模型缺乏普遍适用性.
- 目前的IAP通常仅限于特定的化学物质,或缺乏广泛使用所需的准确性.
研究的目的:
- 开发用于材料科学应用的通用原子间潜力.
- 创建一个基于机器学习的IAP,能够处理各种化学空间并准确预测材料特性.
主要方法:
- 开发了M3GNet,一个基于神经网络的图表,包含三体相互作用的原子间潜力.
- 从材料项目中训练了M3GNet的大型结构放松数据集.
- 应用M3GNet用于选假设的晶体结构和预测材料稳定性.
主要成果:
- M3GNet在结构放松,动态模拟和财产预测方面展示了广泛的适用性.
- 选了3100万个假设结构,使用M3GNet能量确定了180万个潜在的稳定材料.
- 密度函数理论 (DFT) 的计算验证了前2000种最低能耗材料中的1578种材料的稳定性.
结论:
- M3GNet为各种材料提供了通用和准确的原子间潜力.
- 机器学习加速发现新的,稳定的和潜在的可合成材料.
- 这种方法为发现具有特殊性质的材料提供了一条途径.
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