通过解决小数电子问题,推进密度函数的边界
James Kirkpatrick1, Brendan McMorrow1, David H P Turban1
1DeepMind, 6 Pancras Square, London N1C 4AG, UK.
概括
研究人员开发了一种基于神经网络的新函数 DM21,以克服密度函数理论中的系统错误. 这种先进的量子化学工具准确地模拟了复杂的分子系统,
科学领域:
- 量子化学
- 计算材料科学
- 科学中的人工智能
背景情况:
- 密度函数理论 (DFT) 对于描述物质的量子力学属性至关重要.
- 现有的DFT近似包含由于违反确切的功能性质而导致的系统错误.
- 这些错误限制了DFT对各种化学和材料系统的准确性.
研究的目的:
- 克服传统密度函数理论中的基本局限性.
- 开发一个新功能,准确地描述带有小数电荷和旋转的系统.
- 建立一个通往量子力学计算的通用函数的途径.
主要方法:
- 在分子数据上训练神经网络.
- 在训练过程中使用虚构系统.
- 开发基于神经网络输出的DM21 (DeepMind 21) 功能.
主要成果:
- 在主要组原子和分子的基准中,DM21表现优于传统的功能性.
- 新的功能模型准确地模拟了具有挑战性的系统,包括链,充电的DNA基对和二基过渡状态.
- DM21正确地描述了人造电荷转移和强相关现象.
结论:
- DM21函数代表了密度函数理论的重大进展.
- 这种以数据为导向,以约束为基础的方法提供了一条可行的通往精确的通用函数的途径.
- 这种方法可以为广泛的量子化学和材料科学应用提供更高的精度.
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