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Updated: May 6, 2026

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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用机器学习和量子计算机收集化学理解
Shubin Liu1,2
1Research Computing Center, University of North Carolina, Chapel Hill, North Carolina 27599-3420, United States.
ACS physical chemistry Au
|April 1, 2024
概括
机器学习 (ML) 和量子计算机 (QC) 准备通过提供解决施罗丁格方程的新方法来彻底改变理论化学. 克服ML和QC当前的挑战将通过先进的计算方法来解开更深层次的化学见解.
科学领域:
- 理论和计算化学 理论和计算化学
- 量子计算是一种量子计算.
- 机器学习 机器学习
背景情况:
- 传统的方法,如波函数理论和密度函数理论,提供化学理解.
- 预测化学计算的未来需要从过去的进步中学习.
研究的目的:
- 概述理论和计算在化学中的当前作用.
- 预测机器学习 (ML) 和量子计算机 (QC) 对化学理解的影响.
- 提出克服ML和QC开发中的挑战的途径.
主要方法:
- 审查波函数理论和密度函数理论的应用.
- 分析ML和QC在解决施罗丁格方程中的潜力.
- 识别挑战,并为ML和QC实施提出解决方案.
主要成果:
- ML和QC代表了解决施罗丁格方程的范式转变.
- 利用ML特征和QC量子比特可以带来新的化学理解.
- 在ML和QC的开发和应用方面仍然存在重大障碍.
结论:
- ML和QC将改变理论和计算化学.
- 克服当前的障碍对于实现ML和QC的潜力至关重要.
- 预计等级建模将成为in silico模拟的主要方法.
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