MLIP-3:在具有动量张量潜力的原子环境中进行主动学习
Evgeny Podryabinkin1, Kamil Garifullin2, Alexander Shapeev1
1Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Bolshoy boulevard 30, Moscow 143026, Russian Federation.
The Journal of chemical physics
|August 28, 2023
概括
研究人员介绍了MLIP-3,这是一个用于创建和训练动量张量潜力的新包. 这种增强的软件使用机器学习潜力和对原子社区的积极学习来改进原子模拟.
科学领域:
- 计算材料科学 计算材料科学
- 机器学习在物理学中的应用
背景情况:
- 在原子模型中,共享研究代码至关重要.
- 机器学习的潜力正在迅速推进这个领域.
- 现有的软件包有助于古典和量子力学建模.
研究的目的:
- 介绍MLIP-3包,用于构造和训练矩张力电位.
- 通过改进机器学习潜力来增强原子模拟.
- 在大规模模拟中利用原子社区的积极学习.
主要方法:
- 开发了MLIP-3软件包.
- 动量张量潜力的实现.
- 积极学习技术的应用,用于模型培训.
主要成果:
- 与其前身MLIP-2相比,MLIP-3提供了改进的功能.
- 该套件有助于高效地构建和训练矩张力电位.
- 积极学习应用于原子社区,以提高模拟准确度.
结论:
- MLIP-3代表了原子模型软件的重大进步.
- 该包支持机器学习潜力的不断增长领域.
- 增强的积极学习能力提高了大规模模拟的效率和准确性.
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