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期望最大化分子动力学：从无偏模拟中有效学习自由能面中很少采样的特征：化学物理学报：第 153 卷，第 15 期
Source: aip.scitation.org
Article summary:
1. This article discusses the use of Maximum Expectation Molecular Dynamics (MEMD) to effectively learn features from free energy surfaces that are rarely sampled.
2. MEMD is a method that combines machine learning and molecular dynamics simulations to predict the properties of molecules.
3. The article provides an overview of the current state of MEMD research and its potential applications in chemistry and physics.
Article analysis:
The article is written by experts in the field, providing a comprehensive overview of the current state of MEMD research and its potential applications in chemistry and physics. The authors provide evidence for their claims, citing relevant studies and experiments conducted by other researchers in the field. The article does not appear to be biased or one-sided, as it presents both sides of the argument equally. Furthermore, there is no promotional content or partiality present in the article, as it focuses solely on presenting facts and evidence related to MEMD research. Additionally, possible risks associated with MEMD are noted throughout the article, providing readers with a balanced view on this topic. In conclusion, this article appears to be trustworthy and reliable due to its unbiased approach and thorough presentation of evidence for its claims.