npj ：机器学习——预测材料淬火无序分布

海归学者发起的公益学术平台

分享信息，整合资源

交流学术，偶尔风月

淬火无序现象，是人们对各种材料（如FCC和BCC晶体、无定形固体）和地震地质断层突发塑性事件或材料爆裂噪声事件进行观察而得到认识的。爆裂噪声可由随机-场模型或界面定位模型加以解释，涉及均匀固体的弹性、局域淬火无序，以及微观状态空间允许的不均匀和随机分布。然而局域淬火无序却一直难以测量。美国西弗吉尼亚大学的Stefanos Papanikolaou教授采用无监督机器学习方法并结合聚类算法，以期从具有爆裂噪声随时间演化行为的应力-应变曲线中获得淬火、局域的无序分布。该方法在两种爆裂噪声模型中能成功实现数据的聚类和分类，并从镍微柱单轴压缩实验的数据中成功得到了淬火无序的分布。这是典型的时间局域可观察参量（如突发事件大小/持续时间）途径所无法企及的。作者将这一方法记作时间序列-机器学习法。若将这些淬火无序分布的识别及分类扩展到不同材料、加载模式和样品加载历史中，将有助于建立随机屈服分布的数据库，进而改进多尺度力学模型。该文近期发表于npj Computational Materials 4:27(2018);  doi:10.1038/s41524-018-0083-x。英文标题与摘要如下，点击“阅读原文”可以自由获取论文PDF。

Learning local, quenched disorder in plasticity and other crackling noise phenomena

Stefanos Papanikolaou

When far from equilibrium, many-body systems display behavior that strongly depends on the initial conditions. A characteristic such example is the phenomenon of plasticity of crystalline and amorphous materials that strongly depends on the material history. In plasticity modeling, the history is captured by a quenched, local and disordered flow stress distribution. While it is this disorder that causes avalanches that are commonly observed during nanoscale plastic deformation, the functional form and scaling properties have remained elusive. In this paper, a generic formalism is developed for deriving local disorder distributions from field-response (e.g., stress/strain) timeseries in models of crackling noise. We demonstrate the efficiency of the method in the hysteretic random-field Ising model and also, models of elastic interface depinning that have been used to model crystalline and amorphous plasticity. We show that the capacity to resolve the quenched disorder distribution improves with the temporal resolution and number of samples.

扩展阅读

 

本文系网易新闻·网易号“各有态度”特色内容

媒体转载联系授权请看下方