Dasgupta Aparajita, Broderick Scott R, Mack Connor, Kota Bhargava U, Subramanian Ramachandran, Setlur Srirangaraj, Govindaraju Venu, Rajan Krishna
Department of Materials Design and Innovation, University at Buffalo, New York, USA.
Department of Computer Science and Engineering, University at Buffalo, New York, USA.
Sci Rep. 2019 Jan 23;9(1):357. doi: 10.1038/s41598-018-36224-3.
The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions. Our data driven approach takes into account not only a broad variety of thermodynamic, structural and kinetic based criteria, but also incorporates qualitative and descriptive attributes associated with eutectic points in phase diagrams. For the latter, we demonstrate the use of automated machine learning methods that go far beyond text recognition approaches by also being able to interpret phase diagrams. When combined with structural descriptors, this approach provides the foundations to develop a hierarchical probabilistic predication tool that can rank the feasibility of glass formation.
使用机器学习技术来加速新材料的发现和开发是迈向新一代特定领域高功能材料系统加速发展的关键一步。在本文中，我们以块状金属玻璃为例，突出高通量预测领域的关键问题，并使用粗糙集理论提出一种关于玻璃形成能力规则的新概率分析方法。这种方法已应用于广泛的二元合金成分，以预测新的金属玻璃成分。我们的数据驱动方法不仅考虑了各种基于热力学、结构和动力学的标准，还纳入了与相图中共晶点相关的定性和描述性属性。对于后者，我们展示了自动化机器学习方法的使用，这些方法不仅能够解释相图，还远远超越了文本识别方法。当与结构描述符相结合时，这种方法为开发一种能够对玻璃形成的可行性进行排名的分层概率预测工具奠定了基础。
