Non-empirical identification of trigger sites in heterogeneous processes using persistent homology

Macroscopic phenomena, such as fracture, corrosion, and degradation of materials, are associated with various reactions which progress heterogeneously. Thus, material properties are generally determined not by their averaged characteristics but by specific features in heterogeneity (or ‘trigger sites’) of phases, chemical states, etc., where the key reactions that dictate macroscopic properties initiate and propagate. Therefore, the identification of trigger sites is crucial for controlling macroscopic properties. However, this is a challenging task. Previous studies have attempted to identify trigger sites based on the knowledge of materials science derived from experimental data (‘empirical approach’). However, this approach becomes impractical when little is known about the reaction or when large multi-dimensional datasets, such as those with multiscale heterogeneities in time and/or space, are considered. Here, we introduce a new persistent homology approach for identifying trigger sites and apply it to the heterogeneous reduction of iron ore sinters. Four types of trigger sites, ‘hourglass’-shaped calcium ferrites and ‘island’- shaped iron oxides, were determined to initiate crack formation using only mapping data depicting the heterogeneities of phases and cracks without prior mechanistic information. The identification of these trigger sites can provide a design rule for reducing mechanical degradation during reduction.

[1]  B. Clausen,et al.  Connecting the macro- and microstrain responses in technical porous ceramics: modeling and experimental validations , 2011 .

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Gunnar E. Carlsson,et al.  Topology and data , 2009 .

[4]  K. Sugiyama,et al.  Crystal Structure of the SFCAM Phase Ca2(Ca,Fe,Mg,Al)6(Fe,Al,Si)6O20 , 2005 .

[5]  Emerson G. Escolar,et al.  Hierarchical structures of amorphous solids characterized by persistent homology , 2015, Proceedings of the National Academy of Sciences.

[6]  E. T. Turkdogan,et al.  Gaseous reduction of iron oxides: Part II. Pore characteristics of iron reduced from hematite in hydrogen , 1971 .

[7]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[8]  M. Pownceby,et al.  Reaction sequences in the formation of silico-ferrites of calcium and aluminum in iron ore sinter , 2004 .

[9]  Yijin Liu,et al.  Mapping Metals Incorporation of a Whole Single Catalyst Particle Using Element Specific X-ray Nanotomography , 2015, Journal of the American Chemical Society.

[10]  A. Pineau,et al.  Failure of metals III: Fracture and fatigue of nanostructured metallic materials , 2016 .

[11]  W. Mumme,et al.  The crystal structure ofSFCA-I, Ca3.18Fe3+14.66Al1.34Fe2+0.82O28, a homologue of theaenigmatite structure type, and new crystal structure refinements ofß-CFF,Ca2.99Fe3+14.30Fe2+0.55O25and Mg-free SFCA, Ca2.45Fe3+9.04Al1.74Fe2+0.16Si0.6O20 , 1998 .

[12]  N. Shimizu,et al.  Newly designed double surface bimorph mirror for BL-15A of the photon factory , 2016 .

[13]  Herbert Edelsbrunner,et al.  Topological Persistence and Simplification , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[14]  Y. Hiraoka,et al.  Pore configuration landscape of granular crystallization , 2017, Nature Communications.

[15]  Henry Adams,et al.  Persistence Images: A Stable Vector Representation of Persistent Homology , 2015, J. Mach. Learn. Res..

[16]  R. Harrington Part II , 2004 .

[17]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[18]  Guo-Wei Wei,et al.  A topological approach for protein classification , 2015, 1510.00953.

[19]  G. Carlsson,et al.  Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival , 2011, Proceedings of the National Academy of Sciences.

[20]  H. Barshilia,et al.  Evaluation of elasto-plastic properties of ITO film using combined nanoindentation and finite element approach , 2016 .

[21]  Yiying Tong,et al.  Persistent homology for the quantitative prediction of fullerene stability , 2014, J. Comput. Chem..

[22]  H. Nitani,et al.  In situ observation of reduction kinetics and 2D mapping of chemical state for heterogeneous reduction in iron-ore sinters , 2016 .

[23]  Afra Zomorodian,et al.  Computing Persistent Homology , 2004, SCG '04.

[24]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[25]  A Hirata,et al.  Geometric Frustration of Icosahedron in Metallic Glasses , 2013, Science.

[26]  Guo-Wei Wei,et al.  TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions , 2017, PLoS Comput. Biol..

[27]  In situ QXAFS observation of the reduction of Fe2O3 and CaFe2O4 , 2013 .

[28]  Bal Sanghera,et al.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? , 2012, Insights into Imaging.

[29]  Emerson G. Escolar,et al.  Persistent homology and many-body atomic structure for medium-range order in the glass , 2015, Nanotechnology.

[30]  Valerio Pascucci,et al.  Persistence-sensitive simplification functions on 2-manifolds , 2006, SCG '06.

[32]  B. Weckhuysen,et al.  Hard X-ray spectroscopic nano-imaging of hierarchical functional materials at work. , 2013, Chemphyschem : a European journal of chemical physics and physical chemistry.

[33]  M. Hessien,et al.  Sintering and heating reduction processes of alumina containing iron ore samples , 2008 .

[34]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[35]  Herbert Edelsbrunner,et al.  Computational Topology - an Introduction , 2009 .