E-Journal of Advanced Maintenance Vol.9-2 (2017) 38-43
Japan Society of Maintenology

A Solution of Atom Map Data Analysis for Atom Probe Tomography
TrainingDongyue CHEN 1,*, Kenta MURAKAMI2, Kenji NISHIDA3, Zhengcao LI4,*, Naoto
SEKIMURA1
1

The University of Tokyo, Dept. of Nuclear Engineering and Management, JapanThe University of Tokyo, Nuclear Professional School, School of Engineering, Japan
3
Central Research Institute of Electric Power Industry, Japan
4
Tsinghua University, School of Materials Science and Engineering, China
2ABSTRACTAtom probe tomography is a promising tool for future ageing management of nuclear power plants. For a wider
application of atom probe technique in the future, a training program for university students and young
researchers has been proposed, and a practice section has been designed to provide chances to experience thedata analysis of atom probe. However, the available commercial software for the data analysis is expensive andlacks enough flexibility. In this work, a data analysis solution for atom probe with free codes and free software
has been set up. The requirement of atom map visualization has been discussed, and the atom map data has beensuccessfully visualized with the open-source software ParaView. A precipitate searching code has beendeveloped based on the maximum separation method, which is the most popular method currently. The
precipitate searching code consists of three steps: maximum separation step, envelope step and erosion step. The
detailed algorithm of the code has been discussed, and the effects of tuning the precipitate searching parametersin the code have been demonstrated. With this set of tools, the training participants could get a vivid picture of
the advantages and limitations of the atom probe technique.

KEYWORDS

ARTICLE INFORMATION

Atom probe tomography, Training, Precipitate searching, Maximum
separation method

Article history:
Received 7 November 2016
Accepted 27 April 2017

1.

Introduction

Atom probe tomography (APT) is a new analyzing technique good at studying second-phase
precipitation and grain boundary cracking of nuclear reactor components. The three-dimensional atommap provided by APT reaches near-atomic spatial resolution [1], and is especially suitable for thevalidation and calibration of atomistic models in ageing prediction [2]. Despite of its promising futurein ageing management, currently APT is employed in a very limited number of institutes. Only a few
researchers have the chance to benefit from this novel and powerful technique.
For a wider application of APT in the near future, an APT training for university students andyoung researchers has been proposed and designed. The training is not aiming at cultivating APT
experts who can operate APT machine and analyze APT raw data by themselves, but is hoping thesepotential users can get familiar with APT, and can better utilize APT machine or published APT datain any future possible need of APT technique.
A practice section is designed in the APT training, as the traditional way of lecturing inclassrooms may not be enough to convey a vivid impression of the powerful APT technique to theparticipants. In the practice section, participants are supposed to visualize atom maps and toexperience the post analysis of atom maps by themselves, so that they can better understand theadvantages and the limitations of current APT technique. However, the popular commercial software*Corresponding author, E-mail: dychen@safety.n.t.u-tokyo.ac.jp, zcli@tsinghua.edu.cn

ISSN-1883-9894/10 © 2010 – JSM and the authors. All rights reserved.

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D. Y. CHEN, et al./A Solution of Atom Map Data Analysis for Atom Probe Tomography Training

IVAS for APT data analysis is very expensive. In addition, since atom maps contains huge amount ofinformation, the flexibility of data analysis is limited within the framework of the existing commercial
software. Therefore, a solution of APT data analysis with free codes and free software is highlydesired. In this work, based on the requirement of the APT training, attempts are made to build a set offree tools for APT data analysis. As the beginning step, it includes atom map visualization andprecipitate searching.

2.

Atom Map Visualization

Atom map visualization is an essential part of APT data analysis. Speaking of the APT training,participants need to see, rotate and magnify the three-dimensional atom map themselves before fully
understanding the meaning of “near-atomic resolution” and “comparability with atomistic models”.
The atom map raw data can be stored in a typical xyz file. In the xyz file, each line represents
one atom detected, beginning with the element species, followed by its x, y, z coordinates and also
some additional information. There are plenty of free codes and open-source projects available for thevisualization of such xyz files; however, most of them cannot meet the requirement of atom mapvisualization. The major difficulty in atom map visualization is that the number of atoms is huge,
usually in the order from 105 to 107 [3, 4]. Even for the training, in which some smaller atom maps aregoing to be used for simplicity and easy understanding, the atom number should exceed 104 atoms atleast. Visualizing such amount of atoms demands for large computing power, so it is necessary to
trade off what kind of information should be visualized on the screen and what kind of information
could be somehow neglected. In atom maps, the species of elements and the positions of atoms aremost important, while the size of atoms and the possible bonding between atoms are of less priority.
In this way, the free codes designed for chemical applications, such as for the visualization of organic
compounds, are not suitable for our work.
ParaView is an open-source, multi-platform software originally designed for the visualization ofparticle collisions and interactions [5-7]. It supports xyz input file, and was developed to analyze
extremely large datasets. For our visualization of atom maps, the “particles” are the atoms detected byAPT. In ParaView, the atoms can be visualized as dots instead of spheres, and the type of elements
can be distinguished by the color of dots. In this way, much computing power is saved inthree-dimensional visualization. ParaView provides high flexibility in data visualization, and as anopen-source code, further code optimizations are possible as well. Fig. 1 shows an example ofvisualizing a simulated atom map of Fe-Cu binary alloy by ParaView version 5.1. It is around
13×13×13nm in volume and it contains 105 atoms. The blue dots are Fe atoms while the orange ones
are Cu atoms. This atom map is the simplest case of one spherical Cu precipitate at the centersurrounded by Fe matrix, and the precipitate and the matrix do not have a clear interface.

Fig. 1. A simulated ~13×13×13nm atom map of Fe-Cu binary alloy visualized by ParaView 5.1. a) Fe andCu atoms, b) only Cu atoms visualized before precipitate searching.

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D. Y. CHEN, et al./A Solution of Atom Map Data Analysis for Atom Probe Tomography Training

3.

Code for Precipitate Searching

The study of second-phase precipitates in alloys, such as reactor pressure vessel (RPV) steels andstainless steels, is a major topic for APT application in rector ageing management [8]. Some related
APT results have been published in journals and reports in recent years; however, these outputs
heavily rely on the precipitate definition that their authors have selected for precipitate searching [9].
The participants of the training should experience the different outputs brought up by the selection of
precipitate definition themselves, so that they could be careful if they want to use APT technique or
use APT results for modeling in the future.
A code is provided by us to the training participants for precipitate searching in atom maps. The
Cu-enriched precipitates in RPV model alloy steels is chosen as an example for the precipitate
searching practice, not only for its importance in nuclear safety, but also for its simplicity. In thesimplest case, only Fe atoms and Cu atoms are concerned, which is ideal for easy understanding. The
algorithm of the code is based on the maximum separation method [10, 11], which is the mostwidely-accepted method currently. In this code, the important parameters in precipitate searching canbe tuned by the users, namely maximum separation distance dmax, minimum atom number in a
precipitate Nmin, envelope distance L and erosion distance derosion. The type of solute elements forprecipitate searching can also be user-defined. Although the code is developed for searching
Cu-enriched precipitates in RPV steels, with some subtle modifications it can be applied into otheralloys and precipitates.
The brief flowchart of the code algorithm is illustrated in Fig. 2. Three steps are needed tocomplete the precipitate searching. The code has been applied to search precipitates in the simulated
map shown in Fig.1, and the discovered precipitate after each step is shown at the bottom of Fig. 2.

Fig. 2. A brief flowchart of the code algorithm. Below is a typical process of precipitate changing
morphology after each step of precipitate searching by our code (dmax=derosion=L=0.7nm, Nmin=20).

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D. Y. CHEN, et al./A Solution of Atom Map Data Analysis for Atom Probe Tomography Training

3.1

Maximum Separation Step

At beginning, solute elements and non-solute elements can be defined by users. In the case of
Cu-enriched precipitates in RPV steels, solute elements are usually Cu or the combination of Cu, Mn,
Ni and Si.
The core idea of the maximum separation method is that when two solute atoms are within acertain distance, they are included into one precipitate. This distance is named as maximum separation
distance dmax. dmax is the most important parameter in this algorithm. Larger the dmax, looser the criteria
for precipitate searching, and higher the precipitate number density and larger the precipitate size willbe.
Only solute atoms are involved in this step, because solute atoms are used to distinguish whether
a precipitate exists or not while non-solute atoms only affect precipitate size and composition. First,the distances between any two solute atoms are calculated. When two solute atoms are found to bewithin the distance of dmax, they are regarded as one precipitate, say precipitate A. Immediately atree-searching algorithm is triggered to exhaust all the solute atoms in precipitate A, and thediscovered solute atoms are marked as a part of precipitate A. The loop continues to check othersolute atoms for precipitates B, C, D, etc. until all the precipitates in the atom map are found.
However, with such kind of searching algorithm, some nominal “precipitates” can be found evenin atom maps without any actual precipitates. These nominal precipitates are tiny in size, usuallycontaining only several solute atoms, and should be attributed to the coincidental or random
distribution of solute atoms in alloys. A screening method is available by defining the minimum solute
atom number in a precipitate, Nmin. Therefore, after the loop of distance check finishes, the discoveredprecipitates are screened by Nmin, and the tiny precipitates with solute atom number less than Nmin aremarked as the matrix.
3.2

Envelope Step

Since only solute atoms are considered in the maximum separation step, the envelope step andthe erosion step are both for non-solute atoms. An envelope distance L (0.5dmax ≤ L ≤ dmax) is definedin the envelope step. The precipitates found in section 3.1 have only solute atoms, and the distances
between non-solute atoms and these solute atoms in precipitates are calculated. If a non-solute atom is
within the distance L of any solute atoms in one precipitate, then it is included into the precipitate.
In our code, if a non-solute atom could be included into multiple precipitates by the criteria of L,then it is attributed to its nearest precipitate.
3.3Erosion Step

An erosion distance derosion is defined in the erosion step, and derosion ≤ L. For a given precipitate A,
all the solute and non-solute atoms not belonging to this precipitate are selected as the reference atoms
for erosion. The atoms in precipitate A which are within the derosion distance with any reference atom isexcluded from the precipitate.
Large computation time may be needed in the erosion step. To improve the performance of thecode, some methods have been used to reduce the amount of computational work. For example, since
the distances between non-solute atoms and the solute atoms in precipitates have already been
calculated in the envelope step, the non-solute atoms having distances larger than L + derosion with any
solute atoms in any precipitates have been marked and are not going to be the reference atoms forerosion.
Similar to the envelope step, when multiple precipitates are very close to each other, the situation
would be complicated. In these situations, any subtle differences in the details of erosion treatment
could lead to some minor differences in the boundary of the neighboring precipitates. In the envelope
step of our code, when a non-solute atom could be included into multiple precipitates by the criteria ofL, say precipitate A and B, it is attributed to its nearest precipitate, say precipitate A. Then in erosion,
this atom belonging to precipitate A is not used as the reference atom for the erosion of precipitate B,
as it is still within the envelope range L of precipitate B.

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D. Y. CHEN, et al./A Solution of Atom Map Data Analysis for Atom Probe Tomography Training

Fig. 3. The influence of precipitate definition on resultant precipitate size and composition. Cu-enriched
precipitates are searched by our code in the simulated atom map of Fe-Cu binary alloy. Nmin=20.

Our precipitate searching code has been applied in the simulated atom map of Fe-Cu binary alloy
shown in Fig. 1, and the outputs are shown in Fig. 3. Nmin was selected to be 20, and L and derosion were
set to be the same value as dmax. The results give a vivid picture of how precipitate size (in total atomnumber) decreases and how precipitate composition changes as dmax decreases. If dmax furtherdecreases, which means a stricter criteria for precipitate searching, the single precipitate will bedistinguished as two small precipitates. The participants of the training can also use this code for theanalysis of other atom maps with more precipitates.
Our atom map visualization and precipitate searching solution does not use any commercial
software, and can be deployed on Windows, Linux and MacOS. When treating atom map of 105 atoms,
even a typical notebook in recent years could visualize atom maps without lag and could finish
precipitate searching within several minutes. The training participates could do their homework on
their own computer at home. They can treat the code as a black box by only inputting searchingparameters, or they can customize the algorithm by modifying the source code.

4.

Conclusions

For a wider application of APT technique in the future, an APT training program for potential
APT users is proposed, and a practice section is designed to provide chances of experiencing APT
data analysis. However, the available commercial software for APT analysis is expensive and lacks
enough flexibility. In this work, a solution of APT data analysis with free codes and free software has
been set up. The requirement of atom map visualization has been discussed, and an example of using
ParaView to visualize atom map has been shown. A precipitate searching code has been also
developed based on the maximum separation method. The detailed algorithm of the code has been
discussed, and the effects of tuning the precipitate searching parameters in the code have been
demonstrated. With this set of tools, the training participants could get a vivid picture of theadvantages and limitations of the APT technique.

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