Analytics Toolkit - Interim Deployment

Transcription

1 The NOMAD (Novel Materials Discovery) Laboratory a European Centre of Excellence Analytics Toolkit - Interim Deployment Deliverable No: 4.2 Lead Beneficiary: Max Planck Society (MPG) Contributing Beneficiaries: King s College London (KCL), Humboldt-Universitaet zu Berlin (HUB), University of Cambrisge (CAM), Universitat de Barcelona (UB), Aalto University (AALTO), Danmarks Tekniske Universitet (DTU), CSC - IT Center for Science (CSC), Pintail Ltd. (PT)

2 Executive Summary The NOMAD Analytics Toolkit is a web-based set of tools for performing state-of-the-art and beyond Big-Data analytics of materials science data. The concepts and first five tools were described in D4.1 Analytics Toolkit - initial deployment. Subsequent work has focused on expanding and advancing the functionality of the Toolkit. Specifically, the NOMAD Analytics Toolkit now features five new analytic tools and related graphicaluser-interface (GUI) tutorials. Furthermore, the functionality and user-friendliness have been considerably improved. The Toolkit now supports registered users in addition to anonymous users, where registered users are provided with a private workspace for storing their results. We also implemented and deployed an application programming interface (API) that applies NOMAD Metainfo (i.e. metadata structure defined in D1.1 Data formats and compression ) for accessing all data in the NOMAD Archive. The Toolkit is publicly accessible at Copyright 2016/2017 by the NOMAD Consortium. The information in this document is proprietary to the NOMAD Consortium. This document contains preliminary information and is not subject to any license agreement or any other agreement with the NOMAD Consortium. This document contains only intended strategies, developments, and functionalities and is not intended to be binding upon to any particular course of business, product strategy, and/or development of the NOMAD Consortium. The NOMAD Consortium assumes no responsibility for errors or omissions in this document. Furthermore, the NOMADConsortium does not warrant the accuracy or completeness of the information, text, graphics, links, or other items contained within this material. This document is provided without a warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. The NOMAD Consortium shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials. This limitation shall not apply in cases of intent or gross negligence. The statutory liability for personal injury and defective products is not affected. In addition, the materials presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out. * The acronym has been changed from NoMaD to NOMAD - NoMaD is used here in reference to the acronym used in the Grant Agreement. D4.2 Analytics Toolkit - Interim Deployment

3 Analytics Toolkit - Initial Deployment 1 Introduction Results Overview of Toolkit Predicting the energy difference between different crystal structures Analyzing and estimating error bars from high-accuracy references Complexity estimator for accurate-forces learning Ground state of binary alloys Accessing and querying the Archive Resolve API for accessing the data in the Archive Query Atomic Collections User management On-going work Conclusion Revision History Version 1.0, submitted 19/05/2017 Original version D4.2 Analytics Toolkit - Interim Deployment 3

4 1 Introduction The general goal of work package 4 (WP4) Big-Data Analytics is to build the NOMAD Analytics Toolkit. The Toolkit is a software framework (i.e. an extensible and unified library of subroutines and functions) together with Application Programming Interfaces (APIs). At the same time, the Toolkit is a web-server implementation for: (i) querying the data contained in the NOMAD Archive, and (ii) performing analyses on the retrieved data, employing algorithms that are state-of-the-art and beyond. Access to the NOMAD Analytics Toolkit is offered to users through tools ( Apps ) that can be applied off the shelf, or through low level Application Programming Interfaces (APIs). WP4 also offers tutorials to acquaint inexperienced users with very different approaches for (Big-) data analytics and data mining. These tutorials are provided via graphical user interfaces (GUIs). D4.2 describes the development and implementation of several traditional machine-learning algorithms, which was already partially achieved in the initial deployment of the Toolkit (D4.1). Here, we have extended the set of implemented tools from five (D4.1) to ten (section 2.1) and implemented other important functionalities, including: development and implementation of an infrastructure for managing atomic-species properties, required for most of the analytics tools, implementation of user management for Toolkit users, where registered users are provided witha private workspace for storing results, and coding of an API for accessing the complete NOMAD Archive. Furthermore, a first implementation of distributed and efficient queries has been achieved and is currently under debugging. It will be available and announced at by the end of M19. 2 Results 2.1 Overview of Toolkit Users access the Toolkit from: The NOMAD Analytics Toolkit infrastructure is deployed and running on a server maintained at MPG-MPCDF. Table 1 lists the currently implemented tools (with links to the related tutorial), with explicit references to the machine-learning and other data-mining methods that have been developed in this work and are made available. D4.2 Analytics Toolkit - Interim Deployment 4

5 Table 1 Tools and Tutorials currently available in the NOMAD Analytics Toolkit Method Title of the tool/tutorial Link (footnote #) Compressed sensing Predicting energy differences between crystal structures, advanced methodology 1 3 original implementation 2 4 Subgroup discovery Kernel ridge regression Linear and non-linear dimensionality reduction Discovering simple descriptors for crystal-structure classification Evaluating the (dis)similarity of crystalline, disordered, and molecular compounds Predictions of ab initio properties of molecules and clusters Building structure maps for crystal-structure classification Evaluating the (dis)similarity of crystalline, disordered, and molecular compounds Gaussian process Complexity estimator for accurate-forces learning 9 Cluster expansion Describing the ground-state of binary alloys 10 The notebook tutorial 11 themselves are already interactive beyond the visible GUI. Once users open a tutorial in a browser's window, they can go to the menu Notebook (top of the page, right below the Beaker logo) and then clicking Lock. This unlocks the notebook, i.e., it reveals the underlying code, grouped in cells (editable fields in a notebook). Of particular interests should be the Ipython 12 cells that show how the libraries needed for analysis are imported and how 1 Here, we introduce a more powerful compressed-sensing algorithm, compared to the original formulation presented in PRL 114, (2015) and NJP 19, (2017), developed at MPG-FHI during the last months (section 2.1.1). 2 Two tutorials are available under the same link, one for the crystal-structure prediction of octet binaries and one for the discovery of the structure-property relationship of metal nanoclusters The actual analytic-tool part (as opposed to the graphical part, written in, e.g., html or javascript) does not need to be written in python. For example, R, a widely used language for statistical analysis and learning, is supported by the beaker notebook as well. However, presently, all our examples are in python A notebook is an interactive web page which allows users to write and execute code on-the-fly D4.2 Analytics Toolkit - Interim Deployment 5

6 the functions are used. The back-end (i.e. the actual python functions) is not yet openly accessible, but it will be in the near future. In order to help users and to build a user community, we also established a forum ( directly reachable from each tutorial. Through it, users can give feedback to the NOMAD team, including bug reports and suggestions for improvement. Following users suggestions, from within and outside NOMAD, we have implemented several improvements to the tools and tutorials since M12, including a detailed description for the viewer that displays the graphical results of four of the tutorials, 13 the adoption of a color-deficiency-friendly color map for the same viewer, bootstrap tours 14 for the more complex tutorials 15 and the possibility to choose the units for displaying the results. 16 In the following sections, we briefly describe some of the newly implemented tools and tutorials Predicting the energy difference between different crystal structures This tutorial presents an updated and more powerful version of the compressed-sensing algorithm for the identification of descriptors and models for the crystal-structure prediction previously delivered in D4.1. Whereas the analytic formula was previously selected from up to a few thousands of options (for more the procedure became unstable), the number of options can now be millions. Billions of options are possible, but on a larger number of CPUs than those available for public tutorial users. From an implementation point of view, this tutorial supports parallel computing by means of the standard Message Passing Interface (MPI) library. By unlocking the notebook, the user can understand how this can be done within the framework of a notebook, for other applications Analyzing and estimating error bars from high-accuracy references This tutorial estimates the error of the total and formation energy of density-functional theory calculations. Specifically, the present version of the tutorial addresses errors that arise in different types of codes by the underlying methodology and settings. This is an important tool for identifying the numerical settings that should be used in a calculation in order to achieve the desired accuracy. For tutorial purposes, a thorough validation of the adopted estimator is provided, so that the user can, for instance, observe for which chemical elements over the periodic table the convergence of the energy is slower / faster with respect to the numerical settings. The tool is based on a database of about 100,000 calculations for four important electronic-structures codes. The data production protocol is available and other code-developers can contribute in order to add their code to the list of those available for the prediction A javascript library for producing popup tabs with explanations/instructions that react dynamically to the action made by the users in a webpage e.g. D4.2 Analytics Toolkit - Interim Deployment 6

7 2.1.3 Complexity estimator for accurate-forces learning This tutorial makes use of Gaussian process (GP) regression in order to assess the complexity of a given system. Complexity can be defined as the data-set size that is necessary for the GP to predict a target property (e.g. atomic forces, total energy of a configuration). In the showcase example, the reference data are the atomic forces for the mono-crystalline silicon system, at different temperatures, calculated with the 3-body Stillinger-Weber potential. By running the tutorial, the prediction error of the atomic forces learned by the GP model is shown against the size of the training set for the silicon system, as a function of temperature. By calculating the prediction error at a different size of the training data, the user can quantify how big the set of training data should be in order to contain sufficient information to enable predictions on the system and what accuracy can be achieved. The second part of the tutorial shows the pairwise potential estimated by the trained GP model as a function of the interatomic distance and its accuracy. This allows the user to account for extrapolation errors coming from the use of the fitted potential for the description of a system more complex (e.g. at higher temperature) than that used for training the model Ground state of binary alloys This tutorial demonstrates learning of the formation energy of crystalline alloys, by making use of the cluster-expansion technique. The latter provides a means to build an accurate model Hamiltonian, by expanding the energy of a certain arrangement of atomic species in a given lattice in terms of clusters (i.e. sets of crystal sites). Based on a set of calculations of total energies using density-functional theory, effective interactions are assigned to each cluster, hence allowing users to obtain the energy of formation of any configuration of the alloy in a numerically efficient way. In this tutorial, the user can perform an extensive search of stable configurations of a binary alloy (i.e. those yielding stable materials with respect to phase separation). The selection of relevant clusters can be done in several ways, including compressed sensing (in particular, the Least Absolute Shrinkage and Selection Operator, LASSO). 2.2 Accessing and querying the Archive In order to perform queries for the filtered retrieval of the data from the Archive, and therefore allow for fully customized analysis, two aspects were required. Firstly, an API was needed for accessing the files in the Archive and reading the content of the binary HDF5 17 files, when needed. Secondly, a query language and interpreter was needed in order to translate the queries formulated by the user into efficient (scalable) searches over the Archive. In the following sections, we briefly explain how both aspects were implemented. The query will be publicly available at the end of M Resolve API for accessing the data in the Archive To a user, the Archive can be seen as a file system at The subfolders with the parsed and normalized data contain HDF5 files organized according to our metadata structure (D1.1), each containing one or more calculations (i.e. the content of one section_run 17 The data in the archive are stored as human-readable files in JSON format and in binary files designed for the efficient and scalable reading/writing access, in the HDF5 format, see D1.1. D4.2 Analytics Toolkit - Interim Deployment 7

8 after the parsing). This is a convenient way for providing accessing to all data in NOMAD, something that is critical for offering open access to big materials data. However, most users and web applications will need a more granular access to a subset of the parsed and normalized data. As this data is organized in a hierarchical way according to NOMAD metadata, data can be uniquely referred to using the newly developed NOMAD Resolve URI. The NOMAD Resolve URI is implemented as a REpresentational State Transfer (REST) API for the efficient browsing of the parsed and normalized data. In practice, a URL can be constructed to access all metadata in the archive, via any web-browser. This is done by concatenating the string: with a NOMAD Resolve URI, consisting of the unique identifier (UID) of one raw-data zip-archive, followed by a slash (/) the UID of one calculation contained therein, followed by /section_run/ and so forth for the sections (metadata of type section ) contained in the section_run. For example, the following URL accesses the energy_total (metadata for the total energy for a given configuration of atoms and a given physical model) of one specific energy evaluation: where the identifier 0c means the first item in a list, e.g., the first section_single_configuration_calculation of possibly many, etc. The second item in the list would be identified by 1c, and so on. By appending at any level of this hierarchical URL the method?format=recursivejson, a useful visualization that allows the recursive access to lower levels of the hierarchy is displayed. For instance, the URL: DWGvyqvK2g_1yLyJf8nN3j_Mxd/section_run/0c/section_single_configuration_calculation/0c/energy_total/0c DWGvyqvK2g_1yLyJf8nN3j_M-xd?format=recursiveJson displays the content of a specific calculation, which is performed with the code VASP (by clicking on program_name ). It is: a geometry optimization ( section_sampling_method ), performed with the PBE exchange-correlation functional (section_method followed by section_xc_functionals), and it contains a sequence of configurations with an energy evaluation for each. At each level, one can read the field uri that, copy/pasted in the address line of the browser, would allow for direct access to that specific section content or concrete metadata value. In practice, the user does not have to know the rather cumbersome unique identifiers of archives and calculations, as the location of certain values will be the result of a query. However, the result of a query is mainly a list of identifiers of calculations and the implemented REST API allows for the direct access of all the searched information. A full documentation of the REST API will be presented in a forthcoming (open-access) publication Query The NOMAD Resolve URI provides access to the Archive data in a way that mirrors the structure defined by the NOMAD metadata. To query this data, we have also developed a simple query language that reflects the metadata structure. This approach makes querying simple and safe (as D4.2 Analytics Toolkit - Interim Deployment 8

9 anonymous users might use it), and also hides the actual query details so that the technology used to perform the query can be changed. Conceptually, the query scans over all section_single_configuration_calculation and filters out the sections that satisfy all the filters that are specified. Every filter term relates to NOMAD metadata, and can specify if data corresponding to that term should be present or absent, or (if the term is a scalar) compare it to values provided. Instead of looping on all section_single_configuration_calculation, and filtering using all the metadata connected to it (even indirectly, through the section_system or section_method), it is possible to loop on other sections setting the context to them. The result of the query are the identifiers (NOMAD Resolve URI) of the items that satisfy all filters. We plan to also allow the immediate extraction of connected values. This functionality is currently under debugging. By the end of M19, there will be a tutorial 18 to demonstrate how to perform basic and more complex queries of the Archive. 2.3 Atomic Collections Many compressed-sensing, machine-learning and data-mining methods applied to materials science employ input data in terms of properties of free atoms (e.g. atomic number, electronegativity, etc.). It is therefore useful to have access to a library of such atomic data, organized by reference (e.g. publication) or author, and details on the method of measurement/calculation (experimental or theoretical). In addition, it is also useful for the user to be able to upload new atomic-species data. For these reasons, we have developed an infrastructure to store and access atomic-data collections. An atomic-data collection is a data set of free-atom properties, supplemented by metadata attributes about where the data come from, how they were generated or measured, and what numerical settings have been used in the calculations. In order to organize the metadata for the atomic properties, we use the same scheme as the general NOMAD metadata structure (D1.1). In Figure 1, we show the main features of the atomic-collection metadata structure. We define a section_atomic_data_collection, which contains a: section_atomic_property_method with information on the methodology used for measuring or calculating atomic-species properties contained in the collection, as well as a reference DOI (e.g., a published paper or NOMAD Repository DOI for a dataset) to allow atomic-species data to be traced back to their source, and section_atomic_property with a list of properties for atomic species in the periodic table (not necessarily all, i.e., some atomic species can be absent form the list), where the metadata atomic_property_to_method_ref links each specific property for each atom to one of the methods listed in section_atomic_property_method (i..e. the collection can contain data from heterogeneous sources) D4.2 Analytics Toolkit - Interim Deployment 9

10 Figure 1 Metadata structure for the metadata atomic_electron_affinity. The blue arrows signifies is contained in and the red arrow establishes a reference to another section. More detailed documentation can be found at: In addition, as for all NOMAD MetaInfo, the atomic-collection metadata can be browsed at: (e.g. by first searching the root atomic-collection metadata, section_atomic_data_collection and then browsing following its children). Figure 1 shows, as an example, the metadata structure for the metadata: atomic_electron_affinity. Full documentation on the metadata-structure representation is at: The atomic collections are uploaded to the NOMAD Repository in the form of tables (in the Comma Separated Values, CSV, format), where each row is an atomic property for one atomic-species and each column is one metadata (the header must be the metadata name). An.nfo file is also expected together with the table, providing a description of the collection. All the details about the expected format are in: A parser developed by NOMAD maps the table and.nfo file into the metadata structure and makes the collection available to the Toolkit. 19 Notably, the user can flexibly define the units of measurement for expressing the properties that are converted in the SI format, as for the input/outputs of the parsed codes. The usage of the parsed atomic collection is documented at: This infrastructre is already adopted in some of the tools already completed, such as In order to demonstrate the functionalities of this infrastructure, we have created a tutorial for the visualization of a given atomic property and a given collection, by hovering above a familiar-looking periodic table: The user can choose among some already loaded atomic data collections, further filter the selection and read the values of the selected atomic property over the periodic table. If no atomic property is selected, all available atomic properties for the selected collection are displayed by hovering. 2.4 User management Users log in via: (button present in the menu bar of or by accessing any tutorial at: 19 Presently, the user interested in having an atomic-data collection uploaded into the Archive, should contact the NOMAD Analytics Toolkit team via the forum. In the near future, the procedure will be automated. Note that uploaded atomic-data collections will be treated as any file uploaded onto the NOMAD Repository (i.e. it cannot be deleted). D4.2 Analytics Toolkit - Interim Deployment 10

11 toolkit.nomad-coe.eu/. To become a registered user, the user is only required to provide a working address. The registered account is activated by following a link ed to the given address. This single sign-on functionality is currently available for the Toolkit but will be used for access to all NOMAD tools, such as the Encyclopedia and Repository, in the near future. Hence, a unified access to the whole NOMAD Laboratory CoE will be established. From a technical point of view, the Single Sign-On (SSO) functionality for the NOMAD Laboratory CoE is enabled using the Security Assertion Markup Language (SAML) standard. The SAML-based SSO typically contains three roles: the user, the Identity provider (IdP), and the service provider (SP). The general workflow involves a user requesting a service from the SP. The SP requests the IdP to identify the user. The IdP then authenticates the user and delivers an identity assertion to the SP. On the basis of this assertion, the SP can decide whether to allow the user to access the service. To authenticate users and to supply user information to different services in the NOMAD Laboratory CoE, a Shibboleth 20 identity provider is deployed. All services that are compliant with the SAML SP role can be integrated with the NOMAD IdP. The integration typically involves registering metadata of the service providers to the IdP. In case of the Analytics Toolkit, the SAML SP role is implemented using Passport-SAML ( library and is already integrated with the IdP. User registration with NOMAD IdP is implemented using the open-source password selfservice application PWM ( The user data is stored in a Lightweight Directory Access Protocol (LDAP) directory and is used by the IdP to validate the usersupplied information. Moreover, to allow integration with social accounts and other federations, MPASS ( components will be deployed into the Shibboleth IdP. MPASS is an open-source software developed at CSC and contains modules to integrate the SAML-based authentication sources to the NOMAD IdP. In this first deployment of the registered-user functionalities, users can access a private dashboard (Figure 2) with the main purpose of allowing users to access saved notebooks, saved by the users themselves or saved by others and shared with them. Registered users can work on a newly opened notebook or modify an existing one (including current and future tutorials provided by the NOMAD team) and can save them either in their private space or in their public space if they want to share them with others D4.2 Analytics Toolkit - Interim Deployment 11

12 Figure 2 Example view of the user dashboard 2.5 On-going work We are continuing to polish and debug the query functionality of the Toolkit. Presently, an important limitation to the interactivity of the implemented tools and tutorials is that the user cannot freely choose a dataset for the analysis, but can only use the dataset(s) provided for the specific tutorial. This limitation will be overcome by M24, by making use of the query infrastructure. The tutorials will then be revised in order to guide the user on how to select a desired dataset from the NOMAD Archive. The results of queries (essentially lists of URIs for accessing the queried metadata) will be managed via relational databases that each user will be able to create and share with other users. This allows, for example, for repeating a query weeks/months later in search of updates as the Archive is expected to grow steadily, while still taking into account older results that were previously found. 3 Conclusion We have presented the interim deployment of the NOMAD Analytics Toolkit. In line with the project plan, we have implemented new, important machine-learning, compressed-sensing and data-mining tools and tutorials, and improved existing tools, following users comments and suggestions. Furthermore, we have developed and implemented other general functionalities for a more structured and unified user access, namely: registered-user management for the Toolkit, with basic functionalities (enabling access to saved notebooks) as a first step towards a single access point for the entire NOMAD Laboratory CoE, infrastructure for uploading and retrieving data for atomic-species, a fundamental functionality for many data-analytics tools, D4.2 Analytics Toolkit - Interim Deployment 12

13 a (REST) API for accessing all data in the Archive, and a first implementation of the query tool. Importantly, the API and query tool allow full and efficient access to the data contained in the Archive, enabling users to perform analyses on the data. D4.2 Analytics Toolkit - Interim Deployment 13