dataesr/scientific-paragraphs-categorization huggingface dataset:
The OpenCalphad (OC) software, discussed here, has a simple programming interface to facilitate such integration in application software. This is important for coupling validated thermodynamic as well as kinetic data in such simulations for obtaining realistic results. At present, no other high quality open source software is available for calculations of multicomponent systems using CALPHAD-type models, and it is the goal of the OC source code to fill this gap. The OC software is distributed under a GNU license. The availability of the source code can greatly benefit scientists in academia as well as in industry in the development of new models and assessment of model parameters from both experimental data and data from first principles calculations.
Computational thermodynamics has been identified as an essential ingredient in ICME and MGI  [3]  since it can provide input parameters to meso-scale methods such as phase field. Integrating thermodynamic calculations in a simulation of a manufacturing process gives information on the local state of the system, including heat evolution, volume changes, and chemical potentials, that may govern a diffusion process or the movement of a solid/liquid interface. In theory, it is possible to learn from a simulation how to control the external variables to obtain a microstructure with the desired properties of the material. Today, much of this is still done by an expensive trial and error method, relying on the skills and experience of the personnel in charge of the processes. The CALPHAD (Calculation of Phase Diagrams) method using thermodynamic and other physical property databases is currently the only method available for efficient calculation of the properties of multicomponent, multiphase systems with the accuracy that is required for commercial applications.
It has been long recognized  [4]  that coupling CALPHAD thermodynamics with other physical models for kinetic simulations is useful for better understanding and improvement of many materials processes. As a result, the ability to couple a thermodynamic code with application software is an essential requirement for the realistic modeling of meso-scale simulations within the ICME framework. For example, Olson  [5]  demonstrated the benefits of this approach for the development of new steels. In this article, we review the current use of the CALPHAD software, the benefits from the availability of an open source software, and the requirements for use within the ICME framework. We further describe in detail the data structure and formalisms that are used in the OpenCalphad (OC) open source software and comment on databases.
Review The CALPHAD method uses mathematical models to describe the properties of a multicomponent system as a function of temperature, composition, and, if needed, pressure. It was originally developed for describing the thermodynamics of a system for the calculation of phase equilibria but has been extended to calculations of all kinds of equilibria and other thermochemical properties. Experimental data, as well as data from atomistic methods  [6] , are used for determining the model parameters. The strength of the CALPHAD method is that it provides a consistent and transparent functional description of the properties of the unary, binary, and ternary subsystems that can be combined for the calculation of a multicomponent system. As such, software and databases for CALPHAD calculations have long been utilized by major materials producers for research and development.
Current use of the CALPHAD method The CALPHAD method was originally developed for the calculation of phase equilibria. As the number of available thermodynamic descriptions for unary, binary, and ternary systems grew, these descriptions were combined into databases for multicomponent systems that were suitable for the calculation of commercial alloys. Initially, CALPHAD calculations were used to determine whether a candidate alloy composition would produce the desired phases and avoid unwanted phases at the temperatures of interest and to select a temperature regime for processing  [7]. The combination of results from CALPHAD calculations with models for other important alloy characteristics, such as creep resistance, castability, and cost, allowed computational testing of thousands of candidate alloy compositions and the selection of a few promising candidate alloys for experimental evaluation  [8].
Efficient coupling of the CALPHAD calculations with other software makes a suitable programming interface essential. Several commercial CALPHAD software programs with interactive user interfaces, such as CaTCalc  [14] , FactSage  [15] , MatCalc  [16] , MTDATA  [17] , Pandat  [18] , and Thermo-Calc  [19] , are available for the thermodynamic calculations of alloy systems. Some of these programs have already been coupled by their developers with diffusion and precipitation simulation software  [16, 18, 20] , and most of these programs have at least one kind of software interface for coupling with application software. The idea of linking different length scales through coupling materials science simulation tools is not new  [21, 22]. CALPHAD calculations have been successfully integrated with phase field simulations  [23] , fluid flow simulations  [24] , and casting simulations  [25].
Benefits from open source code Commercial casting software today is coupled with commercial CALPHAD software using a programming interface. Although this path was first paved by coupling with an older open source code  [25] , there are many more areas where the software used for materials development and manufacturing would benefit from coupling with thermodynamic calculations. The same open source code was used in the initial JMatPro software  [12]. The benefits of open source codes are well documented  [27, 28]. Licenses for commercial thermodynamic software are expensive and may not be available for the required platform. Open source software overcomes these problems and enables researchers and software developers to explore the benefits from coupling with thermodynamics at minimal cost.
The approaches used in free software for the calculation of geochemical systems, GEMS  [29]  and MELTS  [30] , are not well suited for the calculation of metallic systems. Other free software, such as Gibbs  [31]  or AMPL-based Gibbs energy minimization  [32] , are either limited to a simple regular solution-type model or have specialized in the mapping of phase diagrams. Although well suited for the calculation of alloy systems, the open source code used by Banerjee et al. [25]  lacks the modularity that is needed for the implementation of new models and new kinds of calculations. To date, no general open source software is available for multicomponent CALPHAD calculations. Open source code will make it possible to obtain a better integration between all methods used within the ICME framework, including automating the utilization of data from other methods, such as those from Density Functional Theory (DFT).
Open source software will also provide a tool for interested researchers to implement new thermodynamic models, test new algorithms, and extend the functionalities of the software. Open source codes are already available for DFT calculations  [33, 34] , phase field simulations  [35, 36] , and finite element analysis of the mechanical response of microstructures  [37]. OC fills the gap that exists between the availability of open source codes for the atomic scale and the microscale. We present here the first version of a multicomponent open source thermodynamic code distributed under a GNU license, the OpenCalphad (OC) software. This software provides the basic functionalities for the calculation of phase equilibria, phase diagrams, and thermophysical quantities such as chemical potentials, entropies, etc.
Interfacing application programs with thermodynamics The data needed from the CALPHAD calculations by application programs are usually phase amounts and compositions, chemical potentials, enthalpies, driving forces, or phase boundary slopes. The application software must be able to set the external conditions for the calculations in a very flexible manner. Transfer of the calculated thermodynamic results to the application software can be made by creating lookup tables for the application software or by direct coupling. The advantage of lookup tables is that their use is frequently faster than direct coupling. However, since lookup tables are generated prior to the execution of the application software, this advantage may come at the expense of accuracy as the conditions during the course of the calculations by the application software may deviate from the original conditions used for the creation of the lookup table.
The programming interfaces of commercial software are black boxes with limited flexibility with respect to hardware and software demands and prescribe which calculations can be performed and which values can be obtained. For the implementation of OC with other application software, it is possible to call the routines of OC directly. Data structure and routines of the General Thermodynamic Package (GTP) are documented in detail, and the documentation is provided together with the source code. Although the direct implementation of the OC routines may be advantageous for reducing execution time of the application software, it can be time consuming to program and debug as special care needs to be taken when directly implementing the routines. The availability of a software interface overcomes this problem.
Application software interface A software interface should consist of a simple set of routines that can select a material from a database; set the external conditions on T, P, and composition; calculate the set of stable phases; and provide the needed property data to the application software. An established software interface for thermodynamic software called the TQ interface  [38, 39] , originally developed for FactSage  [15]  and Thermo-Calc  [19] , has been implemented in OC. This interface provides functions to set the values of external conditions such as T, P, and composition as well as to prescribe the set of stable phases, volume, heat content, etc. of a system and to obtain equilibrium values of phase amounts, phase constitutions, and chemical potentials of all components. The TQ interface can be used to calculate local equilibria, such as ongoing transformations during a phase field simulation, and can thus provide important information on thermodynamic and kinetic driving forces.
With the TQ interface of OC, OC-TQ, such calculations can be executed in parallel, substantially improving speed. The TQ interface is an open definition and can be extended to also provide other properties such as mobilities and elastic constants that are needed for the simulation of a phase transition. Such properties often depend on T as well as the phases and their constitutions and can be modeled in a way similar to the thermodynamic properties and stored in databases together with such data.
Models and model parameters All thermodynamic parameters are stored in OC relative to the phases and depend on the model selected for the phase. These parameters can be read from a file (database) containing the phase descriptions. There can be many different kinds of models, from a simple stoichiometric model with no variation in composition to multisublattice models for complex crystalline phases and liquids with a short range order to a gas phase with more than 100 species. The most frequently used models are variants of the Compound Energy Formalism (CEF)  [43]. This formalism describes the gas phase, regular solutions, intermetallics, and ordered phases and liquids with a short range order. The general equation for describing the Gibbs energy function per mole formula unit of a phase α is G α m = srf G α m + cfg G α m + phy G α m + E G α m (1) The term srf G α m represents the 'surface' of reference for the phase relative to other phases and internal ordering; cfg G α m is the configurational term which assumes ideal mixing of the constituents in each sublattice.
The term phy G α m is used to describe contributions to the Gibbs energy from particular physical phenomena like magnetic transitions, and E G α m is needed to describe deviations in the Gibbs energy relative to the first three terms. The phase superscript is omitted in the following equations unless several phases are involved. For example, a phase modeled with two sublattices and two constituents in each is denoted (A, D) a (B, C) b ( 2 ) where a and b are the stoichiometric ratios. The various terms in Equation 1, using customary CALPHAD notation where the elements or species are separated by ':' if they are on different sublattices and ', ' if they are on the same sublattice, for such a model are srf G m = y (1) A y (2) B • G A:B + y (1) A y (2) C • G A:C + y (1) D y (2) B • G D:B + y (1) D y (2) C • G D:C (3) cfg G m = RT a y (1) A ln y (1) A + y (1) D ln y (1) D + b y (2) B ln y (2) B + y (2) C ln y (2) C (4) E G m = y (1) A y (1) D y (2) B L A,D:B + y (2) C L A,D:C + y (2) B y (2) C y (1) A L A:B,C + y (1) D L D:B,C + y (1) A y (1) D y (2) B y (2) C L A,D:B,C (5) where y (1)  A is the constituent fraction of A in sublattice 1; • G A:B is the Gibbs energy of formation of a 'compound' A a B b , usually called an 'endmember' of the phase; L A,D:B is the interaction parameter between A and D on sublattice 1 when sublattice 2 is filled with B; and R is the molar gas constant.
Start values, the grid minimizer The algorithm described above is an iterative method which depends strongly on the initial configuration of the phases. In Figure  3 , the Gibbs energy curves for two phases are shown. If the start constitutions of the two phases are as marked in Figure  3b , an iterative algorithm will find the common tangent shown in Figure  3c. This is a metastable equilibrium because it lies above the most stable common tangent as shown in Figure  3d. To avoid the problem of not finding the global equilibrium for the given conditions, Chen et al. [48, 49]  developed an approach for finding the stable equilibria and values for the start constitutions of the phases. Subsequently, similar approaches were implemented into other software such as Thermo-Calc  [19]  or CaTCalc  [14].
Databases Thermodynamic descriptions are read by OC from thermodynamic database (TDB) files  [50]. This format has developed into a de facto standard and is supported by most commercial software. However, the OC code will not be able to read proprietary,  encrypted database files. Files with descriptions of individual systems or databases for multicomponents can be found as supplemental material of journal articles or in file repositories  [51]. It should be noted here that uncertainty of results from the CALPHAD calculations depends on the databases used and how reliably the original experimental data used in the development of these databases are reproduced. Currently, no method for uncertainty quantification of the results from a CALPHAD database is available. The reliability of the results is most commonly expressed by plotting experimental data versus calculated results under the same conditions  [52].
Conclusions The benefits from using the results from the CALPHAD calculations for materials design and process development are well established. We have highlighted the many pathways by which the results from the CALPHAD calculations can be utilized within the ICME framework. The properties that are currently implemented by the CALPHAD software and databases are the Gibbs energy, molar volume, diffusion, and precipitation kinetics. However, there are many other physical properties that are phase based, and the approach used by the CALPHAD method is well suited for modeling them  [55]. The description of these properties will be most likely tied to the thermodynamic description of the individual phases and for expanding the CALPHAD code to include these properties; the availability of a source code for the calculation of thermodynamics will greatly benefit the efficiency of the new code.
Since the inception of the CALPHAD method, models for temperature, pressure, and compositions have been evolving to better reflect the underlying physics of the phases. For example, new models for the description of the unaries are being discussed  [56] [57] [58] [59] [60]  and expansion of the existing thermodynamic code may be necessary. Such developments are only possible for those who have access to the source code of commercial software unless open source code is available. In this article, we have presented OC, an open source code for the calculation of phase equilibria and thermodynamic properties. The OC software has currently the general CEF implemented but more models can be added by interested users. A well-established algorithm has been selected for finding the equilibrium. This algorithm can handle different types of conditions, including the specification of phases which should be stable, and has a powerful method for providing start values to ensure that global minimum is obtained.
Current state of OC The development of a free thermodynamic software for academic research is underway, and a first version of the OC software is available  [61]. The present version provides the basic functionalities necessary for multicomponent thermodynamic calculations. Currently, implemented features in the downloadable version are • The standard multicomponent CEF model with multiple sublattices including magnetism, regular solution, and gas. Composition-dependent binary and ternary parameters are also implemented. • Command line user interface with macro facilities. • Interactive inputing, editing and listing of models and model parameters. • Ability to read models and model parameters from a standard, unencrypted, thermodynamic databases. • Ability to set conditions for T, P, amounts or fractions of components, chemical potentials, stable phases.
Desirable future developments beyond open source code It was previously mentioned that no method for uncertainty quantification of the results from the CALPHAD calculations is currently available. The first logical step in this direction is to perform a sensitivity analysis of the model parameters that are being adjusted during the optimization of the thermodynamic description of a binary or ternary system. In addition to uncertainty quantification, it is desirable to develop a set of test cases to ensure that the results are consistent with accepted codes. Although the results from calculations should agree once the convergence criteria are fulfilled, it is not always guaranteed that the true set of equilibrium phases has been found. Although grid minimizers are intended to assure that the true equilibrium is found, the reliability of these results will depend on how the grid was established.
Acknowledgements Funding by the  Interdisciplinary Centre for Advanced Materials Simulation (ICAMS) , which is supported by  ThyssenKrupp AG ,  Bayer MaterialScience AG, Salzgitter Mannesmann Forschung GmbH, Robert Bosch GmbH, Benteler Stahl/Rohr GmbH, Bayer Technology Services GmbH , and the state of North-RhineWestphalia as well as the  European Commission  in the framework of the  European Regional Development Fund (ERDF). One of the developers, BS, is grateful for a  Humboldt senior research award. NIST  does not endorse any commercial products, and use of these products does not imply endorsement by  NIST.
What this suggests is that people are remarkably sensitive to the geographically-informative features within the visual environment. But what are those features? In informal debriefings, our subjects suggested that for most images, a few localized, distinctive elements "immediately gave it away". E. g. for Paris, things like windows with railings, the particular style of balconies, the distinctive doorways, the traditional blue/green/white street signs, etc. were particularly helpful. Finding those features can be di cult though, since every image can contain more than 25, 000 candidate patches, and only a tiny fraction will be truly distinctive. In this work, we want to find such local geo-informative features automatically, directly from a large database of photographs from a particular place, such as a city.
Given a large image database of a given city, our algorithm is able to automatically discover the geographically-informative elements (patch clusters to the right of each photo) that help in capturing its "look and feel". On the left, the emblematic street sign, a balustrade window, and the balcony support are all very indicative of Paris, while on the right, the neoclassical columned entryway sporting a balcony, a Victorian window, and, of course, the cast iron railing are very much features of London. its unique look?"  [14]. Their solution was to "run around Paris for a week like mad tourists, just looking at things, talking about them, and taking lots of pictures" not just of the Ei↵el Tower but of the many stylistic Paris details, such as signs, doors etc. [14] (see photos on pp. 120-121).
PRIOR WORK In the field of architectural history, descriptions of urban and regional architectural styles and their elements are well established. Such local elements and rules for combining them have been used in computer systems for procedural modeling of architecture to generate 3D models of entire cities in an astonishing level of detail, e. g. [12] , or to parse images of facades, e. g. [22]. However, such systems require significant manual e↵ort from an expert to specify the appropriate elements and rules for each architectural style. At the other end of the spectrum, data-driven approaches have been leveraging the huge datasets of geotagged images that have recently become available online. For example, Crandall et al. [2]  use the GPS locations of 35 thousand consumer photos from Flickr to plot photographer-defined frequency maps of cities and countries.
Geo-tagged datasets have also been used for place recognition  [17, 8]  including famous landmarks  [10, 11]. Our work is particularly related to  [17, 8] , where geotags are also used as a supervisory signal to find sets of image features discriminative for a particular place. While these approaches can work very well, their image features typically cannot generalize beyond matching specific buildings imaged from di↵erent viewpoints. Alternatively, global image representations from scene recogni-tion, such as GIST descriptor  [13]  have been used for geolocalization of generic scenes on the global Earth scale  [6, 7]. There, too, reasonable recognition performance has been achieved, but the use of global descriptors makes it hard for a human to interpret why a given image gets assigned to a certain location.
Finally, our paper is related to a line of work on unsupervised object discovery  [16, 20]  (and especially  [15] , who also deal with mining geo-tagged image data). Such methods attempt to explicitly discover features or objects which occur frequently in many images and are also useful as humaninterpretable elements of visual representation. But being unsupervised, these methods are limited to only discovering things that are both very common and highly visually consistent. In contrast, here we propose a discovery method that is weakly constrained by location labels derived from GPS tags, and which is able to mine representative visual elements automatically from a large online image dataset. Not only are the resulting visual elements geographically discriminative (i. e. they occur only in a given locale), but they also typically look meaningful to humans, making them suitable for a variety of geo-data visualization applications.