XtalOpt Version r12: An open-source evolutionary algorithm for crystal structure prediction

Abstract

Version 12 of XtalOpt, an evolutionary algorithm for crystal structure prediction, is now available for download from the CPC program library or the XtalOpt website, http://xtalopt.github.io. The new version includes: a method for calculating hardness using a machine learning algorithm within AFLOW-ML (Automatic FLOW for Materials Discovery — Machine Learning), the ability to predict hard materials, a generic optimizer (which allows the user to employ many optimizers that were previously not supported), and the ability to generate simulated XRD (X-ray diffraction) patterns. New version program summaryProgram Title:XtalOptProgram Files doi:http://dx.doi.org/10.17632/jt5pvnnm39.3Licensing provisions: 3-Clause BSD [1]Programming language: C++External routines/libraries:Qt [2], Qwt [3], Avogadro2 [4,5] (optional), libssh [6], Open Babel [7,8] (separate executable), ObjCryst++ [9,10] (separate executable), AFLOW-ML [11,12] (through network), and an external program for optimizing the geometries of extended systems.Subprograms used:pugixml [13], Spglib [14], XtalComp [15], RandSpg [16].Nature of problem: Computationally predicting stable and/or hard crystal structures given only their stoichiometry.Solution method: Evolutionary algorithms (EAs), which use ideas from biological evolution, are optimization algorithms whose goal is to find the optimal solution for a problem that has many degrees of freedom. For a priori crystal structure prediction (CSP), EAs search to find the lattice parameters and atomic coordinates that, for example, minimize the energy/enthalpy or maximize the hardness. The XtalOpt EA for crystal structure prediction is published under the 3-Clause BSD License, which is an open source license that is officially recognized by the Open Source Initiative [17]. More information is available in the following publications: XtalOpt’s original implementation [18], previous version announcements [19–22], manuscripts detailing the subprograms XtalOpt employs: XtalComp [23] and RandSpg [24], and the XtalOpt website [25].Reasons for new version: Since the release of XtalOpt version r11 in January 2018, the following changes have been made: •Added a hardness calculation via AFLOW-ML (Automatic FLOW for Materials Discovery — Machine Learning).•Added a hardness fitness function, which allows for the prediction of hard structures.•Added a generic optimizer, which allows the user to employ many previously unsupported optimizers for minimizing the geometry of an extended system.•Added the ability to generate a simulated XRD (X-ray Diffraction) pattern.•Added the ability to use different optimizers and queuing interfaces for each optimization step.•Implemented various bug fixes.Summary of revisions: The theoretical hardness of a crystal can now be automatically calculated during an XtalOpt run. The hardness is calculated through a linear relationship with the shear modulus (originally discovered by Teter [26]) as reported by Chen [27]. The shear modulus is obtained via AFLOW-ML [11,12], which employs a machine learning model trained with the AFLOW Automatic Elasticity Library (AEL) [28,29]. As a result, the EA can employ a new fitness function, which attempts to minimize the enthalpy and maximize the hardness of the predicted structures. This facilitates the search for crystals that are both stable and hard. Additionally, a new generic optimizer was added that allows the user to employ optimizers that were previously not supported (ADF BAND [30] and ADF DFTB [31] are examples that we have thoroughly tested). The only caveat is that the rules for the generic optimizer, which are provided in the online tutorial, must be followed. Open Babel [7,8] is used to read the output of the generic optimizer. Because of the addition of an executable that uses ObjCryst++ [9,10], a simulated XRD pattern of a crystal can now also be generated during a structure search. Finally, different optimizers and different queuing interfaces can now be used for each optimization step.