While the ongoing search to discover new high-entropy systems is slowly expanding beyond metals, a rational and effective method for predicting “in silico” the solid solution forming ability of ...multi-component systems remains yet to be developed. In this article, we propose a novel high-throughput approach, called “LTVC”, for estimating the transition temperature of a solid solution: ab-initio energies are incorporated into a mean field statistical mechanical model where an order parameter follows the evolution of disorder. The LTVC method is corroborated by Monte Carlo simulations and the results from the current most reliable data for binary, ternary, quaternary and quinary systems (96.6%; 90.7%; 100% and 100%, of correct solid solution predictions, respectively). By scanning through the many thousands of systems available in the AFLOW consortium repository, it is possible to predict a plethora of previously unknown potential quaternary and quinary solid solutions for future experimental validation.
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Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning ...methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules.
Abstract
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his ...discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.
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.
Program Title:XtalOpt
Program Files doi:http://dx.doi.org/10.17632/jt5pvnnm39.3
Licensing 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.
High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the ...discovery of new systems. Here we propose a descriptor-entropy forming ability-for addressing synthesizability from first principles. The formalism, based on the energy distribution spectrum of randomized calculations, captures the accessibility of equally-sampled states near the ground state and quantifies configurational disorder capable of stabilizing high-entropy homogeneous phases. The methodology is applied to disordered refractory 5-metal carbides-promising candidates for high-hardness applications. The descriptor correctly predicts the ease with which compositions can be experimentally synthesized as rock-salt high-entropy homogeneous phases, validating the ansatz, and in some cases, going beyond intuition. Several of these materials exhibit hardness up to 50% higher than rule of mixtures estimations. The entropy descriptor method has the potential to accelerate the search for high-entropy systems by rationally combining first principles with experimental synthesis and characterization.
Twelve different equiatomic five-metal carbides of group IVB, VB, and VIB refractory transition metals are synthesized via high-energy ball milling and spark plasma sintering. Implementation of a ...newly developed ab initio entropy descriptor aids in selection of candidate compositions for synthesis of high entropy and entropy stabilized carbides. Phase formation and composition uniformity are analyzed via XRD, EDS, S/TEM-EDS, and EXAFS. Nine of the twelve candidates form true single-phase materials with the rocksalt (B1) structure when sintered at 2473 K and can therefore be investigated as high entropy carbides (HECs). The composition (V0.2Nb0.2Ta0.2Mo0.2W0.2)C is presented as a likely candidate for further investigation as an entropy stabilized carbide. Seven of the carbides are examined for mechanical properties via nanoindentation. The HECs show significantly enhanced hardness when compared to a rule of mixtures average of the constituent binary carbides and to the highest hardness of the binary constituents. The mechanical properties are correlated to the electronic structure of the solid solutions, offering a future route to tunability of the mechanical properties of carbide ceramics via exploration of a new complex composition space.
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Abstract
Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for ...rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance.
Abstract
High-entropy ceramics are attracting significant interest due to their exceptional chemical stability and physical properties. While configurational entropy descriptors have been ...successfully implemented to predict their formation and even to discover new materials, the contribution of vibrations to their stability has been contentious. This work unravels the issue by computationally integrating disorder parameterization, phonon modeling, and thermodynamic characterization. Three recently synthesized carbides are used as a testbed: (HfNbTaTiV)C, (HfNbTaTiW)C, and (HfNbTaTiZr)C. It is found that vibrational contributions should not be neglected when precursors or decomposition products have different nearest-neighbor environments from the high-entropy carbide.
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