Representations of Materials for Machine Learning

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by a machine learning model. Datasets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and property of interests. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs of machine learning models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus, require further investigation.Comment: 20 pages, 5 figures, To Appear in Annual Review of Materials Research 5

Multi-principal element alloys: Design, properties and heuristic explorations

Computational investigations of structural, chemical, and deformation behavior in high entropy alloys (HEAs), which possess notable mechanical strength, have been limited due to the absence of applicable force fields. First by employing Lennard-Jones (LJ) type potential, we explore the atomic origins of the structural phase transformations (PTs) in AlxCrCoFeNi multi-principal element alloys (MPEAs) using classical molecular dynamics (MD) simulations. We report that the amorphous phase exists above the melting temperatures of the participating elements when the concentration of Al 20 %, while for Al \u3e 20 %, a gradual molten-amorphous phase transition is noted. To extend investigations for mechanical properties, we propose a set of intermolecular potential parameters for a quinary Al-Cr-Co-Fe-Ni alloy, using the available ternary Embedded Atom Method and Lennard-Jones potential in classical molecular-dynamics simulations. The simulation results are validated by a comparison to first-principles Korringa-Kohn-Rostoker (KKR) - Coherent Potential Approximation (CPA) [KKR-CPA] calculations for the HEA structural properties (lattice constants and bulk moduli), relative stability, pair probabilities and high-temperature short-range ordering. The simulation (MD)-derived properties are in quantitative agreement with KKR-CPA calculations (first-principles) and experiments. We study AlxCrCoFeNi for Al ranging from 0 $leq$ x $leq$ 2 mole fraction, and and that the HEA shows large chemical clustering over a wide temperature range for x \u3c 0.5. At various temperatures high-strain compression promotes atomistic rearrangements in Al0:1CrCoFeNi, resulting in a clustering-to-ordering transition that is absent for tensile loading. Large fluctuations under stress, at higher temperatures, are attributed to the thermo-plastic instability in Al0:1CrCoFeNi. With the proposed EAM-LJ potential parameters, we then analyze the dislocation dynamics in the FCC Al0:1CrCoFeNi HEA. During plastic deformation, we find that dislocation nucleation and mobility plays a pivotal role in initially triggering twin boundaries followed by the generation of intrinsic and extrinsic stacking faults in the alloy. At room temperature, we find dislocation annihilation contributes to the shear resistance of the alloy eecting a serration laden plastic ow of stress as uniaxial strain is increased. Designing advanced materials for high-temperature applications is a challenging problem. Traditionally superalloys, especially Ni-based, have been the to-go materials whenever strength, and oxidation/corrosion resistance is required in applications ranging across gas turbines to engines. Refractory elements have a high melting point and are ideal candidates to design refractory based MPEAs. We utilized classical Hume-Rothery rules, like Valence Electron Concentration (VEC), size-effect , alongside density functional theory predicted global stability parameter like formation enthalpy and thermodynamic linear response predicted local stability criteria like short-range order (SRO) to explore the 5D design space in a holistic manner for a quinary refractory MPEA. Our investigation revealed the specific design regimes ideal for enhanced electronic stability alongside desired mechanical strength for a novel refractory alloy series based on Mo-W-Ta-Ti-Zr. Findings suggest that the elastic strength of predicted alloy, composition having greater content of Mo and W (at. %), surpasses current commercial high-temperature alloy (Ti-Zr-Mo). Transport properties of refractory MPEAs are also investigated as a potential new class of high-performance thermoelectric material. Investigations in XTa-MoW MPEAs (X=Ti, V, Nb, Zr) revealed dispersion effects and critical doping concentration that helps in tuning the figure-of-merit (ZT) by a factor of 6 at 1250 K. Further investigations in refractory MPEAs revealed twinning-induced pseudoelasticity in (MoW)0:85(TaTi)7:5Zr7:5. Atomistic insights through structural analysis and role of temperature was carried out in this work. Materials for actuators and bio-medical stents are some of the possible application areas for the predicted pseudoelastic MPEA. We design a robust computational framework that couples the metaheuristic cuckoo search technique with classical molecular dynamics simulations to explore the structure-composition phase space of multicomponent alloys. Thereby the predictive scheme explores a vast materials landscape and accelerates the elemental selection for discovery of novel multicomponent alloys. Structural design of MPEAs is generally based on brute-force Monte-Carlo (MC) techniques which are often computationally demanding and less reliable. We proposed a novel exploratory technique to numerically design initial lattice structures for MPEAs for quantum or atomistic calculations that maintains desired cubic symmetry, at required compositions at a fraction of the computational requirements of current algorithms/frameworks. Structural design of BCC alloy systems from binary to quinay were successfully performed and verified with two different density functional approaches

Nanoinformatics

Machine learning; Big data; Atomic resolution characterization; First-principles calculations; Nanomaterials synthesi

Nanoinformatics

Machine learning; Big data; Atomic resolution characterization; First-principles calculations; Nanomaterials synthesi

Deep Learning Based Generative Materials Design

Discovery of novel functional materials is playing an increasingly important role in many key industries such as lithium batteries for electric vehicles and cell phones. However experimental tinkering of existing materials or Density Functional Theory (DFT) based screening of known crystal structures, two of the major current materials design approaches, are both severely constrained by the limited scale (around 250,000 in ICSD database) and diversity of existing materials and the lack of a sufficient number of materials with annotated properties. How to generate a large number of physically feasible, stable, and synthesizable crystal materials and build accurate property prediction models for screening are the two major unsolved challenges in modern materials science. This dissertation is focused on addressing these two fundamental tasks in material science using deep learning/machine learning models. Deep learning and machine learning have already made tremendous progress in computer vision and natural language processing, as shown by autonomous driving cars and Google’s translators, and have the potential to greatly transform the research of materials science. Compared to conventional tinkering based materials discovery methods, data-driven approaches have been increasingly used in material informatics due to their significantly faster screening speeds for new materials. In this dissertation, we design and develop novel deep learning-based algorithms to learn the hidden intricate chemical rules that assemble atoms into stable crystal structures from known crystals and to generate new crystal structures . We also explore and develop novel representation learning methods upon materials compositions and structures for high performance prediction of materials structural characteristics and elastic properties. In the first topic, we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large-scale generative design of novel cubic materials. When trained on 375 749 ternary materials from the OQMD database, we show that the model can not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such materials have been verified by DFT based phonon dispersion calculation. Our technique allows to generate tens of thousands of new materials given sufficient computing resources. In the second topic, we propose a Physics Guided Crystal Generative Model (PGCGM) for new materials generation, which significantly expands the structural scope of CubicGAN by bringing the capability of generating crystals of 20 space groups. This is achieved by capturing and exploiting the pairwise atomic distance constraints among neighbor atoms, symmetric geometric constraints, and a novel data augmentation strategy using the base atom sites of materials. With atom clustering and merging on generated crystal structures, our method increases the generator’s validity 8 times when compared to one of the baselines and by 143% compared to the previous CubicGAN, along with its superiority in properties distribution and diversity. We further validated our generated candidates by DFT calculations, which successfully optimized/relaxed 1869 materials out of 2000 generated ones, of which 39.6% had negative formation energy, indicating their stability. In the third topic, we propose and evaluate machine-learning algorithms for determining the structure type of materials, given only their compositions. We couple random forest (RF) and multiple-layer perceptron (MLP) neural network models with three types of features: Magpie, atom vectors, and one-hot encoding (atom frequency) for the crystal system and space group prediction of materials. Four types of models for predicting crystal systems and space groups are proposed, trained, and evaluated including one-versus-all binary classifiers, multiclass classifiers, polymorphism predictors, and multilabel classifiers. The synthetic minority over-sampling technique (SMOTE) is conducted to mitigate the effects of imbalanced data sets. Our results demonstrate that RF with Magpie features generally outperforms other algorithms for binary and multiclass prediction of crystal systems and space groups, while MLP with atom frequency features is the best method for structural polymorphism prediction. Finally, we propose using electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction due to its advantage of possessing a close relation with the physical and chemical properties of materials. We develop an ECD-based 3D convolutional neural network (CNN) to predict the elastic properties of materials in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Our experiments show that our method can achieve good performance for elasticity prediction over 2170 Fm-3m materials

Laboratory Directed Research and Development Program FY 2007 Annual Report

The Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD) program reports its status to the U.S. Department of Energy (DOE) in March of each year. The program operates under the authority of DOE Order 413.2B, 'Laboratory Directed Research and Development' (April 19, 2006), which establishes DOE's requirements for the program while providing the Laboratory Director broad flexibility for program implementation. LDRD funds are obtained through a charge to all Laboratory programs. This report includes summaries for all ORNL LDRD research activities supported during FY 2007. The associated FY 2007 ORNL LDRD Self-Assessment (ORNL/PPA-2008/2) provides financial data and an internal evaluation of the program's management process. ORNL is a DOE multiprogram science, technology, and energy laboratory with distinctive capabilities in materials science and engineering, neutron science and technology, energy production and end-use technologies, biological and environmental science, and scientific computing. With these capabilities ORNL conducts basic and applied research and development (R&D) to support DOE's overarching mission to advance the national, economic, and energy security of the United States and promote scientific and technological innovation in support of that mission. As a national resource, the Laboratory also applies its capabilities and skills to specific needs of other federal agencies and customers through the DOE Work for Others (WFO) program. Information about the Laboratory and its programs is available on the Internet at http://www.ornl.gov/. LDRD is a relatively small but vital DOE program that allows ORNL, as well as other DOE laboratories, to select a limited number of R&D projects for the purpose of: (1) maintaining the scientific and technical vitality of the Laboratory; (2) enhancing the Laboratory's ability to address future DOE missions; (3) fostering creativity and stimulating exploration of forefront science and technology; (4) serving as a proving ground for new research; and (5) supporting high-risk, potentially high-value R&D. Through LDRD the Laboratory is able to improve its distinctive capabilities and enhance its ability to conduct cutting-edge R&D for its DOE and WFO sponsors. To meet the LDRD objectives and fulfill the particular needs of the Laboratory, ORNL has established a program with two components: the Director's R&D Fund and the Seed Money Fund. As outlined in Table 1, these two funds are complementary. The Director's R&D Fund develops new capabilities in support of the Laboratory initiatives, while the Seed Money Fund is open to all innovative ideas that have the potential for enhancing the Laboratory's core scientific and technical competencies. Provision for multiple routes of access to ORNL LDRD funds maximizes the likelihood that novel ideas with scientific and technological merit will be recognized and supported