Frequency distribution and selection of space groups in inorganic crystal chemistry

Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery

The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography.

Computational Screening and Stabilization of Boron-Substituted Type-I and Type-II Carbon Clathrates

Boron substitution represents a promising approach to stabilize carbon clathrate structures, but no thermodynamically stable substitution schemes have been identified for frameworks other than the type-VII (sodalite) structure type. To investigate the possibility for additional tetrahedral carbon-based clathrate networks, more than 5000 unique boron decoration schemes were investigated computationally for type-I and type-II carbon clathrates with a range of guest elements including Li, Na, K, Rb, Cs, Mg, Ca, Sr, and Ba. Density functional theory calculations were performed at 10 and 50 GPa, and the stability and impact of boron substitution were evaluated. The results indicate that the boron-substituted carbon clathrates are stabilized under high-pressure conditions. Full cage occupancies of intermediate-sized guest atoms (e.g., Na, Ca, and Sr) are the most favorable energetically. Clathrate stability is maximized when the boron atoms are substituted within the hexagonal rings of the large [51262]/[51264] cages. Several structures with favorable formation enthalpies <-200 meV/atom were predicted, and type-I Ca8B16C30 is on the convex hull at 50 GPa. This structure represents the first thermodynamically stable type-I clathrate identified and suggests that boron-substituted carbon clathrates may represent a large family of diamond-like framework materials with a range of structure types and guest/framework substitutions.

MAGUS: machine learning and graph theory assisted universal structure searcher

Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially the complexity of conformational space and the cost of local optimizations for big systems. Here, we introduce a crystal structure prediction method, MAGUS, based on the evolutionary algorithm, which addresses the above challenges with machine learning and graph theory. Techniques used in the program are summarized in detail and benchmark tests are provided. With intensive tests, we demonstrate that on-the-fly machine-learning potentials can be used to significantly reduce the number of expensive first-principles calculations, and the crystal decomposition based on graph theory can efficiently decrease the required configurations in order to find the target structures. We also summarized the representative applications of this method on several research topics, including unexpected compounds in the interior of planets and their exotic states at high pressure and high temperature (superionic, plastic, partially diffusive state, etc.); new functional materials (superhard, high-energy-density, superconducting, photoelectric materials), etc. These successful applications demonstrated that MAGUS code can help to accelerate the discovery of interesting materials and phenomena, as well as the significant value of crystal structure predictions in general.

Understanding the Role of Entropy in High Entropy Oxides

The field of high entropy oxides (HEOs) flips traditional materials science paradigms on their head by seeking to understand what properties arise in the presence of profound configurational disorder. This disorder, which originates from multiple elements sharing a single lattice site, can take on a kaleidoscopic character due to the vast numbers of possible elemental combinations. High configurational disorder appears to imbue some HEOs with functional properties that far surpass their nondisordered analogs. While experimental discoveries abound, efforts to characterize the true magnitude of the configurational entropy and understand its role in stabilizing new phases and generating superior functional properties have lagged behind. Understanding the role of configurational disorder in existing HEOs is the crucial link to unlocking the rational design of new HEOs with targeted properties. In this Perspective, we attempt to establish a framework for articulating and beginning to address these questions in pursuit of a deeper understanding of the true role of entropy in HEOs.

Theoretical methods for structural phase transitions in elemental solids at extreme conditions: statics and dynamics

In recent years, theoretical studies have moved from a traditionally supporting role to a more proactive role in the research of phase transitions at high pressures. In many cases, theoretical prediction leads the experimental exploration. This is largely owing to the rapid progress of computer power and theoretical methods, particularly the structure prediction methods tailored for high-pressure applications. This review introduces commonly used structure searching techniques based on static and dynamic approaches, their applicability in studying phase transitions at high pressure, and new developments made toward predicting complex crystalline phases. Successful landmark studies for each method are discussed, with an emphasis on elemental solids and their behaviors under high pressure. The review concludes with a perspective on outstanding challenges and opportunities in the field.

Frequency distribution of space groups in soluble and membrane proteins and their complexes

The space-group frequency distributions for two types of proteins and their complexes are explored. Based on the incremental availability of data in the Protein Data Bank, an analytical assessment shows a preferential distribution of three space groups, i.e. P212121 > P1211 > C121, in soluble and membrane proteins as well as in their complexes. In membrane proteins, the order of the three space groups is P212121 > C121 > P1211. The distribution of these space groups also shows the same pattern whether a protein crystallizes with a monomer or an oligomer in the asymmetric unit. The results also indicate that the sizes of the two entities in the structures of soluble proteins crystallized as complexes do not influence the frequency distribution of space groups. In general, it can be concluded that the space-group frequency distribution is homogenous across different types of proteins and their complexes.

Teaching periodicity and aperiodicity using 3D-printed tiles and polyhedra

Unit cell and periodicity are key concepts in crystallography and classically were thought to be inherent properties of ordered media like crystals. Aperiodic crystals (including quasicrystals) forced a change of paradigm, affecting the actual definition of a crystal. However, aperiodicity is usually not taught in crystallography undergraduate courses. The emergence of low-cost 3D-printing technologies makes it possible to tackle hands-on learning of the commonly taught crystallography concepts related to periodicity and to introduce in an uncomplicated manner aperiodic crystals and their related concepts that usually are skipped. In this paper, several examples of the use of 3D printing are shown, including 2D and 3D examples of periodic and aperiodic ordered media; these are particularly useful to understand both conventional periodic crystals and quasicrystals. The STL files of the presented models are made available with the paper.

Thermal Behavior of Iron Arsenides Under Non-Oxidizing Conditions

Fe₂As has been studied in situ by synchrotron powder X-ray diffraction (PXRD) over the range of temperatures 25-850 °C and under a neutral atmosphere to understand its thermal behavior, which is potentially important for gold extraction. For the first time, incongruent high-temperature reactions of Fe₂As are observed as it breaks down and the existence of a previously undiscovered high-temperature FeAs phase with an NiAs-type structure has been determined experimentally. No evidence has been found for the existence of the high-temperature Fe₃As₂ phase. Hence, the previously published phase diagram for the Fe-As system has to be modified accordingly.

Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function

A method is presented for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100 000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, a convolutional neural network (CNN) model is presented which yields a promising result in that it correctly identifies the space group among the top-6 estimates 91.9% of the time. The CNN model also successfully identifies space groups for 12 out of 15 experimental PDFs. Interesting aspects of the failed estimates are discussed, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model-independent assessment of PDF data on a wide class of materials.

AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis

A priori prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically competing structures at formation conditions. Large materials repositories-housing properties of both experimental and hypothetical compounds-offer a path to prediction through the construction of informatics-based, ab initio phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability. Herein, a new module is presented for autonomous thermodynamic stability analysis, implemented within the open-source, ab initio framework AFLOW. Powered by the AFLUX Search-API, AFLOW-CHULL leverages data of more than 1.8 million compounds characterized in the AFLOW.org repository, and can be employed locally from any UNIX-like computer. The module integrates a range of functionality: the identification of stable phases and equivalent structures, phase coexistence, measures for robust stability, and determination of decomposition reactions. As a proof of concept, thermodynamic characterizations have been performed for more than 1300 binary and ternary systems, enabling the identification of several candidate phases for synthesis based on their relative stability criterion-including 17 promising C15 _b-type structures and 2 half-Heuslers. In addition to a full report included herein, an interactive, online web application has been developed showcasing the results of the analysis and is located at aflow.org/aflow-chull .

Transferable Aspherical Atom Modeling of Electron Density in Highly Symmetric Crystals: A Case Study of Alkali-Metal Nitrates

A comparative electron density study (from X-ray diffraction and periodic quantum chemistry) of sodium and potassium nitrates is performed to test the performance of a transferrable aspherical atom model, which is based on the invarioms, to describe chemical bonding features of ions occurring in sites of different symmetry typical of inorganic salts and in different crystal environments. Relying on tabulated entries for the isolated ions (although tailor-made to account for different site symmetries), it takes the same time to employ as the spherical atom model routinely used in X-ray diffraction studies but provides an electron density distribution that faithfully reveals all the interionic interactions-even the weakest ones (such as between the nitrate anions or a K···N interaction found in the metastable form of KNO₃) yet important for properties of inorganic materials-as if obtained from high-resolution X-ray diffraction data.

Ab initio random structure searching

It is essential to know the arrangement of the atoms in a material in order to compute and understand its properties. Searching for stable structures of materials using first-principles electronic structure methods, such as density-functional-theory (DFT), is a rapidly growing field. Here we describe our simple, elegant and powerful approach to searching for structures with DFT, which we call ab initio random structure searching (AIRSS). Applications to discovering the structures of solids, point defects, surfaces, and clusters are reviewed. New results for iron clusters on graphene, silicon clusters, polymeric nitrogen, hydrogen-rich lithium hydrides, and boron are presented.