Statistically Derived Proxy Potentials Accelerate Geometry Optimization of Crystal Structures.

The crystal structures of known materials contain the information about the interatomic interactions that produced these stable compounds. Similar to the use of reported protein structures to extract effective interactions between amino acids, that has been a useful tool in protein structure prediction, we demonstrate how to use this statistical paradigm to learn the effective inter-atomic interactions in crystalline inorganic solids. By analyzing the reported crystallographic data for inorganic materials, we have constructed statistically derived proxy potentials (SPPs) that can be used to assess how realistic or unusual a computer-generated structure is compared to the reported experimental structures. The SPPs can be directly used for structure optimization to improve this similarity metric, that we refer to as the SPP score. We apply such optimization step to markedly improve the quality of the input crystal structures for DFT calculations and demonstrate that the SPPs accelerate geometry optimization for three systems relevant to battery materials. As this approach is chemistry-agnostic and can be used at scale, we produced a database of all possible pair potentials in a tabulated form ready to use.

Optimality guarantees for crystal structure prediction.

Crystalline materials enable essential technologies, and their properties are determined by their structures. Crystal structure prediction can thus play a central part in the design of new functional materials1,2. Researchers have developed efficient heuristics to identify structural minima on the potential energy surface3-5. Although these methods can often access all configurations in principle, there is no guarantee that the lowest energy structure has been found. Here we show that the structure of a crystalline material can be predicted with energy guarantees by an algorithm that finds all the unknown atomic positions within a unit cell by combining combinatorial and continuous optimization. We encode the combinatorial task of finding the lowest energy periodic allocation of all atoms on a lattice as a mathematical optimization problem of integer programming6,7, enabling guaranteed identification of the global optimum using well-developed algorithms. A single subsequent local minimization of the resulting atom allocations then reaches the correct structures of key inorganic materials directly, proving their energetic optimality under clear assumptions. This formulation of crystal structure prediction establishes a connection to the theory of algorithms and provides the absolute energetic status of observed or predicted materials. It provides the ground truth for heuristic or data-driven structure prediction methods and is uniquely suitable for quantum annealers8-10, opening a path to overcome the combinatorial explosion of atomic configurations.

Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry.

The selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here, we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li3.3SnS3.3Cl0.7. The interstitial site occupancy combination in this defect stuffed wurtzite enables a low-barrier ion transport pathway in hexagonal close-packing.

A mobile robotic chemist.

Technologies such as batteries, biomaterials and heterogeneous catalysts have functions that are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare1-5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6-14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water15. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm16-18. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous19,20 free-roaming robot21-24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.

Terminology spectrum analysis of natural-language chemical documents: term-like phrases retrieval routine.

BACKGROUND: This study seeks to develop, test and assess a methodology for automatic extraction of a complete set of 'term-like phrases' and to create a terminology spectrum from a collection of natural language PDF documents in the field of chemistry. The definition of 'term-like phrases' is one or more consecutive words and/or alphanumeric string combinations with unchanged spelling which convey specific scientific meanings. A terminology spectrum for a natural language document is an indexed list of tagged entities including: recognized general scientific concepts, terms linked to existing thesauri, names of chemical substances/reactions and term-like phrases. The retrieval routine is based on n-gram textual analysis with a sequential execution of various 'accept and reject' rules with taking into account the morphological and structural information. RESULTS: The assessment of the retrieval process, expressed quantitatively with a precision (P), recall (R) and F1-measure, which are calculated manually from a limited set of documents (the full set of text abstracts belonging to 5 EuropaCat events were processed) by professional chemical scientists, has proved the effectiveness of the developed approach. The term-like phrase parsing efficiency is quantified with precision (P = 0.53), recall (R = 0.71) and F1-measure (F1 = 0.61) values. CONCLUSION: The paper suggests using such terminology spectra to perform various types of textual analysis across document collections. This sort of the terminology spectrum may be successfully employed for text information retrieval, for reference database development, to analyze research trends in subject fields of research and to look for the similarity between documents.Graphical abstractTerminology spectrum building process with term-like phrases retrieval.

The evolution of the vertebrate beta-globin gene promoter.

Complexity analysis is capable of highlighting those gross evolutionary changes in gene promoter regions (loosely termed "promoter shuffling") that are undetectable by conventional DNA sequence alignment. Complexity analysis was therefore used here to identify the modular components (blocks) of the orthologous beta-globin gene promoter sequences of 22 vertebrate species, from zebrafish to humans. Considerable variation between the beta-globin gene promoters was apparent in terms of block presence/absence, copy number, and relative location. Some sequence blocks appear to be ubiquitous, whereas others are restricted to a specific taxon. Block similarities were also evident between the promoters of the paralogous human beta-like globin genes. It may be inferred that a wide variety of different mutational mechanisms have operated upon the beta-globin gene promoter over evolutionary time. Because these include gross changes such as deletion, duplication, amplification, elongation, contraction, and fusion, as well as the steady accumulation of single base-pair substitutions, it is clear that some redefinition of the term "promoter shuffling" is required. This notwithstanding, and as previously described for the vertebrate growth hormone gene promoter, the modular structure of the beta-globin promoter region and those of its paralogous counterparts have continually been rearranged into new combinations through the alteration, or shuffling, of preexisting blocks. Some of these changes may have had no influence on promoter function, but others could have altered either the level of gene expression or the responsiveness of the promoter to external stimuli. The comparative study of vertebrate beta-globin gene promoter regions described here confirms the generality of the phenomenon of sequence block shuffling and thus supports the view that it could have played an important role in the evolution of differential gene expression.