Exploration of Chemical Space Through Automated Reasoning.

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts.

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.

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 materials. Researchers have developed efficient heuristics to identify structural minima on the potential energy surface.

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.

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 rare. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space.

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 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.