Ex nihilo crystal structure discovery

Lead Research Organisation: University College London
Department Name: Physics and Astronomy

Abstract

The discovery that matter is made up of atoms ranks as one ofmankind's greatest achievements. Twenty first century science isdominated by a quest for the mastery (both in terms of control andunderstanding) of our environment at the atomic level.In biology, understanding life (preserving it, or even attempting tocreate it) revolves around large, complex, molecules -- RNA, DNA, andproteins.Global warming is dictated by the particular way atoms are arrangedto make small greenhouse gas molecules, carbon dioxide and so on.The drive for faster, more efficient, cheaper computer chips forcesnanotechnology upon us. As the transistors that make up themicroscopic circuits are packed ever closer together, electronicengineers must understand where the atoms are placed, or misplaced, inthe semiconducting and insulating materials.Astronomers are currently, daily, discovering new planets outside oursolar system, orbiting alien stars. The largest are the easiest tospot, and many are far larger than Jupiter. The more massive theplanet the higher pressures endured by the matter that makes up itsbulk. How can we hope to determine the structure of matter at theseconditions?The atomic theory of matter leads to quantum mechanics -- a mechanicsof the every small. In principle, to understand and predict thebehaviour of matter at the atomic scale simply requires the solutionof the quantum mechanical Schroedinger equations. This is a challengein itself, but in an approximate way it is now possible to quicklycompute the energies and properties of fairly large collections ofatoms. But is it possible to predict how those atoms will be arrangedin Nature - ex nihilo, from nothing but our understanding ofphysics?Some have referred to it as a scandal that the physical sciencescannot routinely predict the structure of even simple crystals -- butmost have assumed it to be a very difficult problem. A minimum energymust be found in a many dimensional space of all the possiblestructures. Those researchers brave enough to tackle this challengehave done so by reaching for complex algorithms -- such as geneticalgorithms, which appeal to evolution to breed ever betterstructures (with better taken to mean more stable). However, Ihave discovered to my surprise, and to others', that the very simplestalgorithm -- throw the collection of atoms into a box, and move theatoms downhill on the energy landscape -- is remarkably effectiveif it is repeated many times.This approach needs no prior knowledge of chemistry. Indeed thescientist is taught chemistry by its results -- this is critical ifthe method is to be used to predict the behaviour of matter underextreme conditions, where learned intuition will typically fail.I have used this approach, which I call random structure searching to predict the structure of crystals ex nihilo. My firstapplication of it has been to silane at very high pressures, and thestructure I predicted has recently been seen in experiments. Butprobably the most impressive application so far has been to predictingthe structure of hydrogen at the huge pressures found in the gas giantplanets, where it may be a room temperature superconductor.In the course of my fellowship I will extend this work to try toanticipate the structure of matter in the newly discovered exoplanets,to try to discover and design materials with extreme (and hopefully,extremely useful) properties, and to help pharmaceutical researchersunderstand the many forms that their drug molecules adopt when theycrystallise.

Publications

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Bardwell DA (2011) Towards crystal structure prediction of complex organic compounds--a report on the fifth blind test. in Acta crystallographica. Section B, Structural science

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Chandran CV (2010) Improving sensitivity and resolution of MQMAS spectra: a 45Sc-NMR case study of scandium sulphate pentahydrate. in Journal of magnetic resonance (San Diego, Calif. : 1997)

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Chen J (2017) Double-layer ice from first principles in Physical Review B

 
Description The Ab initio random structure searching (AIRSS) approach to material structure prediction was developed over the period of the leadership fellowship. It is now recognised as a key tool for computational materials discovery, complementing "materials genome" type database approaches.

AIRSS has been applied to a very wide range of systems: battery anode materials, pharmaceutical compounds, complex defects, and interfaces.

The major scientific discoveries have been in the field of high pressure physics. I computationally identified a new route to elemental magnetism (in dense potassium), and a what others have called a "new paradigm" of ultradense matter. I found that structure is complex at extremely high (terapascal) pressures of the sort encountered in laser shock experiments such as performed at the National Ignition Facility (LLNL, USA). Aluminium was found to adopt a incommensurate host-guest phase, four new thermodynamically stable of carbon were discovered, and the end of water (its decomposition) proposed.

AIRSS, along with my earlier GIPAW theory, of solid state NMR, is playing a key role in the development of NMR Crystallography.
Exploitation Route It is likely that AIRSS, or methods inspired by it, will be implemented in commercial scientific software packages in due course. In 2017 the AIRSS package was released under GPL2.
Sectors Aerospace, Defence and Marine,Agriculture, Food and Drink,Chemicals,Construction,Electronics,Energy,Environment,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology,Security and Diplomacy,Transport

URL https://www.mtg.msm.cam.ac.uk/Codes/AIRSS
 
Title AIRSS 
Description Ab initio Random Structure Searching (AIRSS) is a very simple, yet powerful and highly parallel, approach to structure prediction. The concept was introduced in 2006 and its philosophy more extensively discussed in 2011. Random structures - or more precisely, random "sensible" structures - are generated and then relaxed to nearby local energy minima. Particular success has been found using density functional theory (DFT) for the energies, hence the focus on "ab initio" random structure searching. The sensible random structures are constructed so that they have reasonable densities, and atomic separations. Additionally they may embody crystallographic, chemical or prior experimental/computational knowledge. Beyond these explicit constraints the emphasis is on a broad, uniform, sampling of structure space. AIRSS has been used in a number of landmark studies in structure prediction, from the structure of SiH4 under pressure to providing the theoretical structures which are used to understand dense hydrogen (and anticipating the mixed Phase IV), incommensurate phases in aluminium under terapascal pressures, and ionic phases of ammonia. The approach naturally extends to the prediction clusters/molecules, defects in solids, interfaces and surfaces (interfaces with vacuum). The AIRSS package is tightly integrated with the CASTEP first principles total energy code. However, it is relatively straightforward to modify the scripts to use alternative codes to obtain the core functionality, and examples are provided. The AIRSS package is released under the GPL2 licence. 
Type Of Technology Software 
Year Produced 2017 
Impact It appears that researcher are routinely using AIRSS. 
URL https://www.mtg.msm.cam.ac.uk/Codes/AIRSS