The General Utility Lattice Program (GULP) has been extended to include the ability to simulate polymers and surfaces, as well as adding many other new features, and the current status of the program is fully documented. Both the background theory is described, as well as providing a concise review of some of the previous applications in order to demonstrate the range of its use. Examples are presented of work performed using the new compatibilities of the software, including the calculation of Born effective charges, mechanical properties as a function of applied pressure, calculation of frequency-dependent dielectric data, surface reconstructions of calcite and the performance of a linear-scaling algorithm for bond-order potentials.

The general utility lattice program (GULP)

We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method allows one to predict the most stable crystal structure and a number of low-energy metastable structures for a given compound at any P-T conditions without requiring any experimental input. Extremely high (nearly 100%) success rate has been observed in a few tens of tests done so far, including ionic, covalent, metallic, and molecular structures with up to 40 atoms in the unit cell. We have been able to resolve some important problems in high-pressure crystallography and report a number of new high-pressure crystal structures (stable phases: epsilon-oxygen, new phase of sulphur, new metastable phases of carbon, sulphur and nitrogen, stable and metastable phases of CaCO3). Physical reasons for the success of this methodology are discussed.

Crystal structure prediction using ab initio evolutionary techniques: principles and applications.

We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method allows one to predict the most stable crystal structure and a number of low-energy metastable structures for a given compound at any P-T conditions without requiring any experimental input. Extremely high success rate has been observed in a few tens of tests done so far, including ionic, covalent, metallic, and molecular structures with up to 40 atoms in the unit cell. We have been able to resolve some important problems in high-pressure crystallography and report a number of new high-pressure crystal structures. Physical reasons for the success of this methodology are discussed.

Crystal structure prediction using ab initio evolutionary techniques: principles and applications

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and e1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.

Recent approaches to global optimization problems through Particle Swarm Optimization

One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

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Recent advances and applications of machine learning in solid-state materials science

The structure of SrRuO3 by time-of-flight neutron powder diffraction

New techniques based on the implementation of genetic algorithms together with energy minimisation procedures are able to predict the crystal structures of complex inorganic solids. A crucial feature of our approach is the use in the initial stages of the simulation of a sophisticated cost function based on Pauling's rules, recently extended and quantified as the bond valence model. Using such functions, we are able to generate candidate structures whose energies may subsequently be minimised using standard lattice energy methods employing Born model potentials. We demonstrate the efficacy of the method in yielding accurate solutions of complex crystal structures by its application to the previously unsolved structure of the ternary oxide Li3RuO4.

Evolutionary programming techniques for predicting inorganic crystal structures

The structural consequences of charge disproportionation in mixed-valence iron oxides. I. The crystal structure of Sr2LaFe3O8.94 at room temperature and 50 K

P.D. Battle

Papers

The structure of SrRuO3 by time-of-flight neutron powder diffraction

Evolutionary programming techniques for predicting inorganic crystal structures

The structural consequences of charge disproportionation in mixed-valence iron oxides. I. The crystal structure of Sr2LaFe3O8.94 at room temperature and 50 K

The crystal and magnetic structures of Sr2LuRuO6, Ba2YRuO6, and Ba2LuRuO6

The structural and magnetic properties of SrMnO3: A reinvestigation