Structure prediction drives materials discovery

Abstract

Progress in the discovery of new materials has been accelerated by the development of reliable quantum-mechanical approaches to crystal structure prediction. The properties of a material depend very sensitively on its structure, therefore structure prediction is the key to computational materials discovery. Structure prediction was considered to be a formidable problem, but the development of new computational tools has allowed the structures of many new and increasingly complex materials to be anticipated. These widely applicable methods, based on global optimisation and relying on little or no empirical knowledge, have been used to study crystalline structures, point defects, surfaces and interfaces. In this Review we discuss structure prediction methods, examining their potential for the study of different materials systems, and present examples of computationally-driven discoveries of new materials -including superhard materials, superconductors and organic materials -that will enable new technologies. Advances in firstprinciple structure predictions also lead to a better understanding of physical and chemical phenomena in materials.

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

“…This way, the ML potential "steers" itself into regions of configuration space that need to be further explored, and large amounts of structures can be quickly assembled, much more quickly than would be possible with DFT-MD. [5] This idea can be taken a step further, by exploring and fitting structural space on the fly (and running all structural optimizations with interim potentials, requiring DFT only for single-point input data), thereby unifying ideas from ML potential fitting and crystal-structure searching. Similar strategies were later used for amorphous carbon, [43] where hundreds of small structural snapshots could be generated in parallel runs using a computationally cheap interim potential, and the end points of these trajectories were evaluated with DFT and added to the database.…”

“…Similar strategies were later used for amorphous carbon, [43] where hundreds of small structural snapshots could be generated in parallel runs using a computationally cheap interim potential, and the end points of these trajectories were evaluated with DFT and added to the database. [5,49] A recently proposed strategy is to explore the PES using global searches [44][45][46][47] that are normally done in DFT-based crystalstructure prediction.…”

“…The most fundamental one is that of individual atoms, and of the 3D structures that they form, driven by physical laws and chemical bonding. [3][4][5] [2] Accurately describing, understanding, and ultimately controlling the atomic-scale structure of matter is therefore a central goal of materials science.…”

“…

In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms.

…”

“…State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms.…”