4 research outputs found
Accelerated discovery of two crystal structure types in a complex inorganic phase field
The discovery of new materials is hampered by the lack of efficient approaches to the exploration of both the large number of possible elemental compositions for such materials, and of the candidate structures at each composition1. For example, the discovery of inorganic extended solid structures has relied on knowledge of crystal chemistry coupled with time-consuming materials synthesis with systematically varied elemental ratios2,3. Computational methods have been developed to guide synthesis by predicting structures at specific compositions4,5,6 and predicting compositions for known crystal structures7,8, with notable successes9,10. However, the challenge of finding qualitatively new, experimentally realizable compounds, with crystal structures where the unit cell and the atom positions within it differ from known structures, remains for compositionally complex systems. Many valuable properties arise from substitution into known crystal structures, but materials discovery using this approach alone risks both missing best-in-class performance and attempting design with incomplete knowledge8,11. Here we report the experimental discovery of two structure types by computational identification of the region of a complex inorganic phase field that contains them. This is achieved by computing probe structures that capture the chemical and structural diversity of the system and whose energies can be ranked against combinations of currently known materials. Subsequent experimental exploration of the lowest-energy regions of the computed phase diagram affords two materials with previously unreported crystal structures featuring unusual structural motifs. This approach will accelerate the systematic discovery of new materials in complex compositional spaces by efficiently guiding synthesis and enhancing the predictive power of the computational tools through expansion of the knowledge base underpinning them
Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design
This paper reviews past and ongoing efforts in using high-throughput ab-inito
calculations in combination with machine learning models for materials design.
The primary focus is on bulk materials, i.e., materials with fixed, ordered,
crystal structures, although the methods naturally extend into more complicated
configurations. Efficient and robust computational methods, computational
power, and reliable methods for automated database-driven high-throughput
computation are combined to produce high-quality data sets. This data can be
used to train machine learning models for predicting the stability of bulk
materials and their properties. The underlying computational methods and the
tools for automated calculations are discussed in some detail. Various machine
learning models and, in particular, descriptors for general use in materials
design are also covered.Comment: 19 pages, 2 figure