Abstract
Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.
Introduction
Synthesis prediction for inorganic materials remains one of the major challenges in accelerating materials discovery1,2,3,4, mostly because the complexity of the synthesis process itself hinders the development of a general, first-principles approach to it3,5. Thermodynamic stability is one of the main factors that strongly influence synthesizability of a material, but extracting it requires the knowledge of the energetics of competing phases. This bottleneck has recently been addressed for inorganic materials by high-throughput (HT) density functional theory (DFT) databases6,7,8,9, which provide access to systematic DFT calculations of thousands of existing inorganic materials as well as hypothetical ones. These databases allow the construction of a comprehensive energy convex-hull: the multidimensional surface formed by the lowest energy combination of all phases. Phases that are on the convex-hull are thermodynamically stable, and tie-lines connecting two phases indicate two-phase equilibria. Given that it is composed of stable materials (nodes) connected by tie-lines (edges), the convex-hull is a naturally occurring thermodynamic network (Fig. 1), analogous to the world-wide-web, social, citation, and protein networks10,11,12,13,14. The information encoded in this new network of materials can be harnessed with the tools provided by the emerging paradigm of network science, and forms the basis of new data-driven models for outstanding materials challenges, such as predicting synthesizability.
For any more information, please log on https://www.nature.com/articles/s41467-019-10030-5
Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.
Introduction
Synthesis prediction for inorganic materials remains one of the major challenges in accelerating materials discovery1,2,3,4, mostly because the complexity of the synthesis process itself hinders the development of a general, first-principles approach to it3,5. Thermodynamic stability is one of the main factors that strongly influence synthesizability of a material, but extracting it requires the knowledge of the energetics of competing phases. This bottleneck has recently been addressed for inorganic materials by high-throughput (HT) density functional theory (DFT) databases6,7,8,9, which provide access to systematic DFT calculations of thousands of existing inorganic materials as well as hypothetical ones. These databases allow the construction of a comprehensive energy convex-hull: the multidimensional surface formed by the lowest energy combination of all phases. Phases that are on the convex-hull are thermodynamically stable, and tie-lines connecting two phases indicate two-phase equilibria. Given that it is composed of stable materials (nodes) connected by tie-lines (edges), the convex-hull is a naturally occurring thermodynamic network (Fig. 1), analogous to the world-wide-web, social, citation, and protein networks10,11,12,13,14. The information encoded in this new network of materials can be harnessed with the tools provided by the emerging paradigm of network science, and forms the basis of new data-driven models for outstanding materials challenges, such as predicting synthesizability.
For any more information, please log on https://www.nature.com/articles/s41467-019-10030-5
