Towards Predictive Synthesis of Inorganic Materials Using Network Science

Abstract

Accelerating materials discovery is the cornerstone of modern technological competitiveness. Yet, the inorganic synthesis of new compounds is often an important bottleneck in this quest. Well-established quantum chemistry and experimental synthesis methods combined with consolidated network science approaches might provide revolutionary knowledge to tackle this challenge. Recent pioneering studies in this direction have shown that the topological analysis of material networks hold great potential to effectively explore the synthesizability of inorganic compounds. In this Perspective we discuss the most exciting work in this area, in particular emerging new physicochemical insights and general concepts on how network science can significantly help reduce the timescales required to discover new materials and find synthetic routes for their fabrication. We also provide a perspective on outstanding problems, challenges and open questions.

Topochemical Modulations from 1D Iron Fluoride Precursor to 3D Frameworks

“…Solid-state reactions, which are widely practiced from the laboratory to industrial scale to produce inorganic materials, require high energy for the facile diffusion of reagents, thereby producing thermodynamically stable products. Although the recent progress in in situ X-ray diffraction − and computational techniques , have provided additional information regarding the formation mechanism of desired phases including metastable ones, the prediction of synthesis outcomes is still difficult as one cannot dictate the atomic diffusion processes occurring through conventional solid-state reactions. This situation is in stark contrast to organic synthesis, wherein molecular chemists employ retrosynthetic approaches to direct the atomic connectivity via solution-state sequential reactions for making targeted natural products, macromolecules, and supramolecular structures.…”

Predicting polymerization reactions via transfer learning using chemical language models

“…Their synthesis typically involves various polymerization steps, with a multitude of possible links between monomer units. The prediction of thermodynamically stable polymer candidates, as well as the determination of a polymer's synthesizability 10 , is still affected by critical methodological limitations.…”