Network analysis of synthesizable materials discovery

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.

arXiv comment20 pages, 5 figures

References in corpus (10)

• Scikit-learn: Machine Learning in Python
• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Power-law distributions in empirical data
• Error and attack tolerance of complex networks
• The structure of scientific collaboration networks
• Powerlaw: a Python package for analysis of heavy-tailed distributions
• Computational Investigation of Half-Heusler Compounds for Spintronics Applications
• Computational Investigation of Inverse-Heusler compounds for Spintronics Applications
• The Phase Stability Network of all Inorganic Materials

Cited by in corpus (11)

• Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry
• Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB${}_2$ Near Hydrogen Liquefaction Temperature
• The Phase Stability Network of all Inorganic Materials
• Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks
• What the foundations of quantum computer science teach us about chemistry
• Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions
• Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties
• AlphaCrystal: Contact map based crystal structure prediction using deep learning
• SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
• Visualizing Temperature-Dependent Phase Stability in High Entropy Alloys
• Network topology mapping of Chemical Compounds Space