Data mined ionic substitutions for the discovery of new compounds

Area of Science:

• Solid state chemistry
• Materials science
• Computational chemistry

Background:

• New compound discovery in solid state chemistry often relies on substituting chemically similar ions in known crystal structures.
• Traditional methods for proposing new compounds, such as considering ionic size, lack quantitative predictive power.

Purpose of the Study:

• To develop a mathematical framework for the process of discovering new compounds through ionic substitutions.
• To propose a probabilistic model for assessing the likelihood of ionic substitution while maintaining crystal structure integrity.

Main Methods:

• A probabilistic model was developed to evaluate the potential for ionic species to substitute within crystal lattices.
• The model was trained using an experimental database of known crystal structures.
• Cross-validation on quaternary ionic compounds was employed to demonstrate the model's predictive capabilities.

Main Results:

• The developed probabilistic model quantitatively suggests novel compounds and their corresponding crystal structures.
• The model's predictive power was validated through rigorous cross-validation.
• Analysis of the model revealed substitution rules that were compared with established chemical heuristics.

Conclusions:

• The probabilistic framework offers a quantitative approach to postulate new compounds via ionic substitution.
• This data-driven method enhances the discovery of novel materials and structures in solid state chemistry.
• The model provides a valuable tool for computational materials discovery, complementing traditional chemical intuition.