Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights

Area of Science:

• Materials Science
• Crystallography
• Computational Chemistry

Background:

• Organic molecules typically form dense crystalline structures.
• Porous molecular crystals contain open spaces, often stabilized by solvent molecules.
• Predicting solvent effects on crystal stability is challenging, hindering discovery.

Purpose of the Study:

• To accelerate the discovery of stable porous molecular crystals.
• To combine computational crystal structure prediction (CSP) with robotic crystallisation screening.
• To identify new crystalline phases of known organic molecules.

Main Methods:

• Utilized crystal structure prediction (CSP) to guide experimental screening.
• Employed a robotic crystallisation platform for high-throughput experiments.
• Investigated solvent effects on the formation of porous frameworks.

Main Results:

• Discovered a new guest-free porous polymorph of trimesic acid (TMA), denoted δ-TMA.
• Identified three novel solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA).
• Demonstrated the efficacy of the hybrid computational-experimental approach.

Conclusions:

• The combined CSP and robotic screening strategy accelerates the discovery of porous molecular crystals.
• This approach can uncover previously unknown crystalline structures and polymorphs.
• The methodology has broad applicability to materials design, including organic electronics and drug formulation.