Machine learning-driven optimization of the output force in photo-actuated organic crystals

2025/01/01 by Kazuki Ishizaki, Toru Asahi, Takuya Taniguchi · 1 voice
Chemistry · Materials Science · #Advanced Polymer Synthesis and Characterization #Machine Learning in Materials Science #Photopolymerization techniques and applications

paper · pdf · doi:10.1039/d4dd00380b

openalex publication_date 2025/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/13

Abstract

We employed two types of machine learning for molecular design and experimental optimization to efficiently maximize the blocking force.

Citations

• <i>OLEX2</i>: a complete structure solution, refinement and analysis program
• <i>ShelXle</i>: a Qt graphical user interface for<i>SHELXL</i>

Discussions

• In today's featured article, Ishizaki, Asahi, and Taniguchi design photo-actuated molecular crystals using machine learning for molecular design and experimental optimization to maximize the blocking [bsky, 2 points, 0 comments]

Related

• Physical embedding machine learning force fields for organic systems
• Molecular modeling with machine-learned universal potential functions
• Synergistic integration of physical embedding and machine learning enabling precise and reliable force field
• Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
• Molecular Packing: Another Key Point for the Performance of Organic and Polymeric Optoelectronic Materials