ML meets MLn: Machine learning in ligand promoted homogeneous catalysis

Journal article

The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised. We focus on the understanding and implementation of key concepts, which serve as conduits to more advanced chemical machine learning literature, much of which is (presently) outside the area of homogeneous catalysis. Potential pitfalls in the ‘workflow’ procedures needed in the machine learning process are identified and all the examples provided are in a chemical sciences context, including several from ‘real world’ catalyst systems. Finally, potential areas of expansion and impact for machine learning in homogeneous catalysis in the future are considered

Authors: Hirst, JD; Boobier, S; Coughlan, J; Streets, J; Jacob, PL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier BV
• Journal: Artificial Intelligence Chemistry
• Volume: 1
• Issue: 2
• Pages: 100006-100006
• Publication date: 2023-07-11
• DOI: 10.1016/j.aichem.2023.100006
• EISSN: 2949-7477
• ISSN: 2949-7477
• Language: English
• Keywords: Artificial intelligence; Biochemical engineering; Workflow; Chemistry; Thermodynamics; Catalysis; Engineering; Process management; Organic chemistry; Context (archaeology); Homogeneous; Process (computing); Homogeneous catalysis; Computer science; Physics