Type and quality of data available in the scientific literature, there are still open questions on how machine learning can be used

The gap

Evidence profile

Sourced from the future work and abstract and future-work section of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 5 journals. Those papers have been cited 19 times in total.

Supporting evidence — 5 representative gaps

• Nanocatalysts in Organic Synthesis: Green, Sustainable, and High-Efficiency Approaches for Modern Synthetic Chemistry (2026) · International Journal of Science and Research (IJSR) · doi

  Future research directions include: • Development of biodegradable nanocatalysts • AI-assisted catalyst design • Biomimetic nanocatalytic systems • Continuous-flow nanoreactors • Hybrid photocatalytic materials • Solar-driven catalytic processes • Sustainable biomass conversion The integration of computational chemistry and machine learning with nanotechnology may revolutionize future catalyst discovery.

  generalfuture work
  Keywords: future catalyst directions include development biodegradable nanocatalysts assisted design biomimetic nanocatalytic systems continuous flow nanoreactors
• Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques (2025) · JACS Au · cited 8× · doi

  Given the type and quality of data available in the scientific literature, there are still open questions on how machine learning can be used by experimentalists working in the field of catalysis to accelerate catalyst design.

  generalabstract
  Keywords: given type quality available scientific literature there still open questions machine learning used experimentalists working
• From data to insight: computational tools for rationalizing organometallic and organocatalytic reactivity (2026) · RENDICONTI LINCEI · doi

  The paper suggests that future research should focus on the development of new computational tools for predicting catalytic behavior. The paper suggests that future research should focus on the application of computational tools to the design of new catalysts.

  generalfuture-work sectionevidence 5/5
  Keywords: paper suggests future research focus development new computational
• A Comprehensive Review on Advancing Greener Epoxidation: Mechanistic Insights and Sustainable Frontiers (2026) · International Journal of Drug Delivery Technology · doi

  The use of renewable energy, biotechnology, and machine-learning in catalyst design is anticipated to transform the discipline. The development of more sustainable and efficient epoxidation methods can improve the production of fine chemicals, drugs, and polymers.

  generalfuture-work sectionevidence 5/5
  Keywords: use renewable energy biotechnology machine-learning catalyst design anticipated
• Polyaniline-based hierarchical heterostructures for photocatalytic degradation of organic pollutants for water treatment – A critical review (2025) · Results in Engineering · cited 11× · doi

  Future research should focus on optimizing hierarchical structures for simultaneous pollutant degradation and disinfection, exploiting solar-driven hybrid systems, and employing machine-learning-assisted materials discovery to accelerate the design of robust, high-performance PANI-based photocatalysts for sustainable water purification.

  generalabstractevidence 5/5
  Keywords: future focus optimizing hierarchical structures simultaneous pollutant degradation disinfection exploiting solar driven hybrid systems employing

Questions about this gap