Sep 2026· Russian Journal of Physical Chemistry· Vol 100, pp. 2104 - 2121· 0 citations· 59 references
TL;DR
This review is a full survey of merging of mathematical models, AI algorithms and the chemistry and all combine to facilitate the discovery of materials on digital platforms, aimed at crossing the disciplinary boundaries and providing a coherent roadmap to the next-generation intelligent materials discovery.
The intrinsic challenges of accessing historical dark data in materials science are described and contrasted with timely opportunities for leveraging massive amounts of experimental data from laboratories in going forwards; by exploiting electronic-lab notebooks, high-throughput experiments, and digital-twin technologi...
Jacqueline M. Cole· Advances in Materials· 0 citations
Artificial intelligence (AI) is transforming the landscape of materials discovery by addressing the
limitations of traditional experimental and computational approaches. Conventional methods,
while foundational, are often slow, resource-intensive, and constrained by the vastness of
chemical space. AI techniques—incl...
Imasuen Aishat Omoh· WORLD JOURNAL OF INNOVATION...· 0 citations
The impact of artificial intelligence (AI) on modern chemical research is also being studied through secondary data from literature. Research studies have shown that the primary applications of AI in chemistry include molecular property prediction, retrosynthetic planning, reaction optimization, de novo molecular desig...
Yogendra Kumar Saraswat, Bhawna Varshney· International Journal of Tec...· 0 citations
Recent advancements in data infrastructure, computational statistics, and artificial intelligence (AI) have inaugurated a transformative era for chemical sciences. These computational paradigms facilitate the optimization of complex systems at an unprecedented rate, transcending the limitations of traditional trial-and...
Samuel Villanueva, Lisbeth Mendoza, Paulino Betancourt et al.· Discover Chemistry· 0 citations
The prerequisites for successfully applying data science in synthetic chemistry are outlined, and data‐driven approaches that can be applied in the development of new chemical reactions and synthetic methodologies are highlighted, including all relevant stages from reaction discovery and optimization to substrate scope...
Niklas Hölter, Felix Katzenburg, Florian Boser et al.· Angewandte Chemie Novit· 0 citations
Machine learning exhibits significant potential in the research of micro‑nano materials, particularly in accelerating material design and performance optimization through precise structure-property prediction. It is capable of precisely predicting the structure and properties of micro‑nano materials, thereby enabling r...
Ke Wu, Zefan Sang, Guangxun Zhang et al.· Small· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
