Advancing materials discovery through artificial intelligence
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15640%2F25%3A73632558" target="_blank" >RIV/61989592:15640/25:73632558 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/25:10259870
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2352940725003981?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352940725003981?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.apmt.2025.102981" target="_blank" >10.1016/j.apmt.2025.102981</a>
Alternative languages
Result language
angličtina
Original language name
Advancing materials discovery through artificial intelligence
Original language description
Artificial intelligence (AI) is transforming materials science by accelerating the design, synthesis, and characterization of novel materials. This review highlights how AI, including machine learning, deep learning, and generative models, is reshaping the discovery pipeline. AI-driven approaches enable rapid property prediction, inverse design, and simulation of complex systems such as nanomaterials and solid-state materials, often matching the accuracy of ab initio methods at a fraction of the computational cost. Machine-learning-based force fields provide efficient and transferable models for large-scale simulations, while explainable AI improves transparency and physical interpretability. In synthesis, AI supports synthesis planning, reaction optimization, and the development of autonomous laboratories capable of real-time feedback and adaptive experimentation. Tools originally developed for organic molecules are increasingly adapted for complex materials, including those with structural, thermodynamic, or kinetic constraints. AI also advances in situ characterization, automating tasks such as spectral interpretation and defect identification. Despite rapid progress, challenges remain in model generalizability, standardized data formats, experimental validation, and energy efficiency. The review underscores the importance of hybrid approaches combining physical knowledge with data-driven models and calls for open-access datasets also including negative experiments and ethical frameworks to ensure responsible deployment. Future directions include modular AI systems, improved human-AI collaboration, integration with techno-economic analysis, and field-deployable robotics. By aligning computational innovation with practical implementation, AI is poised to drive scalable, sustainable, and interpretable materials discovery, turning autonomous experimentation into a powerful engine for scientific advancement.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10403 - Physical chemistry
Result continuities
Project
<a href="/en/project/EH22_008%2F0004587" target="_blank" >EH22_008/0004587: Technology Beyond Nanoscale</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Materials Today
ISSN
2352-9407
e-ISSN
—
Volume of the periodical
47
Issue of the periodical within the volume
December
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
21
Pages from-to
nestránkováno
UT code for WoS article
001612600700001
EID of the result in the Scopus database
2-s2.0-105021214628