Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
First-principles investigation of structural, elastic, electronic, thermodynamic, and optical properties of KBi<sub>3</sub> for optoelectronic applications. [PDF]
Rabbi MM, Khatun MA.
europepmc +1 more source
Suha Wazzan, Sakander Hayat, Wafi Ismail
openaire +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Mechanisms of curcumin adsorption in metal-substituted MOF-74 frameworks using DFT and MD simulations. [PDF]
Wang Y +5 more
europepmc +1 more source
The role of oxygen vacancies in the electronic and optical properties of κ-Ga<sub>2</sub>O<sub>3</sub>. [PDF]
Feng W +7 more
europepmc +1 more source
AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing. [PDF]
Narra M +4 more
europepmc +1 more source
Size and shape dependent cohesive energy and melting temperature in semiconducting nanomaterials. [PDF]
Sherka GT, Berry HD, Zhang Q.
europepmc +1 more source
Multi-Scale Modeling of Doped Magnesium Hydride Nanomaterials for Hydrogen Storage Applications. [PDF]
Chrafih Y +3 more
europepmc +1 more source

