Results 201 to 210 of about 122,075 (313)
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
This review summarizes key advances from 2024 to 2025 that are reshaping esophageal cancer surgery toward a strategy‐oriented, personalized paradigm through the integration of immunotherapy, population aging, and intelligent technologies. Adjuvant nivolumab after neoadjuvant chemoradiotherapy remains the only perioperative approach with durable benefit,
Shuichiro Oya +2 more
wiley +1 more source
Innovations in Gastric Cancer Surgery During Early Minimally Invasive Era and Future Perspectives
With continuing revelations in tumor biology and the emergence of artificial intelligence, new horizons for surgical innovation are opening. At the center of this transformative journey stands the innovative surgeon, driven by passion, guided by data, and steadfast in the commitment to patient safety and quality of life.
Reut El‐On, Young‐Woo Kim
wiley +1 more source
Challenges and enablers in fluidization technology
Abstract Gas–solid fluidized beds provide excellent heat and mass transfer for high‐throughput operations from coating to catalytic conversion and underpin emerging low‐carbon technologies. Yet industrial reliability, scale‐up, and control lag scientific understanding, particularly as finer, stickier, and more variable feedstocks increasingly challenge
J. Ruud van Ommen, Jia Wei Chew
wiley +1 more source
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim +2 more
wiley +1 more source
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob +2 more
wiley +1 more source
Hera-JVM: abstracting processor heterogeneity behind a virtual machine [PDF]
McIlroy, R., Sventek, J.
core
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia +3 more
wiley +1 more source
Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review. [PDF]
Bin Abdulqader MI +3 more
europepmc +1 more source

