Results 221 to 230 of about 63,648 (277)

Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations.

Digit Discov
Sorrentino S, Gussoni A, Calcagno F, Pasotti G, Avagliano D, Rivalta I, Garavelli M, Polli D.   +7 more
europepmc   +1 more source

Machine Learning-Assisted DNA Origami Shape Sorting Using Fingerprinting Nanosensors and Feature Engineering. [PDF]

Anal Chem
Singha S, Demir MM, Morya V, Halvorsen K, Canbaz MA, Chandrasekaran AR, Yigit MV.   +6 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
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, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

AI‑driven photonic noses: from conventional sensors to cloud‑to-edge intelligent microsystems. [PDF]

Microsyst Nanoeng
Zhou H, Zhang H, Zhang R, Yuan X, Chang H.   +4 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

MolPrice: assessing synthetic accessibility of molecules based on market value. [PDF]

J Cheminform
Hastedt F, Hellgardt K, Yaliraki S, Zhang D, Del Rio Chanona A.   +4 more
europepmc   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +1 more source

Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology. [PDF]

PLoS Comput Biol
Long Y, Donald BR.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

Reverse engineering molecules from fingerprints through deterministic enumeration and generative models. [PDF]

J Cheminform
Meyer P, Duigou T, Gricourt G, Faulon JL.   +3 more
europepmc   +1 more source