Results 181 to 190 of about 21,815 (240)

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

Deep Ensemble learning and quantum machine learning approach for Alzheimer's disease detection. [PDF]

Sci Rep
Jenber Belay A, Walle YM, Haile MB.
europepmc   +1 more source

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

Transition role of entangled data in quantum machine learning. [PDF]

Nat Commun
Wang X, Du Y, Tu Z, Luo Y, Yuan X, Tao D.   +5 more
europepmc   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Publisher Correction: Overcoming the coherence time barrier in quantum machine learning on temporal data. [PDF]

Nat Commun
Hu F, Khan SA, Bronn NT, Angelatos G, Rowlands GE, Ribeill GJ, Türeci HE.   +6 more
europepmc   +1 more source

Δ-Quantum machine-learning for medicinal chemistry.

Phys Chem Chem Phys, 2022
Atz K, Isert C, Böcker MNA, Jiménez-Luna J, Schneider G.   +4 more
europepmc   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

Understanding quantum machine learning also requires rethinking generalization. [PDF]

Nat Commun
Gil-Fuster E, Eisert J, Bravo-Prieto C.
europepmc   +1 more source