Results 181 to 190 of about 23,653 (255)

Challenges and enablers in fluidization technology

AIChE Journal, EarlyView.
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

Quantum machine learning with Adaptive Boson Sampling via post-selection. [PDF]

Nat Commun
Hoch F, Caruccio E, Rodari G, Francalanci T, Suprano A, Giordani T, Carvacho G, Spagnolo N, Koudia S, Proietti M, Liorni C, Cerocchi F, Albiero R, Di Giano N, Gardina M, Ceccarelli F, Corrielli G, Chabaud U, Osellame R, Dispenza M, Sciarrino F.   +20 more
europepmc   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
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, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

Quantum machine learning-based electrokinetic mining for the identification of nanoparticles and exosomes with minimal training data. [PDF]

Bioact Mater
Thakur A, Santos Bezerra PC, Abhishek, Zeng S, Zhang K, Treptow W, Luna A, Dougherty U, Kwesi A, Huang IR, Bestvina C, Garassino MC, Duan F, Gokhale Y, Duan B, Chen Y, Lian Q, Bissonnette M, Huang J, Chen HJ.   +19 more
europepmc   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

QCAE-QOC-SVM: A hybrid quantum machine learning model for DoS and Fuzzy attack detection on autonomous vehicle CAN bus. [PDF]

MethodsX
R M, Ramesha SS, Mukhopadhyay A.
europepmc   +1 more source

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

Classical Data in Quantum Machine Learning Algorithms: Amplitude Encoding and the Relation Between Entropy and Linguistic Ambiguity. [PDF]

Entropy (Basel)
Eisinger J, Gauderis W, Huybrecht L, Wiggins GA.   +3 more
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

Quantum machine learning for Lyapunov-stabilized computation offloading in next-generation MEC networks. [PDF]

Sci Rep
Verma VR, Nishad DK, Sharma V, Singh VK, Verma A, Shah DR.   +5 more
europepmc   +1 more source