Results 171 to 180 of about 336,714 (315)

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

The Influence of the Site of Recording and Benchtop and Portable NIRS Equipment on Predicting the Sensory Properties of Iberian Ham. [PDF]

Foods
Revilla I, Vivar-Quintana AM, Martínez-Martín I, Hernández-Ramos P, Hernández-Jiménez M, Grabska J, Beć KB, Huck CW.   +7 more
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

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Hybridizing deep learning algorithms and geostatistical approaches for improved crop yield disaggregation. [PDF]

PLoS One
R S, Jain R, Singh VK, Bharadwaj A, Sehgal VK, Biswas A, Arora A, Krishna H.   +7 more
europepmc   +1 more source

Trajectory Root Mean Square Error (RMSE) distributions of three optimization methods: (A) SQ refers to leastsq(), (B) LM refers to least_squares(method = ‘lm’), and (C) TRF refers to least_squares(method=’trf’).

Yue Huang (285485), Tianqi Tang (6535856), Xiaowu Dai (10073580), Fengzhu Sun (15206)   +3 more
openalex   +1 more source

Peer Review #2 of "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation (v0.2)"

, 2021
Davide Chicco, Matthijs J. Warrens, Giuseppe Jurman, D Allen, M Allen, N Altman, M Krzywinski, R Applegate, C Ballentine, H Gross, E Sarver, C Sarver, J Armstrong, F Collopy, G Barrett, J Barrett, P Bartlett, P Long, G Lugosi, A Tsigler, R Berk, N Blomquist, A Botchkarev, A Botchkarev, L Breiman, S Buuren, K Groothuis-Oudshoorn, T Chai, R Draxler, S Chan, J Chu, Y Zhang, S Nadarajah, C Chen, J Twycross, J Garibaldi, D Chicco, G Jurman, D Chicco, G Jurman, D Chicco, V Starovoitov, G Jurman, D Chicco, N Ttsch, G Jurman, D Chicco, M Warrens, G Jurman, J Cornell, R Berger, D Cox, N Wermuth, I David, B Sukhatme, E De-La-Hoz-Correa, F Mendoza-Palechor, A De-La-Hoz-Manotas, R Morales-Ortega, Adriana, S, A De Myttenaere, B Golden, B Le Grand, F Rossi, A De Myttenaere, B Golden, B Le Grand, F Rossi, A Di Bucchianico, E Dougherty, S Kim, Y Chen, R Farebrother, B Flores, E Gambhir, R Jain, A Gupta, U Tomer, E Gilroy, R Hirsch, T Cohn, M Golberg, H Cho, P Goodwin, R Lawton, G Hahn, G Hancock, M Freeman, K Hannay, G Hoffmann, A Bietenbeck, R Lichtinghagen, F Klawonn, P Huber, R Hyndman, A Koehler, R Ihaka, R Gentleman, K Jaqaman   +99 more
openalex   +2 more sources

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

Extensions to Extended Tight-Binding Methods for Transition-Metal Containing Systems. [PDF]

J Comput Chem
Moradi S, Tomann R, Head-Gordon M, Stein CJ.   +3 more
europepmc   +1 more source

Ablation studies of the CNN-BiGRU model optimized via the GWO algorithm across LendingClub Loan Data and German Credit Data. The evaluation metrics include MAE, MAPE, RMSE, and MSE.

Yetong Fang (21433414)
openalex   +1 more source