Results 171 to 180 of about 15,495,898 (198)

Machine learning potential for modelling H₂ adsorption/diffusion in MOFs with open metal sites. [PDF]

Chem Sci
Liu S, Dupuis R, Fan D, Benzaria S, Bonneau M, Bhatt P, Eddaoudi M, Maurin G.   +7 more
europepmc   +1 more source

Coastal Innovative Learning for Teacher Capacity Building through Local Potential and Environmental Problems on Salemo Island

Eny Syatriana, Muhammad Faisal, Ratnawati Ratnawati   +2 more
openalex   +2 more sources

A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to C_n Clusters for n = 3-10, 20. [PDF]

J Phys Chem A
Gutierrez-Cardenas J, Gibbas BD, Whitaker K, Kaledin M, Kaledin AL.   +4 more
europepmc   +1 more source

Toward Multiscale Modeling of Ni/Molten FLiNaK Interfaces with Equivariant Machine Learning Potentials

Toke Nørremølle Heegaard, Augusta Frost Korsgaard, Renata Sechi, Łukasz Ruszczyński, Tejs Vegge, Esben Klinkby, Klaus Braagaard Møller, José María Castillo-Robles, Ivano Eligio Castelli   +8 more
openalex   +1 more source

Quantitative modeling of phonon-bridge modulated heat transport at GaN/Al interfaces using machine learning potentials

Feng Wang, Shuyu Bi, Jingpei Zhu, Tao Hu, Xiaoxin Zhang, Zhongming Ren   +5 more
openalex   +1 more source

Data and models for "Learning electronic-state-dependent potential-energy surfaces for dislocation dynamics"

Kun Luo, Qi An
openalex   +1 more source

A DEVELOPMENT OF INQUIRY-BASED STUDENT WORKSHEETS (LKPD) TO IMPROVE STUDENTS’ CRITICAL THINKING SKILLS IN SOCIAL STUDIES LEARNING ON THE TOPIC OF CHANGES IN NATURAL RESOURCE POTENTIAL AT SMP NEGERI 55 SURABAYA

Siti Husnia
openalex   +1 more source

Reference-free certification of machine-learning interatomic potentials

Jonas Hänseroth, Christian Dreßler
openalex   +1 more source

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Machine learning for microbiologists

Nature Reviews Microbiology, 2023
Andrew Maltez Thomas, Nicola Segata, Andrea Passerini   +2 more
exaly  

Generalizing from a Few Examples: A Survey on Few-shot Learning

ACM Computing Surveys, 2021
Quanming Yao, Lionel Ni, James Kwok
exaly