Committee Neural Networks for Two-Dimensional Metal-Organic Frameworks

The input & output files and NNP models parameters for the "Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks"

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-01
DOI
https://doi.org/10.5281/zenodo.14002968
Primary Topic
Metal-Organic Frameworks: Synthesis and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Committee Neural Networks for Two-Dimensional Metal-Organic Frameworks

Zenodo (CERN European Organization for Nuclear Research)
Metal-Organic Frameworks: Synthesis and Applications
article

Committee Neural Networks for Two-Dimensional Metal-Organic Frameworks

article en

Abstract

The input & output files and NNP models parameters for the "Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks"

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 73%
Metal-Organic Frameworks: Synthesis and Applications
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Committee Neural Networks for Two-Dimensional Metal-Organic Frameworks · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS