Home Research Papers Datasets State of the Art Pricing

Discover, visualize, and connect AI research papers. Explore the latest trends and insights in artificial intelligence research.

Product

Home
Research Papers
About

Support

Contact
Terms of Service
Privacy Policy

miscellaneous-3

Formation Energy

3260 papers • 126 benchmarks • 313 datasets

On the QM9 dataset the numbers reported in the table are the mean absolute error in eV on the target variable U0 divided by U0's chemical accuracy, which is equal to 0.043.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in formation-energy-3

Trend

Dataset

Best Model

Actions

QM9

Materials Project

OQMD v1.2

Libraries

Use these libraries to find formation-energy-3 models and implementations

Tony-Y/cgnn

5 papers 98

Datasets

QM9

Subtasks

No subtasks available.

Most implemented papers

Neural Message Passing for Quantum Chemistry

O. Vinyals, J. Gilmer, George E. Dahl, S. Schoenholz, Patrick F. Riley•Mon Apr 03 2017

Using MPNNs, state of the art results on an important molecular property prediction benchmark are demonstrated and it is believed future work should focus on datasets with larger molecules or more accurate ground truth labels.

8540

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

JARVIS-DFT

OQM9HK

Acetylacetone

3BPA

Aspirin

Ethanol

Naphthalene

Salicylic Acid

LiPS

LiPS20

GeTe

peterbjorgensen/msgnet

3 papers 12

atomistic-machine-learning/schnetpa…

2 papers 733

nrel/m2p

2 papers 23

OQM9HK

WBM

Paper Graph

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

A. Tkatchenko, Stefan Chmiela, K. Müller, Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix•Sun Jun 25 2017

This work proposes to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid, and obtains a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles.

1298 0

Paper Graph

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

P. B. Jørgensen, K. Jacobsen, Mikkel N. Schmidt•Thu May 31 2018

This work extends the neural message passing model with an edge update network which allows the information exchanged between atoms to depend on the hidden state of the receiving atom.

85 0

Paper Graph

Directional Message Passing for Molecular Graphs

Stephan Günnemann, Johannes Klicpera, Janek Groß•Thu Mar 05 2020

This work proposes a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them, and uses spherical Bessel functions to construct a theoretically well-founded, orthogonal radial basis that achieves better performance than the currently prevalent Gaussian radial basis functions while using more than 4x fewer parameters.

1024 0

Paper Graph

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Chi Chen, Yunxing Zuo, Weike Ye, S. Ong, Chen Zheng•Tue Dec 11 2018

This work develops, for the first time, universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals and demonstrates the transfer learning of elemental embeddings from a property model trained on a larger data set to accelerate the training of property models with smaller amounts of data.

1189 0

Paper Graph

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

Yang Liu, Shuo Zhang, Lei Xie•Sat Nov 14 2020

This work designs a GNN which is both powerful and efficient for molecule structures and builds Multiplex Molecular Graph Neural Network (MXMNet), a molecular mechanics-driven approach which achieves superior results to the existing state-of-the-art models under restricted resources.

66 0

Paper Graph

Adding a benchmark result helps the community track progress.

Formation Energy | State-of-the-Art