Formation Energy
29 papers with code • 14 benchmarks • 8 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.
Benchmarks
These leaderboards are used to track progress in Formation Energy
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Libraries
Use these libraries to find Formation Energy models and implementations
Materials Project
MD17
QM9
OQMD v1.2
OQM9HK
WBM
Latest papers
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2. 27 eV and a permittivity of 20. 5, meeting all target metrics of our multi-objective search.
Crystal-GFN: sampling crystals with desirable properties and constraints
alexhernandezgarcia/gflownet
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Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
ACEsuit/mace
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The top 3 models are UIPs, the winning methodology for ML-guided materials discovery, achieving F1 scores of ~0. 6 for crystal stability classification and discovery acceleration factors (DAF) of up to 5x on the first 10k most stable predictions compared to dummy selection from our test set.
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
divelab/AIRS
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This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
torchmd/torchmd-net
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NeurIPS 2023
The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.
Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability
We compare the performance of xGPR with the reported performance of various deep learning models on 20 benchmarks, including small molecule, protein sequence and tabular data.
Discovery of 2D materials using Transformer Network based Generative Design
Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications.
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
kdmsit/crysgnn
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To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge amount of unlabelled material data.
Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models.
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials Science
Tony-Y/cgnn
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Technical report, RIMCS LLC 2022
We introduce a large-scale dataset of quantum-mechanically calculated properties of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK).
