PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry

By Maho Nakata, T. Shimazaki. 2017

322 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Weihua Hu
2. Jure Leskovec
3. Yuxiao Dong
4. Matthias Fey
5. Hongyu Ren
6. Marinka Zitnik

In Materials Chemistry

1. Jure Leskovec
2. Yuxiao Dong
3. Matthias Fey
4. Hongyu Ren
5. Weihua Hu

In Spectroscopy

1. Jure Leskovec

Citations in the corpus

• EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations (2023, unknown): “However, the issue can be mitigated by pre-training on large datasets like OC20 [30] and PCQM4Mv2 [67] optionally via denoising [68] and then finetuning on smaller datasets.”
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs (2021, methodology): “Note that the HOMO-LUMO gap was calculated by DFT based on the final 3D structures (Nakata and Shimazaki, 2017); hence, it makes more sense to correspond the HOMO-LUMO gap with the SMILES string associated with the final 3D structures.”
• QMugs, quantum mechanical properties of drug-like molecules (2022, unknown): “To determine suitable thresholds for removing a structure from our dataset, the generated geometries were compared to experimental reference values and to DFT-optimized geometries extracted from the PubChemQC dataset 20 .”
• Chemprop: A Machine Learning Package for Chemical Property Prediction (2023, methodology): “The PCQM4MV2 dataset is a collection of DFT-calculated molecular HOMO-LUMO gaps, originally collected as part of the PubChemQC project 32 3 Benchmark results”