# publications (ML/GNN)

publications by categories in reversed chronological order. generated by jekyll-scholar.

## 2024

- ML/GNNFrom Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
*arXiv preprint arXiv:2310.16802*, 2024 - ML/GNNThe Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
*ACS Central Science*, 2024 - ML/GNNFine-Tuned Language Models Generate Stable Inorganic Materials as Text
*ICLR*, 2024 - ML/GNNGeneralization of graph-based active learning relaxation strategies across materials
*Machine Learning: Science and Technology*, 2024

## 2023

- ML/GNNBeyond independent error assumptions in large GNN atomistic models
*The Journal of Chemical Physics*, 2023 - ML/GNNAmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification
*Journal of Open Source Software*, 2023 - ML/GNNChemical Properties from Graph Neural Network-Predicted Electron Densities
*The Journal of Physical Chemistry C*, 2023

## 2022

- ML/GNNHow Do Graph Networks Generalize to Large and Diverse Molecular Systems?
*arXiv preprint arXiv:2204.02782*, 2022 - ML/GNNTransfer learning using attentions across atomic systems with graph neural networks (TAAG)
*The Journal of Chemical Physics*, 2022 - ML/GNNSpherical Channels for Modeling Atomic Interactions
*NeurIPS*, Dec 2022 - ML/GNNRobust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
*Machine Learning: Science and Technology*, Dec 2022

## 2021

- ML/GNNRotation Invariant Graph Neural Networks using Spin Convolutions
*arXiv preprint arXiv:2106.09575*, Dec 2021

## 2020

- ML/GNNMethods for comparing uncertainty quantifications for material property predictions
*Machine Learning: Science and Technology*, May 2020

## 2019

- ML/GNNConvolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts
*The Journal of Physical Chemistry Letters*, Jul 2019