Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening

no code implementations • 20 Sep 2023 • Zhonglin Cao, Simone Sciabola, Ye Wang

Accurate model can achieve high sample efficiency by finding the most promising compounds with only a fraction of the whole library being virtually screened.

Active Learning Bayesian Optimization +2

Neural Network Predicts Ion Concentration Profiles under Nanoconfinement

1 code implementation • 10 Apr 2023 • Zhonglin Cao, Yuyang Wang, Cooper Lorsung, Amir Barati Farimani

Overall, our deep learning model is a fast, flexible, and accurate surrogate model to predict ion concentration profiles in nanoconfinement.

MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction

1 code implementation • 25 Oct 2022 • Zhonglin Cao, Rishikesh Magar, Yuyang Wang, Amir Barati Farimani

Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited.

Property Prediction Self-Supervised Learning

Molecular Contrastive Learning of Representations via Graph Neural Networks

1 code implementation • 19 Feb 2021 • Yuyang Wang, Jianren Wang, Zhonglin Cao, Amir Barati Farimani

In this work, we present MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks (GNNs), a self-supervised learning framework that leverages large unlabeled data (~10M unique molecules).

BIG-bench Machine Learning Contrastive Learning +4

Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

no code implementations • 19 Jan 2021 • Yuyang Wang, Zhonglin Cao, Amir Barati Farimani

Structure and geometry optimization of nanopores on such materials is beneficial for their performances in real-world engineering applications, like water desalination.

reinforcement-learning Reinforcement Learning (RL)