Research Topic
active learning
11 publications exploring this topic
2025
Machine Learning anomaly detection of automated HPLC experiments in the Cloud Laboratory
(2025)
Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility.
Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential
(2025)
Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge.
2023
Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling
J. Chem. Inf. Model., 63, 583–594 (2023)
Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling.
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
(2023)
Abstract Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery.
2022
Active learning guided drug design lead optimization based on relative binding free energy modeling
(2022)
In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE).
2021
Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
Advanced Intelligent Systems, 3 (2021)
The bandgap is one of the most fundamental properties of condensed matter.
2020
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
Scientific Data, 7 (2020)
Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2020)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2020)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
2019
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2019)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
2018
Less is more: Sampling chemical space with active learning
The Journal of Chemical Physics, 148 (2018)
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task.