Machine learning for the advancement of membrane science and technology: A critical review
Abstract Machine learning (ML) has been rapidly transforming the landscape of natural
sciences and has the potential to revolutionize the process of data analysis and hypothesis …
sciences and has the potential to revolutionize the process of data analysis and hypothesis …
[HTML][HTML] Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design
The expansion of the chemical space to tangible libraries containing billions of
synthesizable molecules opens exciting opportunities for drug discovery, but also …
synthesizable molecules opens exciting opportunities for drug discovery, but also …
When do quantum mechanical descriptors help graph neural networks to predict chemical properties?
Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …
molecular properties. However, because of the complexity of chemical space, such models …
Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy
Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger
RNA delivery technology. Here, to advance the identification of ionizable lipids beyond …
RNA delivery technology. Here, to advance the identification of ionizable lipids beyond …
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision …
ADMETlab 3.0 is the second updated version of the web server that provides a
comprehensive and efficient platform for evaluating ADMET-related parameters as well as …
comprehensive and efficient platform for evaluating ADMET-related parameters as well as …
Machine Learning-Assisted High-Donor-Number Electrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion Batteries
The utilization of electrolyte additives has been regarded as an efficient strategy to construct
dendrite-free aqueous zinc-ion batteries (AZIBs). However, the blurry screening criteria and …
dendrite-free aqueous zinc-ion batteries (AZIBs). However, the blurry screening criteria and …
Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
We investigate the potential of graph neural networks for transfer learning and improving
molecular property prediction on sparse and expensive to acquire high-fidelity data by …
molecular property prediction on sparse and expensive to acquire high-fidelity data by …
Machine-Learning-Assisted Design of Buried-Interface Engineering Materials for High-Efficiency and Stable Perovskite Solar Cells
Buried-interface engineering is crucial to the performance of perovskite solar cells. Self-
assembled monolayers and buffer layers at the buried interface can optimize charge transfer …
assembled monolayers and buffer layers at the buried interface can optimize charge transfer …
ROBERT: bridging the gap between machine learning and chemistry
Beyond addressing technological demands, the integration of machine learning (ML) into
human societies has also promoted sustainability through the adoption of digitalized …
human societies has also promoted sustainability through the adoption of digitalized …
Thermodynamics-consistent graph neural networks
We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting
composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture …
composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture …