SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes
J Yu, C Zhang, Y Cheng, YF Yang, YB She, F Liu… - Digital …, 2023 - pubs.rsc.org
Deep learning models based on NLP, mainly the Transformer family, have been
successfully applied to solve many chemistry-related problems, but their applications are …
successfully applied to solve many chemistry-related problems, but their applications are …
Scientific deep machine learning concepts for the prediction of concentration profiles and chemical reaction kinetics: Consideration of reaction conditions
N Adebar, J Keupp, VN Emenike… - The Journal of …, 2024 - ACS Publications
Emerging concepts from scientific deep machine learning such as physics-informed neural
networks (PINNs) enable a data-driven approach for the study of complex kinetic problems …
networks (PINNs) enable a data-driven approach for the study of complex kinetic problems …
Insights into Hildebrand Solubility Parameters–Contributions from Cohesive Energies or Electrophilicity Densities?
We introduce certain concepts and expressions from conceptual density functional theory
(DFT) to study the properties of the Hildebrand solubility parameter. The original form of the …
(DFT) to study the properties of the Hildebrand solubility parameter. The original form of the …
Advancing CO2RR with O-Coordinated Single-Atom Nanozymes: A DFT and Machine Learning Exploration
H Sun, J Liu - ACS Catalysis, 2024 - ACS Publications
Electrochemical CO2 reduction reaction (CO2RR) offers a promising route toward zero-
carbon emissions. Recently emerged single-atom nanozymes (SANs), which combine the …
carbon emissions. Recently emerged single-atom nanozymes (SANs), which combine the …