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Chemical Reviews|July 7, 2021
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical SystemsJohn A Keith, Valentin Vassilev-Galindo, Bingqing Cheng, et al.
Scientific Reports|January 10, 2025
Machine learning in the prediction of human wellbeingEkaterina Oparina, Caspar Kaiser, Niccolò Gentile, et al.
The Journal of Physical Chemistry Letters|June 27, 2015
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical SpaceKatja Hansen, Franziska Biegler, Raghunathan Ramakrishnan, et al.
The Journal of Physical Chemistry. C, Nanomaterials and Interfaces|June 20, 2015
Interaction of Isophorone with Pd(111): A Combination of Infrared Reflection-Absorption Spectroscopy, Near-Edge X-ray Absorption Fine Structure, and Density Functional Theory StudiesKarl-Heinz Dostert, Casey P O'Brien, Wiebke Riedel, et al.
Science Advances|January 11, 2023
Accurate global machine learning force fields for molecules with hundreds of atomsStefan Chmiela, Valentin Vassilev-Galindo, Oliver T Unke, et al.
Journal of Chemical Theory and Computation|October 3, 2024
Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for EthanolApurba Nandi, Priyanka Pandey, Paul L Houston, et al.
Nature Communications|June 29, 2022
BIGDML-Towards accurate quantum machine learning force fields for materialsHuziel E Sauceda, Luis E Gálvez-González, Stefan Chmiela, et al.
Journal of Chemical Information and Modeling|February 16, 2026
Improving the Stability and Transferability of Effective ADMET Models by Adding Quantum Mechanical DescriptorsAshmita Bose, Gian-Luca R Anselmetti, Matthias Degroote, et al.
Nature Communications|September 29, 2025
Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactionsMirela Puleva, Leonardo Medrano Sandonas, Balázs D Lőrincz, et al.
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