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Quantum-Chemical Insights from Interpretable Atomistic Neural Networks.

, , , and . Explainable AI, volume 11700 of Lecture Notes in Computer Science, Springer, (2019)

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Machine learning for molecular simulation., , , and . CoRR, (2019)Quantum-informed simulations for mechanics of materials: DFTB+MBD framework., , , , and . CoRR, (2024)Modeling of molecular atomization energies using machine learning., , , and . J. Cheminformatics, 4 (S-1): 33 (2012)sGDML: Constructing accurate and data efficient molecular force fields using machine learning., , , , and . Comput. Phys. Commun., (2019)Learning representations of molecules and materials with atomistic neural networks., , and . CoRR, (2018)Crystal structure evaluation: calculating relative stabilities and other criteria: general discussion, , , , , , , , , and 31 other author(s). Faraday Discussions, (2018)Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, , , and . Phys. Rev. Lett., 108 (5): 058301 (January 2012)Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective., and . CoRR, (2022)Learning Invariant Representations of Molecules for Atomization Energy Prediction., , , , , , , , and . NIPS, page 449-457. (2012)BIGDML: Towards Exact Machine Learning Force Fields for Materials., , , , , and . CoRR, (2021)