mlsanityData to 14 Oct 2026

Hierarchical modeling of molecular energies using a deep neural network

arXiv 1710.00017 ↗First posted 29 Sep 2017stat.ML physics.chem-phcited by 37

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Abstract (arXiv)

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network--a composition of many nonlinear transformations--acting on a representation of the molecule. HIP-NN achieves state-of-the-art performance on a dataset of 131k ground state organic molecules, and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Sep 2017.

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Citing papers per year: 2021: 9; 2022: 10; 2023: 7; 2024: 5; 2025: 3; 2026: 3.

37 papers.

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  1. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  2. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
  3. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
  4. 2022So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systemscited by 39
  5. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  6. 2022Graph Neural Networks for Moleculescited by 21
  7. 2021Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentialscited by 15
  8. 2023Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentialscited by 11
  9. 2023Data efficiency and extrapolation trends in neural network interatomic potentialscited by 10
  10. 2021Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Momentscited by 9
  11. 2021Geometric Transformer for End-to-End Molecule Properties Predictioncited by 8
  12. 2024FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentialscited by 6
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  1. 2026Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentialscited by 4
  2. 2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Spacecited by 3
  3. 2022Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistrycited by 3
  4. 2022Synthetic data enable experiments in atomistic machine learningcited by 3
  5. 2025Equivariant Spherical Transformer for Efficient Molecular Modelingcited by 3
  6. 2021A Universal Framework for Featurization of Atomistic Systemscited by 2
  7. 2022Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergencecited by 2
  8. 2024Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterialscited by 2
  9. 2024Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networkscited by 2
  10. 2024FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUscited by 2
  11. 2021Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systemscited by 1
  12. 2022EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Acceleratorscited by 1
  13. 2022Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reactioncited by 1
  14. 2022Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineeringcited by 1
  15. 2023PANNA 2.0: Efficient neural network interatomic potentials and new architecturescited by 1
  16. 2023From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fieldscited by 1
  17. 2025Optimal Invariant Bases for Atomistic Machine Learningcited by 1
  18. 2021A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurizationcited by 0
  19. 2023HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networkscited by 0
  20. 2023Spline-based neural network interatomic potentials: blending classical and machine learning modelscited by 0
  21. 2023Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Watercited by 0
  22. 2024Pretraining Strategy for Neural Potentialscited by 0
  23. 2025Ensemble Knowledge Distillation for Machine Learning Interatomic Potentialscited by 0
  24. 2026Magnetic HIP-NN for spin dynamics in disordered itinerant magnetscited by 0
  25. 2026Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnetscited by 0