mlsanityData to 7 Oct 2026

DScribe: Library of Descriptors for Machine Learning in Materials Science

arXiv 1904.08875 ↗First posted 18 Apr 2019cond-mat.mtrl-sci cs.LGcited by 63

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Abstract (arXiv)

DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2019.

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Citing papers per year: 2021: 5; 2022: 12; 2023: 10; 2024: 11; 2025: 11; 2026: 14.

63 papers.

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  1. 2022The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentialscited by 100
  2. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  3. 2022MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Predictioncited by 28
  4. 2021Differentiable sampling of molecular geometries with uncertainty-based adversarial attackscited by 25
  5. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 24
  6. 2025MatterChat: A Multi-Modal LLM for Material Sciencecited by 18
  7. 2022Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systemscited by 16
  8. 2021Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structurecited by 15
  9. 2023Machine learning for phase ordering dynamics of charge density wavescited by 15
  10. 2022Capturing long-range interaction with reciprocal space neural networkcited by 13
  11. 2022Machine learning predictions for local electronic properties of disordered correlated electron systemscited by 12
  12. 2023Kernel quadrature with randomly pivoted Choleskycited by 11
38 more
  1. 2023MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflowscited by 10
  2. 2024Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approachcited by 10
  3. 2024Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulationscited by 9
  4. 2023Instructor-inspired Machine Learning for Robust Molecular Property Predictioncited by 8
  5. 2024Multitask methods for predicting molecular properties from heterogeneous datacited by 8
  6. 2023Fast evaluation of spherical harmonics with sphericartcited by 6
  7. 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
  8. 2021Optimal radial basis for density-based atomic representationscited by 4
  9. 2021Complex Spin Hamiltonian Represented by Artificial Neural Networkcited by 4
  10. 2022A smooth basis for atomistic machine learningcited by 4
  11. 2024Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalancecited by 4
  12. 2025LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modelingcited by 4
  13. 2026Machine-learning force-field models for dynamical simulations of metallic magnetscited by 3
  14. 2026Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPscited by 3
  15. 2024Transfer Learning for Molecular Property Predictions from Small Data Setscited by 2
  16. 2025Unsupervised Machine-Learning Pipeline for Data-Driven Defect Detection and Characterisation: Application to Displacement Cascadescited by 2
  17. 2025Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spacescited by 2
  18. 2026Equivariant Neural Networks for Force-Field Models of Lattice Systemscited by 2
  19. 2026CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Modelscited by 2
  20. 2026Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPscited by 2
  21. 2026ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environmentscited by 2
  22. 2021Surrogate-Based Black-Box Optimization Method for Costly Molecular Propertiescited by 1
  23. 2022Persistent homology-based descriptor for machine-learning potential of amorphous structurescited by 1
  24. 2022Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processescited by 1
  25. 2022Machine learning frontier orbital energies of nanodiamondscited by 1
  26. 2023Machine learning for structure-property relationships: Scalability and limitationscited by 1
  27. 2023From structure mining to unsupervised exploration of atomic octahedral networkscited by 1
  28. 2023Machine learning force-field models for metallic spin glasscited by 1
  29. 2024Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentialscited by 1
  30. 2025Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloyscited by 1
  31. 2025Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantificationcited by 1
  32. 2026Machine learning nonequilibrium phase transitions in charge-density wave insulatorscited by 1
  33. 2026Graph neural network force fields for adiabatic dynamics of lattice Hamiltonianscited by 1
  34. 2026Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systemscited by 1
  35. 2022Machine Learning Diffusion Monte Carlo Energiescited by 0
  36. 2022Lagrangian Density Space-Time Deep Neural Network Topologycited by 0
  37. 2023Knowledge Discovery from Atomic Structures using Feature Importancescited by 0
  38. 2023Chemellia: An Ecosystem for Atomistic Scientific Machine Learningcited by 0

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