Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials | Paper Citation Record

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This paper cites Podryabinkin, A.V.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Podryabinkin, A.V

Reference 1

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This paper cites Bernstein, G.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Bernstein, G

Reference 2

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This paper cites Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

Reference 3

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This paper cites Lysogorskiy, A.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Lysogorskiy, A

Reference 4

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Observation 18e9595f-e32f-4f29-9c89-f9386be5b3ae · outbound

This paper cites On-the-fly machine learning force field generation: Application to melting points.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials On-the-fly machine learning force field generation: Application to melting points

Reference 5

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Observation a1357bd8-a977-4fdc-8ca4-a18f665bc419 · outbound

This paper cites On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Reference 6

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This paper cites Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics

Reference 7

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This paper cites Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

Reference 8

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Observation 9ed8c5f7-9388-4ced-8738-a154ea1d8a6f · outbound

This paper cites Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC

Reference 9

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Observation 8598a944-e29e-4c8b-b641-247ba50fd878 · outbound

This paper cites An entropy-maximization approach to automated training set generation for interatomic potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials An entropy-maximization approach to automated training set generation for interatomic potentials

Reference 10

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Observation 26f89fea-0e60-42e8-b55c-22511428a6f4 · outbound

This paper cites Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

Reference 11

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Observation 2c5e736e-720b-4ad9-8bd7-874eee61897a · outbound

This paper cites High-dimensional neural network potentials for metal surfaces: A prototype study for copper.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials High-dimensional neural network potentials for metal surfaces: A prototype study for copper

Reference 12

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This paper cites Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes

Reference 13

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This paper cites Metadynamics for training neural network model chemistries: A competi- tive assessment.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Metadynamics for training neural network model chemistries: A competi- tive assessment

Reference 14

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This paper cites Uncertainty-driven dynamics for active learning of interatomic potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Uncertainty-driven dynamics for active learning of interatomic potentials

Reference 15

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This paper cites Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials

Reference 16

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Observation 09d8ad31-98b2-4f34-92f3-7a650c2e9fb4 · outbound

This paper cites Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

Reference 17

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This paper cites Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Reference 18

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This paper cites ArXiv abs/2506.17139.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials ArXiv abs/2506.17139

Reference 19

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This paper cites Flow matching for accelerated simulation of atomic transport in crystalline materials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Flow matching for accelerated simulation of atomic transport in crystalline materials

Reference 20

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Observation ac9c9d50-6ca4-4a15-8579-89f257710c68 · outbound

This paper cites Empirical interatomic potential for silicon with improved elastic properties.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Empirical interatomic potential for silicon with improved elastic properties

Reference 21

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Observation a6b9a734-a7a2-4853-8b86-499dddf1b194 · outbound

This paper cites Computer simulation of local order in condensed phases of silicon.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Computer simulation of local order in condensed phases of silicon

Reference 22

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This paper cites Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals.Physical Review B, 29(12):6443, 1984.doi:10.1103/PhysRevB.29.6443.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals.Physical Review B, 29(12):6443, 1984.doi:10.1103/PhysRevB.29.6443

Reference 23

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This paper cites Modified embedded atom potentials for HCP metals.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Modified embedded atom potentials for HCP metals

Reference 24

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This paper cites E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Reference 25

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This paper cites Learning local equivariant representations for large-scale atomistic dynamics.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Learning local equivariant representations for large-scale atomistic dynamics

Reference 26

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Observation d96d4f61-482d-44ce-a83b-f7647187c5aa · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 27

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Observation 3acc2f67-e0c7-4b74-b4e0-90fd8b0825bf · outbound

This paper cites BeyondBOLSIG+:MonteCarlosimulation of electron and ion swarms to obtain transport and rate coefficients forplasmamodeling.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials BeyondBOLSIG+:MonteCarlosimulation of electron and ion swarms to obtain transport and rate coefficients forplasmamodeling

Reference 28

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This paper cites Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons

Reference 29

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Observation f78446fd-3f0a-4e38-831b-02472e73933e · outbound

This paper cites Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Reference 30

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Observation e5388890-8dc3-48d6-920c-1a09d93726aa · outbound

This paper cites Atomic cluster expansion for accurate and transferable interatomic potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Atomic cluster expansion for accurate and transferable interatomic potentials

Reference 31

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This paper cites Lelievre, M.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Lelievre, M

Reference 32

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This paper cites Enhanced Sampling Methods for Molecular Dynamics Simulations.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Enhanced Sampling Methods for Molecular Dynamics Simulations

Reference 33

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This paper cites A bound for the error in the normal approximation to the distribution of a sum of dependent random variables.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials A bound for the error in the normal approximation to the distribution of a sum of dependent random variables

Reference 34

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This paper cites Stein variational gradient descent as gradient flow.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Stein variational gradient descent as gradient flow

Reference 35

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This paper cites On the Mean-Field Limit of Stein Variational Gradient Descent.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials On the Mean-Field Limit of Stein Variational Gradient Descent

Reference 36

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This paper cites Thompson, H.M.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Thompson, H.M

Reference 37

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Observation 096eaf80-143c-4e9b-ba4c-15c7f57b25b5 · outbound

This paper cites Stochastic Gradient MCMC with Repulsive Forces.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Stochastic Gradient MCMC with Repulsive Forces

Reference 38

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Observation 145848d0-42a9-4496-a810-af238282fc47 · outbound

This paper cites On the geometry of Stein variational gradient descent.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials On the geometry of Stein variational gradient descent

Reference 39

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Observation a0097db2-0de2-4bef-9922-a363931608d3 · outbound

This paper cites Stein Self-Repulsive Dynamics: Benefits From Past Samples.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Stein Self-Repulsive Dynamics: Benefits From Past Samples

Reference 40

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Observation a40d8bde-89b7-4ba2-b3fb-21a77a87a5f0 · outbound

This paper cites Bayesian experimental design using regularized determinantal point processes.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Bayesian experimental design using regularized determinantal point processes

Reference 41

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This paper cites Stein Points.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Stein Points

Reference 42

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This paper cites A Stein variational Newton method.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials A Stein variational Newton method

Reference 43

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Observation 4fc4095a-c76f-4065-8cfd-467ec7a48208 · outbound

This paper cites Stein variational gradient descent with matrix-valued kernels.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Stein variational gradient descent with matrix-valued kernels

Reference 44

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This paper cites A stochastic version of Stein variational gradient descent for efficient sampling.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials A stochastic version of Stein variational gradient descent for efficient sampling

Reference 45

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Observation 0aa6f3a3-7d11-45d9-bcf7-845ee6b1799e · outbound

This paper cites Multilevel Stein variational gradient descent with applications to Bayesian inverse problems.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Multilevel Stein variational gradient descent with applications to Bayesian inverse problems

Reference 46

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Observation 2ea27bee-279d-4c5d-9140-0875b71ff936 · outbound

This paper cites p-Kernel Stein Variational Gradient Descent for Data Assimilation and History Matching.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials p-Kernel Stein Variational Gradient Descent for Data Assimilation and History Matching

Reference 47

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This paper cites ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs

Reference 48

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Observation 420e22e8-196d-411e-be1d-04af1233c5b5 · outbound

This paper cites Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Reference 49

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Observation 1d2f6a1e-f7b7-460f-9369-6109546c616a · outbound

This paper cites Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials

Reference 50

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Observation 1addf9a3-b13d-4a20-918d-9a301f276973 · outbound

This paper cites P.et al.MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.J.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials P.et al.MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.J

Reference 51

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This paper cites Dotson, Raimondas Galvelis, John E.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Dotson, Raimondas Galvelis, John E

Reference 52

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Observation c6086e1a-9199-426b-9192-231b23101991 · outbound

This paper cites Early Application Experiences on Aurora at ALCF: Moving From Petascale to Exascale Systems.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Early Application Experiences on Aurora at ALCF: Moving From Petascale to Exascale Systems

Reference 53

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Observation 54a55ff3-eb09-4fc5-8370-0bc040c6804d · outbound

This paper cites Calculating free energies using average force.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Calculating free energies using average force

Reference 54

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Observation 3688a778-39f7-4f16-8ce3-96a9c961a680 · outbound

This paper cites Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods

Reference 55

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This paper cites Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions

Reference 56

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This paper cites Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method

Reference 57

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This paper cites Schirhagl, K.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Schirhagl, K

Reference 58

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This paper cites Lectures in Mathematics ETH Zürich.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Lectures in Mathematics ETH Zürich

Reference 59

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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Super-Samples from Kernel Herding

Reference 60

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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Projected support points: a new method for high-dimensional data reduction

Reference 61

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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Support Points

Reference 62

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This paper cites Kernel Thinning.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Kernel Thinning

Reference 63

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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Compress Then Test: Powerful Kernel Testing in Near-linear Time

Reference 64

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This paper cites A coreset selection of coreset selection literature: Introduction and recent advances.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials A coreset selection of coreset selection literature: Introduction and recent advances

Reference 65

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials LoRA: Low-Rank Adaptation of Large Language Models

Reference 66

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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials Unresolved cited work

Reference 67

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