Paper Citation Record · LEDGER

Active learning and explicit electrostatics enable accurate modeling of electrolytes

As of 7 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2510.03479.

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2510.03479 v3

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Outbound references

Observation 662afebe-e3f2-4354-a6de-c7d63a4d3c8f · outbound

This paper cites Boosting rechargeable batteries R&D by multiscale modeling: myth or reality?.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Boosting rechargeable batteries R&D by multiscale modeling: myth or reality?

Reference 1

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This paper cites CALiSol-23: Experi- mental electrolyte conductivity data for var- ious Li-salts and solvent combinations.

Active learning and explicit electrostatics enable accurate modeling of electrolytes CALiSol-23: Experi- mental electrolyte conductivity data for var- ious Li-salts and solvent combinations

Reference 2

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Observation 92288d13-a601-455c-8ddd-a26dbf6ae833 · outbound

This paper cites Ab initio simulations of liquid electrolytes for energy conversion and storage.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Ab initio simulations of liquid electrolytes for energy conversion and storage

Reference 3

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Observation 20ec5de7-7a5a-425f-9474-fe42fbc3f48f · outbound

This paper cites Lithium ion sol- vation and diffusion in bulk organic elec- trolytes from first-principles and classical re- active molecular dynamics.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Lithium ion sol- vation and diffusion in bulk organic elec- trolytes from first-principles and classical re- active molecular dynamics

Reference 4

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This paper cites The solvation struc- ture, transport properties and reduction be- havior of carbonate-based electrolytes of lithium-ion batteries.

Active learning and explicit electrostatics enable accurate modeling of electrolytes The solvation struc- ture, transport properties and reduction be- havior of carbonate-based electrolytes of lithium-ion batteries

Reference 5

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This paper cites Develop- ment of many- body polarizable force fields for Li-battery components: 1. Ether, Alkane, and carbonate-based solvents.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Develop- ment of many- body polarizable force fields for Li-battery components: 1. Ether, Alkane, and carbonate-based solvents

Reference 6

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This paper cites Quan- tum chemistry and molecular dynamics sim- ulation study of dimethyl carbonate: ethylene carbonate electrolytes doped with LiPF6.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Quan- tum chemistry and molecular dynamics sim- ulation study of dimethyl carbonate: ethylene carbonate electrolytes doped with LiPF6

Reference 7

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This paper cites A foundation model for atomistic materials chemistry.

Active learning and explicit electrostatics enable accurate modeling of electrolytes A foundation model for atomistic materials chemistry

Reference 8

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This paper cites Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Reference 9

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This paper cites High-dimensional neural network potential for liquid electrolyte simulations.

Active learning and explicit electrostatics enable accurate modeling of electrolytes High-dimensional neural network potential for liquid electrolyte simulations

Reference 10

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Observation e6b9ecf5-3952-47d6-be64-b8bd9d57cd11 · outbound

This paper cites Systematic softening in universal machine learning interatomic po- tentials.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Systematic softening in universal machine learning interatomic po- tentials

Reference 11

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This paper cites An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials.

Active learning and explicit electrostatics enable accurate modeling of electrolytes An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials

Reference 12

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This paper cites Universal Machine Learning Potentials under Pressure.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Universal Machine Learning Potentials under Pressure

Reference 13

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This paper cites Machine learn- ing force fields for molecular liquids: Ethy- lene Carbonate/Ethyl Methyl Carbonate bi- nary solvent.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Machine learn- ing force fields for molecular liquids: Ethy- lene Carbonate/Ethyl Methyl Carbonate bi- nary solvent

Reference 14

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Observation 331c8f3b-bf97-45cc-8242-d39067d7bf3e · outbound

This paper cites Transferability of Data Sets between Machine-Learned Inter- atomic Potential Algorithms.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Transferability of Data Sets between Machine-Learned Inter- atomic Potential Algorithms

Reference 15

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This paper cites A fourth-generation high- dimensional neural network potential with accurate electrostatics including non-local charge transfer.

Active learning and explicit electrostatics enable accurate modeling of electrolytes A fourth-generation high- dimensional neural network potential with accurate electrostatics including non-local charge transfer

Reference 16

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This paper cites A deep potential model with long-range electrostatic interactions.

Active learning and explicit electrostatics enable accurate modeling of electrolytes A deep potential model with long-range electrostatic interactions

Reference 17

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This paper cites Phys- Net: A neural network for predicting en- ergies, forces, dipole moments, and partial charges.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Phys- Net: A neural network for predicting en- ergies, forces, dipole moments, and partial charges

Reference 18

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This paper cites The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.

Active learning and explicit electrostatics enable accurate modeling of electrolytes The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

Reference 19

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This paper cites Accurate fourth- generation machine learning potentials by electrostatic embedding.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Accurate fourth- generation machine learning potentials by electrostatic embedding

Reference 20

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Observation dac2a402-8b73-4af2-a5ab-dbbfef8ca388 · outbound

This paper cites Less is more: Sampling chemical space with active learning.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Less is more: Sampling chemical space with active learning

Reference 21

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

Active learning and explicit electrostatics enable accurate modeling of electrolytes On-the-fly ac- tive learning of interpretable Bayesian force fields for atomistic rare events

Reference 22

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This paper cites Training data selection for accuracy and transferability of interatomic potentials.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Training data selection for accuracy and transferability of interatomic potentials

Reference 23

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Active learning of linearly parametrized interatomic potentials

Reference 24

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Accelerating high- throughput searches for new alloys with active learning of interatomic potentials

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Active learning and explicit electrostatics enable accurate modeling of electrolytes How to find a good submatrix

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Active learning and explicit electrostatics enable accurate modeling of electrolytes The MLIP pack- age: moment tensor potentials with MPI and active learning

Reference 27

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Thermophysical proper- ties of molten FLiNaK: A moment tensor po- tential approach

Reference 28

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This paper cites Ring polymer molecu- lar dynamics and active learning of moment tensor potential for gas-phase barrierless re- actions: Application to S+ H2.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Ring polymer molecu- lar dynamics and active learning of moment tensor potential for gas-phase barrierless re- actions: Application to S+ H2

Reference 29

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Accelerating structure pre- diction of molecular crystals using ac- tively trained moment tensor potential

Reference 30

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This paper cites Actively trained magnetic moment tensor potentials for me- chanical, dynamical, and thermal properties of paramagnetic CrN.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Actively trained magnetic moment tensor potentials for me- chanical, dynamical, and thermal properties of paramagnetic CrN

Reference 31

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Bayesian infer- ence of composition-dependent phase dia- grams

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Moment Tensor Poten- tial and Equivariant Tensor Network Poten- tial with explicit dispersion interactions

Reference 33

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Accelerat- ing crystal structure prediction by machine- learning interatomic potentials with active learning

Reference 34

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Accelerating the global search of adsorbate molecule positions using machine-learning interatomic potentials with active learning

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Moment Tensor Potentials: A Class of Systematically Im- provable Interatomic Potentials

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Active learning and explicit electrostatics enable accurate modeling of electrolytes The MLIP package: moment tensor potentials with MPI and ac- tive learning

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Unresolved cited work

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Performance and cost as- sessment of machine learning interatomic po- tentials

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Towards reliable cal- culations of thermal rate constants: Ring polymer molecular dynamics for the OH+ HBr→ Br+ H2O reaction

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Active learning and explicit electrostatics enable accurate modeling of electrolytes The properties of ethylene carbonate and its use in electrochemical ap- plications a literature review

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Active learning and explicit electrostatics enable accurate modeling of electrolytes MLIP-3: Active learning on atomic environments with mo- ment tensor potentials

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Active learning and explicit electrostatics enable accurate modeling of electrolytes SolvationAnaly- sis: A Python toolkit for understanding liq- uid solvation structure in classical molecular dynamics simulations

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Active learning and explicit electrostatics enable accurate modeling of electrolytes MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Change of conductivity with salt content, solvent composition, and tem- perature for electrolytes of LiPF6 in ethylene carbonate-ethyl methyl carbonate

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Effect of salt concentration on properties of mixed carbonate-based electrolyte for Li-ion batter- ies: a molecular dynamics simulation study

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Structure of the Li+ ion close environment in various solvents

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Communica- tion—microscopic view of the ethylene carbonate based lithium-ion battery elec- trolyte by x-ray scattering

Reference 49

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Transport phe- nomena in low temperature lithium-ion bat- tery electrolytes

Reference 50

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Enhanced Ion Solvation and Conductivity in Lithium-Ion Electrolytes via Tailored EMC-TMS Solvent Mixtures: A Molecular Dynamics Study

Reference 51

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Software V ASP, vienna (1999)

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Active learning and explicit electrostatics enable accurate modeling of electrolytes Generalized gradient approxima- tion made simple

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Active learning and explicit electrostatics enable accurate modeling of electrolytes A consistent and accu- rate ab initio parametrization of density func- tional dispersion correction (DFT-D) for the 94 elements H-Pu

Reference 54

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Observation 0efdeb6c-75c8-4ce8-ad33-0dbd08f58b80 · outbound

This paper cites Markoff Random Pro- cesses and the Statistical Mechanics of Time- Dependent Phenomena. II. Irreversible Pro- cesses in Fluids.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Markoff Random Pro- cesses and the Statistical Mechanics of Time- Dependent Phenomena. II. Irreversible Pro- cesses in Fluids

Reference 55

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Observation a6422809-23d7-4a84-8d7c-658e9e9d737d · outbound

This paper cites Statistical-Mechanical The- ory of Irreversible Processes. I. General The- ory and Simple Applications to Magnetic and Conduction Problems.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Statistical-Mechanical The- ory of Irreversible Processes. I. General The- ory and Simple Applications to Magnetic and Conduction Problems

Reference 56

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Observation 85151a84-fca6-416f-8dde-525053f01c24 · outbound

This paper cites Esti- mates of Electrical Conductivity from Molec- ular Dynamics Simulations: How to Invest the Computational Effort.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Esti- mates of Electrical Conductivity from Molec- ular Dynamics Simulations: How to Invest the Computational Effort

Reference 57

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Observation b7ce216d-154d-42f9-9e45-c5bbe15f8904 · outbound

This paper cites Estimating ionic con- ductivity of ionic liquids: Nernst–Einstein and Einstein formalisms.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Estimating ionic con- ductivity of ionic liquids: Nernst–Einstein and Einstein formalisms

Reference 58

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Observation a3642f71-85f2-4817-bbac-8e3d98c41b20 · outbound

This paper cites Best practices for computing transport properties 1. Self- diffusivity and viscosity from equilibrium molecular dynamics [article v1. 0].

Active learning and explicit electrostatics enable accurate modeling of electrolytes Best practices for computing transport properties 1. Self- diffusivity and viscosity from equilibrium molecular dynamics [article v1. 0]

Reference 59

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Pith citing papers

Observation 4b32c7b8-1e2b-4317-a3c1-719144bd6ebd · inbound

Fragment-Constrained Charge Equilibration for Charge-Aware Machine Learning Potentials at Electrochemical Interfaces cites this paper.

Fragment-Constrained Charge Equilibration for Charge-Aware Machine Learning Potentials at Electrochemical Interfaces Active learning and explicit electrostatics enable accurate modeling of electrolytes

Reference 43

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