GNNs are shown to lack continuity under graph resolution changes due to message-passing schemes, with a derived modification enabling consistent multi-scale representations validated experimentally.
Title resolution pending
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
citation-role summary
background 2
baseline 1
citation-polarity summary
years
2026 9representative citing papers
Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.
Non-autoregressive ionic transport predictor learns dynamics from auxiliary trajectory data during training only, achieving over 200x speedup versus autoregressive models and lower error than non-autoregressive baselines on both dataset types.
A physics-informed autoencoder compresses 3D charge density into a 16x16x16x16 latent representation that, combined with MAGPIE descriptors, predicts bulk modulus, Young's modulus, shear modulus, formation energy, and Debye temperature with R2 values of 0.94, 0.88, 0.87, 0.96, and 0.89 on 6059 DFT-s
LEGO-MOF maps MOF linkers to an equivariant latent space for continuous editing and uses test-time optimization to achieve a 147.5% average boost in pure CO2 uptake while preserving structural validity.
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
Spatial statistics on voxelized structures using FFT correlations and PCA yield low-dimensional convex features that support accurate predictions with as few as 10 training samples.
A crystal fractional graph neural network fuses local graph attention on 16-atom environments with global composition fractions to predict high-entropy alloy energies at RMSE levels comparable to first-principles calculations on quaternary test structures.
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
citing papers explorer
-
Graph Neural Networks Are Not Continuous Across Graph Resolutions
GNNs are shown to lack continuity under graph resolution changes due to message-passing schemes, with a derived modification enabling consistent multi-scale representations validated experimentally.
-
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.
-
Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor
Non-autoregressive ionic transport predictor learns dynamics from auxiliary trajectory data during training only, achieving over 200x speedup versus autoregressive models and lower error than non-autoregressive baselines on both dataset types.
-
Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction
A physics-informed autoencoder compresses 3D charge density into a 16x16x16x16 latent representation that, combined with MAGPIE descriptors, predicts bulk modulus, Young's modulus, shear modulus, formation energy, and Debye temperature with R2 values of 0.94, 0.88, 0.87, 0.96, and 0.89 on 6059 DFT-s
-
LEGO-MOF: Equivariant Latent Manipulation for Editable, Generative, and Optimizable MOF Design
LEGO-MOF maps MOF linkers to an equivariant latent space for continuous editing and uses test-time optimization to achieve a 147.5% average boost in pure CO2 uptake while preserving structural validity.
-
Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
-
Spatial statistics for screening molecular structures
Spatial statistics on voxelized structures using FFT correlations and PCA yield low-dimensional convex features that support accurate predictions with as few as 10 training samples.
-
Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys
A crystal fractional graph neural network fuses local graph attention on 16-atom environments with global composition fractions to predict high-entropy alloy energies at RMSE levels comparable to first-principles calculations on quaternary test structures.
-
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.