An adaptive fine-tuning workflow for foundation-model MLIPs enables efficient CSP in the CaFeNi ternary, reproducing the low-pressure hull and predicting a new phase Ca6FeNi stable above 100 GPa.
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Constructs G-equivariant ViTs for arbitrary discrete G ≤ O(2), proves H ≤ G implies G-models embed into H-models and single-head equivariant attention realizes all ordinary G-equivariant maps, introduces D6 hexagonal model, and reports preliminary accuracy gains on PatternNet in low-data regimes.
Presents an end-to-end constraint-aware quantum optimization pipeline using XY-mixer QAOA and Grover Adaptive Search for low-energy defect configurations in doped ZrO2, with QAOA validated against exact enumeration on a high-accuracy QUBO surrogate of MACE energies.
mHIP-NN extends hierarchical message-passing networks to model electron-mediated spin dynamics in disordered itinerant magnets while preserving rotational symmetry.
Ion irradiation creates a layer-disordered phase with planar defects in MnBi2Te4 that suppresses symmetry and anomalous Hall conductivity fivefold while magnetic order persists.
An O(L^3) algorithm computes contracted Clebsch-Gordan tensor products for equivariant ML potentials using a structured angular grid and spherical Poisson bracket to handle parity-odd terms at fixed CP rank.
QT-Net predicts atomic electron populations and multipoles via a new SOAP-cluster held-out test, improving molecular property prediction and recovering QM9 dipole moments from per-atom outputs.
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
A flexible interface in GROMACS integrates PyTorch neural network potentials into hybrid ML/MM molecular dynamics simulations for biomolecular systems.
A transfer compression technique using semi-empirical data reduces molecular representation dimensions by a median 72% (range 36-98%) while retaining accuracy for energy, heat capacity, dipole moment and polarizability on QM9 and VQM24, and improves data efficiency for dipoles to 19% of training dat
Pre-registered validation of an ML Na-cathode voltage screen yields 0.67 V MAE against experiment, with Materials Project PBE+U references 0.54 V low and dominating the error.
Multitask learning on linear-scaling GFN1-xTB orbital charges cuts energy MAE by 46% and data needs by 5x versus energy-only MLIPs while outperforming DFT atomic charge augmentation.
Machine learning force field molecular dynamics simulations reveal anisotropic crystallization in Sb2S3 with [100] facet fastest growth and interface-controlled kinetics with activation energy 0.55-0.57 eV.
A reweighting method with mean energy-gap approximation transfers PMFs between MLIPs to recover target reaction and activation free energies at low cost for a 601-atom Li+ transport system across DFT levels.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
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.
dm-PhiSNet predicts 1-RDMs from geometries via equivariant PhiSNet with two-stage training and analytic refinement, reducing SCF iterations 49-81% on six closed-shell molecules while giving accurate one-shot energies and forces without force supervision.
A Poisson-coupled neural field predicts electron density through the Hartree potential, producing one-shot Kohn-Sham matrices that accelerate and transfer SCF calculations.
QCOF ML potentials tuned on COF data outperform general MACE models for defective systems and reveal higher thermal defect sensitivity in CTF-1 versus COF-LZU1 with nearly invariant low-strain mechanics.
Neural networks trained on molecular configurations from different force fields classify ZIF polymorph phases accurately in simulations and expose transition mechanisms without force-field bias.
QUASAR is a new autonomous LLM-based system that orchestrates multi-scale atomistic simulations and benchmarks as a general reasoning tool rather than a narrow automation script.
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
r²SCANY@r²SCANX uses distinct exact-exchange fractions for density (X) and energy (Y) to reduce self-interaction errors and improve band gaps, magnetic moments, and oxidation energies in 20 strongly correlated transition-metal oxides over r²SCAN and DFT+U.
citing papers explorer
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Adaptive fine-tuning of foundation models for crystal structure prediction: Discovery of high-pressure phases in the CaFeNi system
An adaptive fine-tuning workflow for foundation-model MLIPs enables efficient CSP in the CaFeNi ternary, reproducing the low-pressure hull and predicting a new phase Ca6FeNi stable above 100 GPa.
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A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$
Constructs G-equivariant ViTs for arbitrary discrete G ≤ O(2), proves H ≤ G implies G-models embed into H-models and single-head equivariant attention realizes all ordinary G-equivariant maps, introduces D6 hexagonal model, and reports preliminary accuracy gains on PatternNet in low-data regimes.
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Constraint-Aware Quantum Optimization of Defect Configurations in Doped ZrO2: XY-Mixer QAOA and Grover Adaptive Search
Presents an end-to-end constraint-aware quantum optimization pipeline using XY-mixer QAOA and Grover Adaptive Search for low-energy defect configurations in doped ZrO2, with QAOA validated against exact enumeration on a high-accuracy QUBO surrogate of MACE energies.
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Magnetic HIP-NN for spin dynamics in disordered itinerant magnets
mHIP-NN extends hierarchical message-passing networks to model electron-mediated spin dynamics in disordered itinerant magnets while preserving rotational symmetry.
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Disorder-driven symmetry suppression by van der Waals planar defects in a magnetic topological insulator
Ion irradiation creates a layer-disordered phase with planar defects in MnBi2Te4 that suppresses symmetry and anomalous Hall conductivity fivefold while magnetic order persists.
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Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks
An O(L^3) algorithm computes contracted Clebsch-Gordan tensor products for equivariant ML potentials using a structured angular grid and spherical Poisson bracket to handle parity-odd terms at fixed CP rank.
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QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
QT-Net predicts atomic electron populations and multipoles via a new SOAP-cluster held-out test, improving molecular property prediction and recovering QM9 dipole moments from per-atom outputs.
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Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
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Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
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Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS
A flexible interface in GROMACS integrates PyTorch neural network potentials into hybrid ML/MM molecular dynamics simulations for biomolecular systems.
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Property-Specific Molecular Representations via Feature-Space Transfer Compression
A transfer compression technique using semi-empirical data reduces molecular representation dimensions by a median 72% (range 36-98%) while retaining accuracy for energy, heat capacity, dipole moment and polarizability on QM9 and VQM24, and improves data efficiency for dipoles to 19% of training dat
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Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
Pre-registered validation of an ML Na-cathode voltage screen yields 0.67 V MAE against experiment, with Materials Project PBE+U references 0.54 V low and dominating the error.
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Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
Multitask learning on linear-scaling GFN1-xTB orbital charges cuts energy MAE by 46% and data needs by 5x versus energy-only MLIPs while outperforming DFT atomic charge augmentation.
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Anisotropic Crystallization Kinetics and Interfacial Dynamics of Phase-Change Material Sb$_2$S$_3$ from Machine Learning Force Field Simulations
Machine learning force field molecular dynamics simulations reveal anisotropic crystallization in Sb2S3 with [100] facet fastest growth and interface-controlled kinetics with activation energy 0.55-0.57 eV.
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Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
A reweighting method with mean energy-gap approximation transfers PMFs between MLIPs to recover target reaction and activation free energies at low cost for a 601-atom Li+ transport system across DFT levels.
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Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
-
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.
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Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement
dm-PhiSNet predicts 1-RDMs from geometries via equivariant PhiSNet with two-stage training and analytic refinement, reducing SCF iterations 49-81% on six closed-shell molecules while giving accurate one-shot energies and forces without force supervision.
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DeepHartree: A Poisson-Coupled Neural Field for One-Shot Density Functional Theory
A Poisson-coupled neural field predicts electron density through the Hartree potential, producing one-shot Kohn-Sham matrices that accelerate and transfer SCF calculations.
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Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks
QCOF ML potentials tuned on COF data outperform general MACE models for defective systems and reveal higher thermal defect sensitivity in CTF-1 versus COF-LZU1 with nearly invariant low-strain mechanics.
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Force Field-Agnostic Phase Classification of Zeolitic Imidazolate Framework Polymorphs
Neural networks trained on molecular configurations from different force fields classify ZIF polymorph phases accurately in simulations and expose transition mechanisms without force-field bias.
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QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities
QUASAR is a new autonomous LLM-based system that orchestrates multi-scale atomistic simulations and benchmarks as a general reasoning tool rather than a narrow automation script.
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Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
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Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
r²SCANY@r²SCANX uses distinct exact-exchange fractions for density (X) and energy (Y) to reduce self-interaction errors and improve band gaps, magnetic moments, and oxidation energies in 20 strongly correlated transition-metal oxides over r²SCAN and DFT+U.
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A Hybrid Quantum Mechanics Machine Learning Forcefield (QM/ML) Framework for Accurate Solute-Dislocation Interaction Simulations
Hybrid QM/ML forcefield framework couples DFT with MLIPs to enable scalable, chemically accurate simulations of solute-dislocation interactions, demonstrated on Sn/Fe segregation in Zr and magnetic effects in steel.
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Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction
Loss-guided adaptive scale refinement on NaCl aqueous system reduces overall force MAE from 399.65 to 381.23 by discovering intermediate scales from initial anchors.
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Electron dynamics mediate the water-carbon {\pi} bond
Electron dynamics in the aromatic pi cloud quench and amplify specific vibrational signals from a single water molecule bound to pyrene anion.
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Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
Machine-learned force fields trained on coupled-cluster potential energy surfaces produce phonon dispersions and vibrational densities of states for solids that agree better with experiment than DFT-based models.
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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.
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Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries
MACE-MPA-0 predicts Li diffusion Ea of 0.22 eV in LiF, fine-tuned version with 300 points gives 0.20 eV, close to DeePMD reference of 0.24 eV, using far less training data.
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Accelerating point defect simulations using data-driven and machine learning approaches
Machine learning models trained on quantum mechanical data can predict defect properties in solids with high accuracy but at much lower computational cost than traditional methods.