arXiv subjects

Connor W. Edwards

Publications and source records attributed to Connor W. Edwards.

4 recordsLinked to original sources

arXiv2026

Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry

Universal machine-learned interatomic potentials (uMLIPs) offer a promising approach to performing atomistic simulations at near-DFT accuracy with greatly reduced computational cost. Here, we present a new high-temperature benchmarking dataset of 40~ps ab~initio molecular dynamics (AIMD) trajectories simulated at 300, 1000, and 2000 K for nine zinc- and zirconium-based metal-organic frameworks (MOFs): ZIF-8, CALF-20, MOF-10, MOF-5, MIP-206, UiO-66, UiO-67, UiO-66-NH2, and NU-1000. These trajectories capture equilibrium dynamics, thermally induced distortions, and early-stage decomposition events, including linker degradation and metal node aggregation. Subsequently, we use this dataset to benchmark five leading uMLIPs: ORB-v3, MACE-MP-0a, MACE-MPA-0, fairchem ODAC23, and fairchem OMAT. Our results reveal that ORB-v3 and fairchem OMAT achieve the lowest energy, force, and stress errors across all temperatures. However, all models exhibit significant error under high-temperature conditions. Long-timescale molecular dynamics simulations produced with ORB-v3 demonstrate that the generative error of uMLIPs far exceeds model losses captured during static validation, highlighting the limitations of current universal models for simulating high-temperature MOF dynamics. This work provides a benchmark for assessing the robustness of uMLIPs in extreme regimes and guides future development of potentials capable of accurately modeling the chemistry of high-temperature MOF dynamics.

cond-mat.mtrl-sci↗

arXiv2026

MLIP-MC: A Framework for Adsorption Simulations using Machine-Learned Interatomic Potentials

Grand canonical Monte Carlo (GCMC) simulations are essential for screening metal-organic frameworks (MOFs) for gas adsorption, yet their accuracy is limited by underlying interatomic potentials. Universal machine-learned interatomic potentials (MLIPs), trained on diverse chemical datasets, promise zero-shot prediction without system-specific training. We introduce MLIP-MC, an open-source Python framework to conduct GCMC simulations with MLIPs, and use this framework to benchmark a series of universal models, including MACE-MP-0, ORB-v3, and fairchem ODAC, for CO2 adsorption on ZIF-8, ZIF-4, and Mg-MOF-74. All universal models exhibit systematic biases, consistently over- or underestimating adsorption energetics. Crucially, accuracy depends on training data composition: only models trained on MOF-adsorbate interactions achieve reasonable agreement with a density functional theory derived reference. Errors grow linearly with CO2 uptake, reflecting compounding inaccuracies in adsorbate-adsorbate interactions. Our results demonstrate that current universal MLIPs require finetuning for quantitative adsorption predictions and demonstrate the power of MLIP-MC to rapidly test models.

cond-mat.mtrl-sci↗

arXiv2026

Simulations of High Temperature Decomposition of Metal-Organic Frameworks to form Amorphous Catalysts

Metal-organic framework (MOF) derived materials formed through high temperature processes show great potential as catalysts. However, understanding of structure-property relationships between the initial MOF and the resulting MOF-derived catalyst is limited because the amorphous nature of the catalyst challenges standard structural characterization methods. Neural network approaches that learn interatomic potentials from density functional theory offer a promising solution. We simulated the pyrolysis of UiO-66, UiO-67 and MIP-206 using both foundational and fine-tuned machine learned interatomic potentials (MLIPs). To mimic experimental conditions, an atmosphere of CO2 and H2 was introduced and the structures were doped with 20 wt% copper to probe the effect of copper on the structural evolution of MOFs. These simulations provide atomistic insights into gas evolution, metal nanoparticle formation, and linker decomposition that were compared to available experimental data. Overall, this work demonstrates the potential of MLIPs to accurately model high temperature MOF dynamics under experimentally relevant conditions and guide the design of new catalytic materials.

cond-mat.mtrl-sci↗

arXiv2025

Evaluating Mechanical Property Prediction across Material Classes using Molecular Dynamics Simulations with Universal Machine-Learned Interatomic Potentials

We assess the accuracy of six universal machine-learned interatomic potentials (MLIPs) for predicting the temperature and pressure response of materials by molecular dynamics simulations. Accuracy is evaluated across 13 diverse materials (nine metal-organic frameworks and four inorganic compounds), computing bulk modulus, thermal expansion, and thermal decomposition. These MLIPs employ three different architectures (graph neural networks, graph network simulators, and graph transformers) with varying training datasets. We observe qualitative accuracy across these predictions but systematic underestimation of bulk modulus and overestimation of thermal expansion across all models, consistent with potential energy surface softening. From all tested models, three top performers arise; `MACE-MP-0a', `fairchem_OMAT', and `Orb-v3', with average error across metrics and materials of 41%, 44%, and 47%, respectively. Despite strong overall performance, questions arise about the limits of model transferability: dataset homogeneity and structural representation dominate model accuracy. Our results show that certain architectures can compensate for biases, a step closer to truly universal MLIPs.

cond-mat.mtrl-sci↗