arXiv subjects

Sander Vandenhaute

Publications and source records attributed to Sander Vandenhaute.

5 recordsLinked to original sources

arXiv2026

A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids

Thermodynamic Integration (TI) is the state-of-the-art computational technique for accurate Gibbs free energy predictions of solids. Conventional TI schemes start from an NVT harmonic reference and require three successive corrections to recover the Gibbs free energy of the real crystal in the NPT ensemble. However, the NVT-to-NPT correction neglects full cell flexibility. Here, we present a rigorous (and only) two-step TI scheme that operates entirely in the NPT ensemble, eliminating the need for the approximate NVT-to-NPT step. The key methodological advancement is the novel NPT reference that explicitly accounts for full cell fluctuations. The new approach is compared with the conventional one via two complementary case studies. For ice polymorphs, having simple cell-shape distributions, the new approach reproduces conventional TI results with excellent agreement. For CsPbI3, whose black phase exhibits complex cell-shape behavior, we demonstrate that our novel method provides more accurate Gibbs free energy differences than the conventional one. Moreover, the proposed framework maintains comparable computational cost while offering a simplified workflow. Overall, the new NPT TI scheme provides rigorous and direct Gibbs free energy calculations for solids.

physics.comp-ph↗

arXiv2026

The effect of the A-site cation on the phase transition temperature of metal halide perovskites

A key challenge for the practical application of metal halide perovskites (MHPs) is the instability of the desired perovskite phase relative to the optically non-active $δ$ phase. To determine the phase stability, we previously developed a procedure to compute the harmonic free energy as a function of temperature, which was suited for CsPbI$_3$ but fails when Cs is replaced by organic cations due to their rotational freedom. Herein we propose a multistep thermodynamic integration (TI) approach that corrects the harmonic free energy to obtain the Gibbs free energy. Given the abundance of local minima in these materials, we employ replica exchange to prevent simulations from getting trapped, while introducing an intermediate potential energy surface to improve convergence and reduce computational cost. Benchmarking energy and forces from different exchange-correlation functionals and dispersion methods against high-level RPA+HF calculations identifies PBE+D3(BJ) as the best trade-off between accuracy, computational efficiency, and precision. To perform molecular dynamics simulations within the TI framework, it was necessary to train a machine learning potential using the MACE architecture on ab initio data calculated with density functional theory. Our results show that, for all three materials, the free energy difference between the $γ$ and $δ$ phases exhibits a very similar temperature dependence. This suggests that phase stability is primarily governed by differences in ground-state energy, rather than by material-specific thermal effects. Beyond these three materials, our methodology provides a robust framework for investigating the phase behavior of other MHPs, paving the way for the discovery of more stable perovskites.

cond-mat.mtrl-sci↗

arXiv2025

Orb-v3: atomistic simulation at scale

We introduce Orb-v3, the next generation of the Orb family of universal interatomic potentials. Models in this family expand the performance-speed-memory Pareto frontier, offering near SoTA performance across a range of evaluations with a >10x reduction in latency and > 8x reduction in memory. Our experiments systematically traverse this frontier, charting the trade-off induced by roto-equivariance, conservatism and graph sparsity. Contrary to recent literature, we find that non-equivariant, non-conservative architectures can accurately model physical properties, including those which require higher-order derivatives of the potential energy surface. This model release is guided by the principle that the most valuable foundation models for atomic simulation will excel on all fronts: accuracy, latency and system size scalability. The reward for doing so is a new era of computational chemistry driven by high-throughput and mesoscale all-atom simulations.

cond-mat.mtrl-sci↗

arXiv2025

Cluster-based machine learning potentials to describe disordered metal-organic frameworks up to the mesoscale

Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in various technological fields. Defect engineering requires a fundamental understanding of the nature of spatial disorder and consequent changes in material properties, which is currently lacking. We introduce the cluster-based learning methodology, enabling the development of state-of-the-art machine learning potentials (MLPs) from defective systems at any length scale. Our method identifies atomic interactions in bulk structures and extracts local environments as finite molecular fragments to augment the model's training data where needed. We show that cluster-based learning delivers MLPs capable of accurately describing spatial defects in mesoscopic systems with over twenty thousand atoms. Afterwards, we select our best model to investigate some major mechanical properties of spatially disordered UiO-66-derived structures, elucidating the influence of defect concentration and composition on material behaviour. Our analysis includes large supercell structures, demonstrating that (near-) ab initio accuracy is within reach at the mesoscale.

cond-mat.mtrl-sci↗

arXiv2024

Rare Event Sampling using Smooth Basin Classification

The efficiency of atomic simulations of materials and molecules can rapidly deteriorate when large free energy barriers exist between local minima. We propose smooth basin classification, a universal method to define reaction coordinates based on the internal feature representation of a graph neural network. We achieve high data efficiency by exploiting their built-in symmetry and adopting a transfer learning strategy. We benchmark our approach on challenging chemical and physical transformations, and show that it matches and even outperforms reaction coordinates defined based on human intuition.

physics.chem-ph↗