arXiv subjects

Ralf Drautz

Publications and source records attributed to Ralf Drautz.

At least 19 recordsLinked to original sources

arXiv2026

Reactive calcium carbonate precipitation from an atomic cluster expansion potential and enhanced sampling

Calcium carbonate formation from aqueous solution is central to biomineralization and to carbon sequestration through mineral carbonation. At near-neutral pH, the process is highly reactive, with proton transfer mediating the interconversion between carbonate species. Most atomistic simulations to date either treat carbonate speciation as fixed or consider proton transfer only in small clusters. Here, we combine an ab initio trained atomic cluster expansion (ACE) machine-learning potential for molecular dynamics with enhanced sampling to enable reactive simulations of the early stages of calcium carbonate precipitation at previously inaccessible length and time scales. We study proton transfer and carbonate speciation in ion pairs and triplets, as well as in the collective aggregation of many ions. Our simulations with few ions show that ion association provides a favorable pathway for proton transfer, facilitating interconversion between carbonate, bicarbonate, and carbonic acid. In many-ion systems, proton transfer occurs spontaneously alongside aggregation, and we observe significant changes in the coordination environments as species evolve during the simulations. These results show that ion aggregation and chemical reactivity can be strongly coupled during the early stages of nucleation from solution.

physics.chem-ph↗

arXiv2026

A hidden low-temperature transformation pathway in compositionally complex materials

Most compositionally complex materials (CCMs, frequently referred to as high entropy alloys) are metastable and their attractive properties often belong to kinetically trapped states. However, pathways towards lower-free-energy phase states governing long-term stability, can remain hidden because diffusion-controlled atomic redistribution is too slow to be revealed at experimentally accessible timescales. This blind spot is acute in CCM design: enormous compositional spaces are screened for performance, yet the low-temperature kinetics and the associated transformation pathways determining whether that performance persists are rarely considered in material selection. Here we use defect-rich nanoscale volumes coupled with atom-probe tomography to access and reconstruct the hidden phase-evolution pathway in a metastable Ag24Au20Pd50Pt6 electrocatalyst, without relying on elevated temperatures to accelerate the transformation. By varying microstructural starting state, annealing temperature and time, we reveal precipitation of a Pt-rich phase within the fcc matrix, its coarsening and re-homogenization. The Pt-rich phase recurs after homogenization with delayed kinetic accessibility, while prolonged annealing extends the pathway to 300°C. Atomistic simulations independently predict the same Pt-rich phase selection. The transformation is accompanied by a 3.7-fold loss of catalytic activity for hydrogen evolution. These results establish hidden phase-evolution pathways as a materials-design variable: resolving them can guide the selection of metastable CCMs not only for their as-synthesized properties, but also for the phase states and associated functionalities they may access over time.

cond-mat.mtrl-sci↗

arXiv2026

Computing binary alloy phase diagrams with explicit configurational and vibrational entropy

Phase stability in multicomponent solid solutions depends on configurational entropy beyond the ideal mixing limit, but capturing it together with vibrational entropy within the same atomistic framework remains challenging. Here, we extend non-equilibrium thermodynamic integration to composition-dependent transformations through an alchemical interpolation of the interactions, combined with Monte Carlo identity exchange moves and molecular dynamics that sample the vibrational and non-ideal configurational entropy along the integration path. We apply the framework to the Au-Cu binary alloy using Atomic Cluster Expansion potentials trained on density functional theory data using the LDA, PBE, and r2SCAN functionals, and construct composition-temperature phase diagrams directly from atomistic free energies. We find that explicit configurational sampling lowers the AuCu order-disorder transition temperature predicted by the ACE potential trained on LDA data from approximately 810 K to 710 K, closer to the experimental value of 683 K, and substantially widens the stability range of the solid solution. At the same time, the much larger sensitivity to the exchange-correlation functional shows that this level of agreement should not be interpreted as general predictive accuracy. Non-ideal configurational entropy must therefore be sampled explicitly, alongside a careful choice of functional, for a reliable atomistic description of binary phase diagrams.

cond-mat.mtrl-sci↗

arXiv2026

Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity

Aromatic organic solutes in water exhibit a delicate balance between hydrophobic solvation and directional O-H$\cdots π$ hydrogen bonds, yet widely used force fields and state-of-the-art density functional approaches struggle to provide a consistent picture of these pivotal interactions. We introduce a data-efficient upfitting strategy to train a machine learning interatomic potential (MLIP) based on the graph atomic cluster expansion for aqueous aromatic molecules with CCSD(T) accuracy for condensed phase simulations, using only finite molecular clusters. We apply our method to aqueous toluene (C$_6$H$_5$CH$_3$). The resulting CCSD(T)-quality MLIP reproduces coupled cluster energies and forces in bulk and reveals that commonly employed methods do not capture the crucial balance between hydrophilic and hydrophobic solvation, distorting the interactions of aromatic molecules with their environment. Representative biomolecular force fields substantially understructure the hydrophobic solvation shell and misorient interfacial water, while overestimating $π$-contacts, yielding an inconsistent solvation balance. Even hybrid DFT and MP2 overestimate barriers to breaking of water-$π$ hydrogen bonds. Our workflow provides a practical, general route to CCSD(T)-quality condensed-phase simulations of aqueous solutions, and thus constructed interaction potentials now open the door to consistent, highly accurate benchmark studies of $π$-contacts and hydrophobic effects in biomolecular contexts such as solvation of proteins and DNA in aqueous environments.

physics.chem-ph↗

arXiv2026

A general-purpose atomic cluster expansion interatomic potential for niobium

Niobium, a body-centered cubic transition metal, poses a challenge for interatomic potentials, which struggle to capture its properties, such as phonons, high-pressure behavior, energy barriers to dislocation glide, and others. To tackle this challenge, we constructed a general-purpose atomic cluster expansion (ACE) potential for niobium. We trained our ACE on thousands of density functional theory (DFT) structures spanning a diversity of local environments. We validated it across a range of properties and compared it with existing empirical and machine learning (ML) potentials, including a novel universal ML potential. The resulting ACE balances accuracy, efficiency, and robustness, enabling large-scale exploration of niobium with near-DFT precision. Finally, our ACE held its own in a stringent test: a near-million-atom molecular dynamics simulation of fracture

cond-mat.mtrl-sci↗

arXiv2026

Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks

We present an $\mathcal{O}(L^3)$ algorithm for evaluating contracted Clebsch--Gordan tensor products in $\mathrm{O}(3)$-equivariant machine learning potentials at fixed Canonical Polyadic (CP) rank. Mapping the angular integral to a structured Gauss--Legendre and Fourier tensor-product grid decouples the radial channel contractions from the angular transforms. The antisymmetric parity-odd Clebsch--Gordan channels, unreachable by the symmetric pointwise product on a scalar $S^2$ grid, are recovered through the surface-curl pairing $\hat r \cdot [\nabla_{S^2} A \times \nabla_{S^2} B]$, the spherical Poisson bracket, which supplies the $L=1$ angular momentum on the grid while preserving rotational equivariance. The construction extends to parity-aware equivariant message passing in atomic-cluster-expansion-style architectures and is verified by direct numerical quadrature. The full uncontracted Clebsch--Gordan tensor product remains subject to the $\mathcal{O}(L^4)$ output-size lower bound. A benchmark shows wall-clock scaling empirically as $L^2$ across the practical $l_{\max}$ range. For the on-site contraction this is pre-asymptotic, giving way to $L^3$ at large $l_{\max}$. For message passing it is structural and the runtime is memory-bandwidth bound on $L^2$-sized grid tensors.

physics.comp-ph↗

arXiv2026

Hydride formation and phase separation in palladium nanoparticles from a transferable atomic cluster expansion potential

The palladium-hydrogen system is a prototype for hydrogen-metal interactions and underpins technologies such as hydrogen storage, catalysis and purification. Yet its nanoscale behaviour -- where surface and interface energetics, elastic coherency strain and size-dependent thermodynamics govern phase separation -- has eluded accurate atomistic simulation. Empirical potentials misrepresent the energetics of interstitial hydrogen, while existing machine-learning models are restricted to bulk phases at low-hydrogen environments. Here we introduce an atomic cluster expansion (ACE) for Pd-H that reproduces formation energies, phonon spectra, elastic constants, hydrogen migration barriers and surface adsorption with near-DFT accuracy, benchmarked directly against neutron-scattering, high-pressure and lattice-expansion experiments. Its near-linear scaling and CPU efficiency make molecular dynamics of PdH$_x$ nanoparticles exceeding 28,000 atoms ($\sim$12 nm in diameter) tractable over nanosecond timescales. These simulations resolve, at the atomic scale, the kinetic separation of $α$- and $β$-PdH$_x$ into a core-shell architecture, reproduce the experimentally observed size dependence of the lattice parameter, and uncover a pronounced hydrogen-induced lowering of the nanoparticle melting temperature. The potential brings experimentally relevant scales of metal-hydride dynamics within quantitative reach.

cond-mat.mtrl-sci↗

arXiv2026

Data-efficient machine-learning of complex Fe-Mo intermetallics using domain knowledge of chemistry and crystallography

Atomistic simulations of multi-component systems require accurate descriptions of interatomic interactions to resolve details in the energy of competing phases. A particularly challenging case are topologically close-packed (TCP) phases with close energetic competition of numerous different site occupations even in binary systems like Fe-Mo. In this work, machine learning (ML) models are presented that overcome this challenge by using features with domain knowledge of chemistry and crystallography. The resulting data-efficient ML models need only a small set of training data of simple TCP phases $A$15, $σ$, $χ$, $μ$, $C$14, $C$15, $C$36 with 2-5 WS to reach robust and accurate predictions for the complex TCP phases $R$, $M$, $P$, $δ$ with 11-14 WS. Several ML models with kernel-ridge regression, multi-layer perceptrons, and random forests, are trained on less than 300 DFT calculations for the simple TCP phases in Fe-Mo. The performance of these ML models is shown to improve systematically with increased utilization of domain knowledge. The convex hulls of the $R$, $M$, $P$ and $δ$ phase in the Fe-Mo system are predicted with uncertainties of 20-25 meV/atom and show very good agreement with DFT verification. Complementary X-ray diffraction experiments and Rietveld analysis are carried out for an Fe-Mo R-phase sample. The measured WS occupancy is in excellent agreement with the predictions of our ML model using the Bragg Williams approximation at the same temperature.

cond-mat.mtrl-sci↗

arXiv2026

AI-Driven Expansion and Application of the Alexandria Database

We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.

cond-mat.mtrl-sci↗

arXiv2026

Hydrogen uptake and hydride formation in Al$_x$CoCrFeNi high-entropy alloys: First-principles, universal-potential, and experimental study

Hydrogen uptake in complex multicomponent alloys, including high-entropy alloys (HEAs), governs both hydrogen storage capacity and resistance to hydrogen-induced degradation. We combine high-pressure experiments, density-functional theory (DFT), and a GRACE universal interatomic potential to investigate hydrogen absorption in Al$_{0.3}$CoCrFeNi and Al$_3$CoCrFeNi HEAs. In H$_2$ as a pressure-transmitting medium, the FCC Al$_{0.3}$CoCrFeNi alloy forms hydrides at ambient temperature above 3 GPa, whereas the Al-rich B2 Al$_3$CoCrFeNi alloy shows no evidence of hydride formation even upon heating at pressures up to 50 GPa. Experiments and calculations show that aluminum suppresses hydrogen uptake by increasing solution energies and destabilizing interstitial sites. The universal potential, employed in the calculations and pretrained on large DFT databases, closely reproduces DFT energetics and demonstrates transferability from the dilute limit to the hydride-forming regime. Simulations further disentangle the roles of local ordering, volume changes, composition, and crystal structure. Overall, our results indicate that hydrogen solubility in Al-containing HEAs is governed primarily by composition, with Al-driven B2 ordering as a strong secondary effect.

cond-mat.mtrl-sci↗

arXiv2026

Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions

Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training datasets that heavily emphasize low-energy, near-equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized entropy (SMAX) dataset yields markedly improved robustness across a range of stringent benchmarks. These include large-strain phase transformations in tin, defect evolution in tungsten-based alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions.

cond-mat.mtrl-sci↗

arXiv2026

Water Phase Diagram from a General-Purpose Atomic Cluster Expansion Potential

Water's phase diagram remains one of the most intricate and challenging benchmarks in molecular modeling. In this study, we compute the phase diagram of water using an Atomic Cluster Expansion (ACE) potential trained on density-functional theory (DFT) calculations based on the revPBE-D3 exchange and correlation functional. We compute solid-liquid chemical potential differences and melting points using biased coexistence simulations with the On-the-Fly Probability Enhanced Sampling (OPES) method. Starting from these points, we trace coexistence lines using Gibbs-Duhem integration. This combination of methods allows us to consistently map pressure-temperature phase boundaries and reconstruct the full phase diagram between approximately 100-500 K and 0-4 GPa. The stability regions of the main ice polymorphs (Ih, II, V, VI, and VII) are reproduced in close agreement with experiments. As in earlier studies based on DFT, ice III is metastable and there are systematic shifts of coexistence lines with respect to experimental results. Our results demonstrate the capability of our general-purpose ACE potential to capture the complex phase behavior of water across wide thermodynamic conditions.

cond-mat.mtrl-sci↗

arXiv2026

Graph atomic cluster expansion for foundational machine learning interatomic potentials

Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We introduce universal potentials based on the graph atomic cluster expansion (GRACE) framework, trained on several of the largest available materials datasets. Through comprehensive benchmarks, we demonstrate that the GRACE models establish a new Pareto front for accuracy versus efficiency among foundational interatomic potentials. We further showcase their exceptional versatility by adapting them to specialized tasks and simpler architectures via fine-tuning and knowledge distillation, achieving high accuracy while preventing catastrophic forgetting. This work establishes GRACE as a robust and adaptable foundation for the next generation of atomistic modeling, enabling high-fidelity simulations across the periodic table.

cond-mat.mtrl-sci↗

arXiv2025

Conservative adaptive-precision interatomic potentials

Adaptive precision molecular dynamics simulations have developed along energy- and force-coupling approaches, which allow for a continuous transition between different particle descriptions or interaction potentials. Most approaches consider different (fixed) spatial regions, which control the transition between the descriptions and consequently avoid a consistent momentum-conserving Hamiltonian description. We present here a new approach to fully integrate the coupling into a Hamiltonian, therefore allowing for a conservative description, which, by design, guarantees both energy and momentum conservation. By coupling a fast EAM potential to a highly accurate ACE potential, we verify numerically the conservation properties and show that one can achieve - dependent on both the potential and the atomistic system - a speedup of one or two orders of magnitude compared to a pure ACE simulation.

physics.comp-ph↗

arXiv2025

Efficient local atomic cluster expansion for BaTiO$_3$ close to equilibrium

Barium titanate (BTO) is a representative perovskite oxide that undergoes three first-order ferroelectric phase transitions related to exceptional functional properties. In this work, we develop two atomic cluster expansion (ACE) models for BTO to reproduce fundamental properties of bulk as well as defective BTO phases. The two ACE models do not target full transferability but rather aim to examine the influence of implicit and explicit treatment of long-range Coulomb interactions. We demonstrate that both models describe equally well the temperature induced phase transitions as well as polarization switching due to applied electric field. Even though the parametrizations are based on a limited number of configurations that are mostly not far away from the equilibrium, the ACE models are able to capture also properties of important crystal defects, such as oxygen vacancies, stacking faults and domain walls. A systematic comparison shows that the phase transitions as well as the fundamental properties of the investigated defects can be described with similar accuracy with or without explicit treatment of charges and Coulomb interactions allowing for efficient short-range machine learning potentials.

cond-mat.mtrl-sci↗

arXiv2025

Nanoindentation simulations for copper and tungsten with adaptive-precision potentials

We perform nanoindentation simulations for both the prototypical face-centered cubic metal copper and the body-centered cubic metal tungsten with a new adaptive-precision description of interaction potentials including different accuracy and computational costs: We combine both a computationally efficient embedded atom method (EAM) potential and a precise but computationally less efficient machine learning potential based on the atomic cluster expansion (ACE) into an adaptive-precision (AP) potential tailored for the nanoindentation. The numerically expensive ACE potential is employed selectively only in regions of the computational cell where large accuracy is required. The comparison with pure EAM and pure ACE simulations shows that for Cu, all potentials yield similar dislocation morphologies under the indenter with only small quantitative differences. In contrast, markedly different plasticity mechanisms are observed for W in simulations performed with the central-force EAM potential compared to results obtained using the ACE potential which is able to describe accurately the angular character of bonding in W due to its half-filled d-band. All ACE-specific mechanisms are reproduced in the AP nanoindentation simulations, however, with a significant speedup of 20-30 times compared to the pure ACE simulations. Hence, the AP potential overcomes the performance gap between the precise ACE and the fast EAM potential by combining the advantages of both potentials.

cond-mat.mtrl-sci↗

arXiv2025

Core structure of dislocations in ordered ferromagnetic FeCo

We elucidated the core structure of screw dislocations in ordered B2 FeCo using a recent magnetic bond-order potential (BOP) [Egorov et al., Phys. Rev. Mater. 7, 044403 (2023)]. We corroborated that dislocations in B2 FeCo exist in pairs separated by antiphase boundaries. The equilibrium separation is about 50 A, which demands large-scale atomistic simulations - inaccessible for density functional theory but attainable with BOP. We performed atomistic simulations of these separated dislocations with BOP and predicted that they reside in degenerate core structures. Also, dislocations induce changes in the local electronic structure and magnetic moments.

cond-mat.mtrl-sci↗

arXiv2025

A practical guide to machine learning interatomic potentials -- Status and future

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years.

cond-mat.mtrl-sci↗