Light elements play an important role in influencing the macroscale properties of engineering alloys through grain boundary (GB) segregation phenomena. However, the scarcity and scattered nature of ab initio datasets for light elements in steels makes reproduction and extraction of general trends from the literature difficult. Here, we present a comprehensive ab initio evaluation of the segregation energies and cohesive effects for H, He, B, C, N, O, P, S, extensively sampling both substitutional and interstitial sites in six model coincident site lattice (CSL) ferritic iron GBs using density functional theory (DFT). Cohesive effects are evaluated in both a quantum-chemistry bond-order and rigid Rice-Wang interfacial cohesive strength framework. Our calculations indicate that, compared at the same concentration, B and C enhance GB cohesion, N, P, H are mildly detrimental, and He, O, S as powerful decohesive agents/embrittlers. Sampling both interstitial and substitutional starting positions is necessary to accurately capture segregation spectra. Commonly utilised sampling criteria such as site volumes prove insufficient for identifying deepest GB binding sites. Solutes placed in either kind of site can induce large relaxations to the same final configuration, resulting in site classification ambiguity. The nearest neighbour distance of a solute to its neighbours after relaxation is shown to be a controlling factor for the lower threshold of segregation energies at sites. The freely available DFT dataset and analysis repositories are expected to advance understanding of GB segregation behaviours of light elements in steels and serve as a resource for developing machine learning interatomic potentials.

As one of the most abundant interstitial elements, nitrogen (N) is effective in improving yield strength of metallic materials, due to interstitial solid solution strengthening. Doping N can substantially enhance the yield strength but often leads to a decreased ductility, revealing a strength-ductility trade-off phenomenon. Here, we simultaneously enhance the strength and ductility in a non-equiatomic Cr-Mn-Fe-Co-Ni high-entropy alloy via N alloying and unravel the underlying microscopic mechanisms. The N-doped alloy (1 at.% N) shows an excellent combination of higher yield strength (104% increase) and larger ductility (38% increase), with a two-stage strain hardening behavior, compared to the N-free alloy. Detailed transmission electron microscopy (TEM) analysis reveals that N-doping introduces more short-range order (SRO) domains within the microstructure, leads to pronounced planar slip, and promotes the formation of nano-spaced (6-15 nm) stacking faults and deformation twins. Continuous generation and interaction of the fine-spaced SFs act as a strong barrier for dislocation movement and provide ample room for dislocation storage. The interaction of SRO with dislocations and the evolution of SFs ascribe to the first strain hardening stage, and the disordering of the SRO along with the activation of deformation twins are attributed to the second strain hardening stage. Our work shows that Ndoping is effective in simultaneously improving the strength-ductility synergy and provides novel insights into alloy design with slightly elevating the SFE, and manipulating the ordered structure within the HEA.

High-entropy alloys (HEAs) exhibit exceptional structural and functional properties arising from their complex local chemical environments, and their vast compositional space offers considerable flexibility to further tune and optimize these properties. Atomistic simulations based on density functional theory (DFT) have played a central role in elucidating the thermodynamic, mechanical, magnetic, and defect-related properties of HEAs. However, DFT simulations are severely limited by the intrinsic chemical and configurational complexity of these alloys, particularly because reliable predictions require extensive statistical sampling over chemically diverse configurations and access to extended spatial and temporal scales. In this review, we summarize recent advances in atomistic simulations of HEAs, with particular emphasis on machine learning interatomic potentials (MLIPs), which extend beyond conventional DFT approaches. We discuss how MLIPs enable statistically robust simulations with near-DFT accuracy while dramatically reducing computational cost, thereby allowing explicit treatment of chemical short-range order, vibrational contributions to Gibbs energies, point defects, diffusion, dislocation behavior, grain boundaries, and hydrogen absorption in chemically complex alloys. Particular attention is devoted to the role of local chemical environments, many-body interactions, and configurational sampling in determining HEA properties. We further review recent developments in universal/foundation MLIPs trained on chemically diverse datasets and discuss their potential for rapid and transferable atomistic simulations of HEAs across broad compositional and configurational spaces. We discuss current limitations and open challenges, including transferability to highly distorted defect configurations, treatment of magnetic and charge degrees of freedom, incorporation of finite-temperature excitations, and construction of representative training datasets for chemically and structurally complex systems. This review aims to provide a comprehensive perspective on the ongoing transition from conventional DFT-based simulations toward scalable, statistically rigorous, and predictive atomistic modeling frameworks for HEAs and related compositionally complex materials.

The VCoNi alloy is a face-centered cubic medium-entropy alloy with exceptionally high yield strength, serving as a prototypical system for investigating short-range order and phase stability in compositionally complex alloys. However, density functional theory calculations underestimate the stability of the random solid solution by several hundred Kelvin. To resolve this discrepancy, we present accurate Gibbs energy calculations for both the random solid solution and a prototypical L12 ordered phase. Our findings reveal that vibrational and electronic excitations account for nearly half of the entropy difference between the ordered phase and disordered solid solution. These factors reduce the energy difference between the two phases by about one-third and help stabilize the solid solution. Our thermodynamic analysis is validated through direct comparison with experimental thermodynamic data.

Accurate treatment of long-range interactions in machine learning interatomic potentials (MLIPs) is essential for electrochemical simulations. However, aggregate energy and force errors alone are insufficient to establish an MLIP's physical accuracy since they do not detect qualitative inconsistencies in the model such as the prediction of image-charge attraction, dielectric screening, or charge transfer. We introduce a benchmark suite EPhEct (Electrostatic Phenomena for Electrochemistry) of focused test cases designed to evaluate MLIPs on electrochemically relevant physical phenomena. The tests probe for image-charge attraction at a metal electrode, the splitting between longitudinal and transverse optical phonons as a probe of ionic and electronic screening, the dipole moment of interfacial water, and Fermi-level pinning during ion discharge. These tests establish a qualitative diagnostic routine complementary to aggregate energy-force metrics.

We develop and benchmark a general-purpose machine-learned interatomic potential for the Fe-O-H ternary system, based on the Atomic Cluster Expansion. Following our approach developed for the Fe-O system, magnetism is explicitly treated by the model in an Ising-like manner. This allows efficient incorporation of magnetic degrees of freedom, making the potential applicable to large-scale atomistic calculations. We demonstrate the capability of the model to accurately describe a wide range of properties and capture basic mechanisms underlying hydrogen-based reduction of iron oxides, interaction of water with iron and hydrogen embrittlement of metallic iron.

Grain boundary networks and their evolution are strongly influenced by triple junctions. The defect nature of these line defects significantly affects the network's properties, but they have not been fully characterized to date. Here, we use scanning transmission electron microscopy combined with atomistic computer simulations to investigate a triple junction at the atomic scale in an Al thin film with texture. Using sampling methods, we were able to construct a computer model of the same junction as in the experiment. We present a technique to calculate the Burgers vector of the triple junction. This allows us to connect the junction's dislocation character to the microscopic degrees of freedom of the joining grain boundaries. The junction line energy can then be calculated using an embedded atom method potential. It follows the same laws as bulk dislocations. Finally, we discovered a range of possible triple junctions for the observed grain boundaries, which vary in the magnitude of their Burgers vector. Interestingly, the experimentally observed junction does not have the smallest possible Burgers vector and energy. This suggests that the kinetics of transforming the junction line are likely too slow to be driven by the small energy contribution of the triple junction.

We present a prototype implementation of a framework for hybrid workflows that integrates automated computation and analysis with manual experimental measurements. Leveraging the pyiron workflow engine, we introduce a lightweight, parameterized procedure description layer that can adjust instrument settings and orchestrate human interventions. Rather than replacing the existing execution engine, we add a minimal abstraction layer that translates procedure descriptions into executable steps for manual operations, enabling seamless handoffs between automated tasks and manual experimental tasks. We demonstrate the approach on a use case that combines manual tensile testing with subsequent analytical evaluation and result aggregation, illustrating how parameters and metadata propagate through the workflow and how instrument state changes and measurement results are captured. We also report a usability study that quantifies the ease with which lab scientists can create and modify workflows. Finally, we summarize lessons learned from this prototype, including improved provenance capture and streamlined experimental orchestration, as well as current limitations. We conclude that the proposed lightweight hybrid workflow description offers a promising path to bridging automation, computation and manual experimentation, and we outline directions for future work.

Abstract First-principles defect calculations in hematite ( α -Fe 2 O 3 ) suffer from a >2 eV spread in reported formation energies, arising from (i) metastable or delocalized charge states and (ii) inconsistent error cancellation with chemical-potential boundaries across studies. We introduce a charge-localization protocol based on the Fe-3 d occupation matrix that enforces physically meaningful small-polaron configurations and eliminates the spurious loss of intermediate charge states; the resulting thermodynamic charge-transition levels are essentially independent of computational parameters. To establish a common energy scale, we use the crossing point of the fully ionized Frenkel pair ( $${V}_{{\rm{Fe}}}^{3-}$$ V Fe 3 − and $${{\rm{Fe}}}_{i}^{3+}$$ Fe i 3 + ) as an intrinsic anchor and align the chemical potentials to parameter-independent experimental formation enthalpies. This reduces the inter-study deviation by ~80%, and reveals that the remaining absolute shifts are dominated by VBM displacement amplified by the large nominal defect charges. The framework provides a reproducible benchmark for hematite defect energetics and is directly transferable to other correlated oxides.

The past decade has seen a significant increase in research efforts aimed at understanding the thermodynamics of low-dimensional phases existing in many materials systems, ranging from two-dimensional materials to core regions of extended defects in crystalline solids. We review the current status of theoretical, computational, and experimental research on the “defect phases,” focusing on grain boundaries (GBs) in elemental and multicomponent polycrystalline materials. After reviewing the generalized concept of a phase of any dimensionality, we discuss recent progress in atomistic computer simulations of GB phase transformations and phase coexistences, including the observation of one-dimensional defects separating GB phases (defects in defects). Computational predictions compare well with experimental observations of multiple GB phases and segregation-induced phase transformations. An intriguing open question of GB thermodynamics is whether the GB free energy can be driven to a zero value by increasing solute segregation. We review recent efforts to understand this ultimate thermodynamic stabilization of GB phases and the possible polycrystalline microstructures that may arise. An outlook for future research in the field is discussed.

We generalize the classic Néel diagram and identify another type of ferrimagnetic phase that remains fully magnetically compensated below the Curie temperature, forming a continuous line of compensated points. It exhibits zero net magnetization while retaining non-relativistic, eV-scale reciprocal spin splitting. We further find a persistently enhanced intrinsic switching field over a broad temperature range near the fully compensated phase. Proximity to the compensated line is achieved by minimizing the net local moment while balancing exchange interactions with respect to the number of equivalent atoms in each sublattice. The resulting extended Néel diagram provides practical design rules for engineering fully compensated ferrimagnetic phases via targeted chemical substitution that combines atoms with robust and weak local moments, as demonstrated through density functional theory and Monte Carlo simulations for GdCo_5-type ferrimagnets.

Introducing electric fields into density functional theory (DFT) calculations is essential for understanding electrochemical processes, interfacial phenomena, and the behavior of materials under applied bias. However, applying user-defined electrostatic potentials in DFT is nontrivial and often requires direct modification to the specific DFT code. In this work, we present an implementation for supercell DFT calculations under arbitrary electric fields and discuss the required corrections to the energies and forces. The implementation is realized through the recently released VASP-Python interface, enabling the application of user-defined fields directly within the standard VASP software and providing great flexibility and control. We demonstrate the application of this approach with diverse case studies, including molecular adsorption on electrified surfaces, field ion microscopy, electrochemical solid-water interfaces, and implicit solvent models.

Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of ‘machine learning’ in materials discovery campaigns. The benefits including automation, reproducibility, data provenance, and reusability of managed data, however, are not widely available in the experimental domain. We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided through active learning, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings. With data from all domains in the same framework, an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available.

Segregation of alloying elements and impurities at grain boundaries (GBs) critically influences material behavior by affecting cohesion. In this study, we present an ab initio high-throughput evaluation of segregation energies and cohesive effects for all elements in the periodic table (Z: 1 to 92, H to U) across six model ferritic iron GBs using density functional theory (DFT). From these data, we construct comprehensive elemental maps for solute segregation tendencies and cohesion at GBs, providing guidance for segregation engineering. We systematically assess the cohesive effects of different elements in all segregating positions along multiple fracture paths with a quantum-chemistry bond-order method as well as a modified Rice-Wang theory of interfacial cohesion. The effects of segregants on the cohesion of GBs are shown to vary drastically as a function of site character, and hence their induced cohesive effects must be considered as a thermodynamic average over the spectral energy distribution. Thus, models that overlook these aspects may fail to accurately predict the impacts of varying alloying concentrations, thermal processing conditions, or GB types. The insights presented here, along with our accompanying dataset, are expected to advance our understanding of GB segregation in steels and other materials.

Intense electrostatic fields, such as those able to break bonds and cause field-ion emission, can fundamentally alter the behaviour of atoms at and on the surface. Using density functional theory (DFT) calculations on the Li (110) surface under high electrostatic fields, we identify a critical field strength at which surface atoms occupying a kink site become thermodynamically unstable against adatom formation. This mechanism leads to the formation of a highly concentrated two-dimensional (2D) adatom gas on the surface. Moreover, the applied field reverses the stability of preferred adsorption sites, enabling barrierless diffusion of lithium atoms even well below the threshold required for field evaporation. The here identified mechanisms offer a unified explanation for experimental observations in atom probe tomography and for understanding high electric field phenomena in systems such as battery interfaces and electrochemical environments.

We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials (MLIP) for multicomponent alloys, called Automated Small SYmmetric Structure Training or ASSYST. Based on exploring the full space of random crystal structures with space groups, it facilitates the construction of training sets for MLIPs in an automatic way without prior knowledge of the material in question. The advantages of this approach are that only cells consisting of few atoms (≈ 10) are needed for the DFT training set, and the size and completeness of the data set can be systematically controlled with very few parameters. We validate that potentials fitted this way can accurately describe a wide range of binary and ternary phases, random alloys, as well as point and extended defects, that have not been part of the training set. Finally, we estimate the binary phase diagrams with good experimental agreement. We demonstrate that the overall excellent performance is not a coincidence, but a consequence of the extensive sampling in phase space of ASSYST. Overall, this means that ASSYST will enable the largely autonomous generation of high-quality DFT reference data and MLIPs.

In the current work, we study the role of grain boundary (GB) misorientation-dependent segregation on austenite nucleation in a 50 % cold rolled intercritically annealed 10Mn-0.05C-1.5Al (wt. %) medium Mn steel. During intercritical annealing at 500 degrees C, austenite nucleates predominantly at high-angle GBs. At 600 degrees C, austenite nucleates additionally at low-angle GBs, exhibiting a temperature dependance. Correlative transmission Kikuchi diffraction /atom probe tomography reveals a misorientation-dependent segregation. While GB segregation has been reported to assist austenite nucleation in medium manganese steels (3-12 wt. % Mn), an understanding of the temperature and misorientation dependance is lacking, which is the aim of current work. Since artifacts of the atom probe can cause a broadening of the segregation width, we combined experiments with results from density functional theory (DFT) calculations that reveal that the Mn segregation is not limited to the GB plane but confined to a region in the range of approximately 1 nm. Consequently, GB segregation alters both the GB interface energy and the free energy per unit volume corresponding to the transformation. We estimate the local driving force for austenite nucleation accounting for the segregation width. Based on classical nucleation theory, we clarify the effect of GB segregation on the critical radius and activation energy barrier for confined austenite nucleation at the GB.

The combined structural and electronic complexity of iron oxides poses many challenges to atomistic modeling. To leverage limitations in terms of the accessible length and time scales, one requires a physically justified interatomic potential which is accurate to correctly account for the complexity of iron-oxygen systems. Such a potential is not yet available in the literature. In this work, we propose a machine-learning potential based on the Atomic Cluster Expansion for modeling the iron-oxygen system, which explicitly accounts for magnetism. We test the potential on a wide range of properties of iron and its oxides, and demonstrate its ability to describe the thermodynamics of systems spanning the whole range of oxygen content and including magnetic degrees of freedom.

Hydrogen embrittlement (HE), degradation of the mechanical properties of metals due to the presence of hydrogen, is a persistent problem that has been attracting the attention of the material science community for about fifteen decades. Extensive experimental observations indicate the presence of nanovoids and the increase of free volume at the grain boundaries in hydrogen contaminated metals. This rate-dependent phenomenon motivates theoretical investigations of the underlying mechanisms. Here, a hydrogen enhanced cross-slip (HECS) mechanism in the close vicinity of the grain boundaries is demonstrated by direct molecular dynamics simulations and theoretical calculations. To this end, the interaction of screw dislocations with a variety of symmetric tilt grain boundaries in H-charged and H-free bicrystalline nickel is examined. The presence of segregated H atoms at the grain boundaries induces a stress field in their vicinity, and thus,- the barrier for cross-slip of screw dislocations considerably decreases. The enhanced cross-slip of dislocations facilitates the formation of jogs on bowedout dislocations. These jogs can form vacancies during the glide process. This mechanism of defect production shows nanoscale evidence of enhanced vacancy formation and subsequent increase in the free volume along the grain boundaries in the presence of H.