arXiv subjects

Bjørk Hammer

Publications and source records attributed to Bjørk Hammer.

At least 19 recordsLinked to original sources

arXiv2025

Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence

Machine learning interatomic potentials have become an indispensable tool for materials science, enabling the study of larger systems and longer timescales. State-of-the-art models are generally graph neural networks that employ message passing to iteratively update atomic embeddings that are ultimately used for predicting properties. In this work we extend the message passing formalism with the inclusion of a continuous variable that accounts for fractional atomic existence. This allows us to calculate the gradient of the Gibbs free energy with respect to both the Cartesian coordinates of atoms and their existence. Using this we propose a gradient-based grand canonical optimization method and document its capabilities for a Cu(110) surface oxide.

cond-mat.mtrl-sci↗

arXiv2025

Atomistic Generative Diffusion for Materials Modeling

We present a generative modeling framework for atomistic systems that combines score-based diffusion for atomic positions with a novel continuous-time discrete diffusion process for atomic types. This approach enables flexible and physically grounded generation of atomic structures across chemical and structural domains. Applied to metallic clusters and two-dimensional materials using the QCD and C2DB datasets, our models achieve strong performance in fidelity and diversity, evaluated using precision-recall metrics against synthetic baselines. We demonstrate atomic type interpolation for generating bimetallic clusters beyond the training distribution, and use classifier-free guidance to steer sampling toward specific crystallographic symmetries in two-dimensional materials. These capabilities are implemented in Atomistic Generative Diffusion (AGeDi), an open-source, extensible software package for atomistic generative diffusion modeling.

physics.comp-ph↗

arXiv2025

Active Δ-learning with universal potentials for global structure optimization

Universal machine learning interatomic potentials (uMLIPs) have recently been formulated and shown to generalize well. When applied out-of-sample, further data collection for improvement of the uMLIPs may, however, be required. In this work we demonstrate that, whenever the envisaged use of the MLIPs is global optimization, the data acquisition can follow an active learning scheme in which a gradually updated uMLIP directs the finding of new structures, which are subsequently evaluated at the density functional theory (DFT) level. In the scheme, we augment foundation models using a Δ-model based on this new data using local SOAP-descriptors, Gaussian kernels, and a sparse Gaussian Process Regression model. We compare the efficacy of the approach with different global optimization algorithms, Random Structure Search, Basin Hopping, a Bayesian approach with competitive candidates (GOFEE), and a replica exchange formulation (REX). We further compare several foundation models, CHGNet, MACE-MP0, and MACE-MPA. The test systems are silver-sulfur clusters and sulfur-induced surface reconstructions on Ag(111) and Ag(100). Judged by the fidelity of identifying global minima, active learning with GPR-based Δ-models appears to be a robust approach. Judged by the total CPU time spent, the REX approach stands out as being the most efficient.

cond-mat.mtrl-sci↗

arXiv2025

Anharmonic infrared spectra of cationic pyrene and superhydrogenated derivatives

Studying the anharmonicity in the infrared (IR) spectra of polycyclic aromatic hydrocarbons (PAHs) at elevated temperatures is important to understand vibrational features and chemical properties of interstellar dust, especially in the James Webb Space Telescope (JWST) era. We take pyrene as an example PAH and investigate how different degrees of superhydrogenation affects the applicability of the harmonic approximation and the role of temperature in IR spectra of PAHs. This is achieved by comparing theoretical IR spectra generated by classical molecular dynamics (MD) simulations and experimental IR spectra obtained via gas-phase action spectroscopy which utilizes the Infrared Multiple Photon Dissociation (IRMPD). All simulations are accelerated by a machine learning interatomic potential, in order to reach first principle accuracies while keeping low computational costs. We have found that the harmonic approximation with empirical scaling factors is able to reproduce experimental band profile of pristine and partially superhydrogenated pyrene cations. However, a MD-based anharmonic treatment is mandatory in the case of fully superhydrogenated pyrene cation for matching theory and experiment. In addition, band shifts and broadenings as the temperature increases are investigated in detail. Those findings may aid in the interpretation of JWST observations on the variations in band positions and widths of interstellar dust.

physics.chem-ph↗

arXiv2025

Cascading symmetry constraint during machine learning-enabled structural search for sulfur induced Cu(111)-$(\sqrt{43}\times\sqrt{43})$ surface reconstruction

In this work, we investigate how exploiting symmetry when creating and modifying structural models may speed up global atomistic structure optimization. We propose a search strategy in which models start from high symmetry configurations and then gradually evolve into lower symmetry models. The algorithm is named cascading symmetry search and is shown to be highly efficient for a number of known surface reconstructions. We use our method for the sulfur induced Cu (111) $(\sqrt{43}\times\sqrt{43})$ surface reconstruction for which we identify a new highly stable structure which conforms with experimental evidence.

cond-mat.mtrl-sci↗

arXiv2025

$Δ$-model correction of Foundation Model based on the models own understanding

Foundation models of interatomic potentials, so called universal potentials, may require fine-tuning or residual corrections when applied to specific subclasses of materials. In the present work, we demonstrate how such augmentation can be accomplished via $Δ$-learning based on the representation already embedded in the universal potentials. The $Δ$-model introduced is a Gaussian Process Regression (GPR) model and various types of aggregation (global, species-separated, and atomic) of the representation vector are discussed. Employing a specific universal potential, CHGNet [Deng et al., Nat. Mach. Intell. 5, 1031 (2023)], in a global structure optimization setting, we find that it correctly describes the energetics of the "8" Cu oxide, which is an ultra-thin oxide film on Cu(111). The universal potential model even predicts a more favorable structure compared to that discussed in recent DFT-based literature. Moving to sulfur adatom overlayers on Cu(111), Ag(111), and Au(111) the CHGNet model, however, requires corrections. We demonstrate that these are efficiently provided via the GPR-based $Δ$-model formulated on the CHGNet's own internal atomic embedding representation. The need for corrections is tracked to the scarcity of metal-sulfur atomic environments in the materials project database that CHGNet is trained on leading to an overreliance on sulfur-sulfur atomic environments. Other universal potentials trained on the same data, MACE-MP0, SevenNet-0, and ORB-v2-only-MPtrj show similar behavior, but with varying degrees of error, demonstrating the general need for augmentation schemes for universal potential models.

cond-mat.mtrl-sci↗

arXiv2025

Water Nucleation via Transient Bonds to Oxygen Functionalized Graphite

We present a study the initial stages of ice growth on pristine and oxygen-functionalized highly oriented pyrolytic graphite (O-HOPG), combining low-temperature scanning tunneling microscopy (LT-STM) and machine-learning structural searches. LT-STM images show that oxygen atoms act as nucleation sites for ice growth, and that the size, structure and porosity of the nanometer-sized ice clusters depend strongly on the growth temperature. Machine learning-assisted structural searches and first-principles energy calculations confirm that clusters of water molecules are likely to bind to chemisorbed oxygen atoms through hydrogen bonding. During the early stages of the cluster growth clusters of water molecules are likely to be immobilized by binding to more than one chemisorbed oxygen atom through hydrogen bonding. However, the energy gain by hydrogen bond formation of a molecule, upon incorporation into smaller clusters only bound to a single oxygen atom, is large enough to induce cluster diffusion and favor the growth of larger ice clusters. Our results demonstrate that the mobility of water molecules is significantly lowered in the presence of defects on the surface. The observed lower mobility on defected carbon presented here offers an enhanced understanding of macroscopic anti-icing properties observed for functionalized HOPG under ambient conditions and provides insight into the early stages of ice growth on dust grain surfaces in interstellar space.

astro-ph.GA↗

arXiv2024

Augmentation of Universal Potentials for Broad Applications

Universal potentials open the door for DFT level calculations at a fraction of their cost. We find that for application to systems outside the scope of its training data, CHGNet\cite{deng2023chgnet} has the potential to succeed out of the box, but can also fail significantly in predicting the ground state configuration. We demonstrate that via fine-tuning or a $Δ$-learning approach it is possible to augment the overall performance of universal potentials for specific cluster and surface systems. We utilize this to investigate and explain experimentally observed defects in the Ag(111)-O surface reconstruction and explain the mechanics behind its formation.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating structure search using atomistic graph-based classifiers

We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of 2-dimensional pyroxene, 3-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles.

physics.chem-ph↗

arXiv2024

Efficient ensemble uncertainty estimation in Gaussian Processes Regression

Reliable uncertainty measures are required when using data based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian Process Regression type MLIP a stochastic uncertainty measure akin to the query-by-committee approach often used in conjunction with neural network based MLIPs. The uncertainty measure is coined \textit{"label noise"} ensemble uncertainty as it emerges from adding noise to the energy labels in the training data. We find that this method of calculating an ensemble uncertainty is as well calibrated as the one obtained from the closed-form expression for the posterior variance when the sparse GPR is treated as a projected process. Comparing the two methods, our proposed ensemble uncertainty is, however, faster to evaluate than the closed-form expression. Finally, we demonstrate that the proposed uncertainty measure acts better to support a Bayesian search for optimal structure of Au$_{20}$ clusters.

physics.comp-ph↗

arXiv2024

Generative diffusion model for surface structure discovery

We present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by including masked atoms and $z$-directional confinement. Using a rotational equivariant neural network architecture, we design a method that trains a denoiser-network for diffusion alongside a force-field for guided sampling of low-energy surface phases. An effective data-augmentation scheme for training the denoiser-network is proposed to scale generation far beyond structure sizes represented in the training data. We showcase the generative model by investigating multiple surface systems and propose an atomistic structure model for a previously unknown silver-oxide domain-boundary of unprecedented size.

physics.comp-ph↗

arXiv2024

Generating candidates in global optimization algorithms using complementary energy landscapes

Global optimization of atomistic structure rely on the generation of new candidate structures in order to drive the exploration of the potential energy surface (PES) in search for the global minimum energy (GM) structure. In this work, we discuss a type of structure generation, which locally optimizes structures in complementary energy (CE) landscapes. These landscapes are formulated temporarily during the searches as machine learned potentials (MLPs) using local atomistic environments sampled from collected data. The CE landscapes are deliberately incomplete MLPs that rather than mimicking every aspect of the true PES are sought to become much smoother, having only few local minima. This means that local optimization in the CE landscapes may facilitate identification of new funnels in the true PES. We discuss how to construct the CE landscapes and we test their influence on global optimization of a reduced rutile SnO2(110)-(4x1) surface, and an olivine (Mg2SiO4)4 cluster for which we report a new global minimum energy structure.

physics.chem-ph↗

arXiv2023

A machine learning potential for simulating infrared spectra of nanosilicate clusters

The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here we apply an accurate and efficient protocol to collect training data for constructing a neural network based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics (MD) simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium (ISM) and in circumstellar environments.

physics.chem-ph↗

arXiv2022

Top-down formation of ethylene from fragmentation of superhydrogenated polycyclic aromatic hydrocarbons

Fragmentation is an important decay mechanism for polycyclic aromatic hydrocarbons (PAHs) under harsh interstellar conditions and represents a possible formation pathway for small molecules such as H2, C2H2, C2H4. Our aim is to investigate the dissociation mechanism of superhydrogenated PAHs that undergo energetic processing and the formation pathway of small hydrocarbons. We obtain, experimentally, the mass distribution of protonated tetrahydropyrene (C16H15 , py+5H+) and protonated hexahydropyrene (C16H17+, py+7H+) upon collision induced dissociation (CID). The IR spectra of their main fragments are recorded by infrared multiple-photon dissociation (IRMPD). Extended tight-binding (GFN2-xTB) based molecular dynamics simulations are performed in order to provide the missing structure information in experiment and identify fragmentation pathways. The pathways for fragmentation are further investigated at a hybrid-density functional theory (DFT) and dispersion corrected level. A strong signal for loss of 28 mass units of py+7H+ is observed both in the CID experiment and the MD simulation, while py+5H+ shows negligible signal for the product corresponding to a mass loss of 28. The 28 mass loss from py+7H+ is assigned to the loss of ethylene (C2H4) and a good fit between the calculated and experimental IR spectrum of the resulting fragment species is obtained. Further DFT calculations show favorable kinetic pathways for loss of C2H4 from hydrogenated PAH configurations involving three consecutive CH2 molecular entities. This joint experimental and theoretical investigation proposes a chemical pathway of ethylene formation from fragmentation of superhydrogenated PAHs. This pathway is sensitive to hydrogenated edges (e.g. the degree of hydrogenation and the hydrogenated positions). The inclusion of this pathway in astrochemical models may improve the estimated abundance of ethylene.

astro-ph.GA↗

arXiv2022

Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory

Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid states chemistry and physics. Pair distribution function (PDF) analysis of X-Ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases a reliable structural motif is needed as starting configuration for structure refinements. Here, we present an algorithm that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model that combines density functional theory (DFT) calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape. Due to the nature of this landscape, even metastable configurations can be determined.

cond-mat.mtrl-sci↗

arXiv2022

Atomistic Global Optimization X: A Python package for optimization of atomistic structures

Modelling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual identity and position of all involved atoms. Obtaining such information for macro-molecules, nano-particles, clusters, and for the surface, interface, and bulk phases of amorphous and solid materials represents a difficult high dimensional global optimization problem. The rise of machine learning techniques in materials science has, however, led to many compelling developments that may speed up such structure searches. The complexity of the new methods have established the necessity for an efficient way of experimenting with and assembling them into global optimization algorithms. In this paper we introduce the Atomistic Global Optimization X (AGOX) framework and code, as a customizable approach to building efficient global optimization algorithms. A modular way of expressing global optimization algorithms is described and modern programming practices are used to enable that modularity in the freely available AGOX python package. Two examples of global optimization problems are analyzed: One that is computationally inexpensive which is used to showcase that AGOX enables the expression of multiple global optimization algorithms. As the other example, AGOX is used for solving a complex atomistic optimization problem for a metal-nitride nano-cluster embedded in a graphene sheet as described at the density functional theory (DFT) level.

physics.chem-ph↗

arXiv2022

Atomistic structure search using local surrogate mode

We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based on a the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch $k$-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic system including molecules, nano-particles, surface supported clusters and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches.

physics.chem-ph↗

arXiv2022

Dimerization of dehydrogenated polycyclic aromatic hydrocarbons on graphene

Dimerization of polycyclic aromatic hydrocarbons (PAHs) is an important, yet poorly understood, step in the on-surface synthesis of graphene (nanoribbon), soot formation, and growth of carbonaceous dust grains in the interstellar medium (ISM). The on-surface synthesis of graphene and the growth of carbonaceous dust grains in the ISM require the chemical dimerization in which chemical bonds are formed between PAH monomers. An accurate and cheap method of exploring structure rearrangements is needed to reveal the mechanism of chemical dimerization on surfaces. This work has investigated the chemical dimerization of two dehydrogenated PAHs (coronene and pentacene) on graphene via an evolutionary algorithm augmented by machine learning surrogate potentials and a set of customized structure operators. Different dimer structures on surfaces have been successfully located by our structure search methods. Their binding energies are within the experimental errors of temperature programmed desorption measurements. The mechanism of coronene dimer formation on graphene is further studied and discussed.

physics.chem-ph↗