arXiv subjects

Joakim S. Jestilä

Publications and source records attributed to Joakim S. Jestilä.

4 recordsLinked to original sources

arXiv2026

MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces

Predicting how organic molecules adsorb, assemble, and interact on metal surfaces is central to surface chemistry and molecular electronics, particularly in the context of interpreting high-resolution scanning probe microscopy. Yet, the application of first-principles simulations to interfaces is hampered by the computational cost for evaluating the electronic structure for the large number of atoms typically involved. We hereby present MAD-SURF, a machine learning interatomic potential specifically tailored for molecular adsorption on coinage metal surfaces. Trained on a broad dataset spanning diverse molecules, adsorption motifs, surfaces, molecular dynamics trajectories and non-covalent aggregates, MAD-SURF achieves accuracy comparable to the underlying DFT reference while enabling simulations orders of magnitude faster than density functional theory. The model reliably reproduces energies, forces and adsorption geometries across the three coinage metal substrates. We demonstrate its capabilities on experimentally characterized systems, including organic monolayers, polycyclic aggregates, flexible biomolecules and the long-range herringbone reconstruction of gold. By merging accuracy, speed, and generalizability, MAD-SURF offers a practical framework for accelerating atomistic simulations and advancing data-driven workflows in surface science.

cond-mat.mtrl-sci↗

arXiv2024

Precise large-scale chemical transformations on surfaces: deep learning meets scanning probe microscopy with interpretability

Scanning Probe Microscopy (SPM) techniques have shown great potential in fabricating nanoscale structures endowed with exotic quantum properties achieved through various manipulations of atoms and molecules. However, precise control requires extensive domain knowledge, which is not necessarily transferable to new systems and cannot be readily extended to large-scale operations. Therefore, efficient and autonomous SPM techniques are needed to learn optimal strategies for new systems, in particular for the challenge of controlling chemical reactions and hence offering a route to precise atomic and molecular construction. In this paper, we developed a software infrastructure named AutoOSS (\textbf{Auto}nomous \textbf{O}n-\textbf{S}urface \textbf{S}ynthesis) to automate bromine removal from hundreds of Zn(II)-5,15-bis(4-bromo-2,6-dimethylphenyl)porphyrin (\ch{ZnBr2Me4DPP}) on Au(111), using neural network models to interpret STM outputs and deep reinforcement learning models to optimize manipulation parameters. This is further supported by Bayesian Optimization Structure Search (BOSS) and Density Functional Theory (DFT) computations to explore 3D structures and reaction mechanisms based on STM images.

cond-mat.mtrl-sci↗

arXiv2024

Direct imaging of carbohydrate stereochemistry

Carbohydrates, essential biological building blocks, exhibit functional mechanisms tied to their intricate stereochemistry. Subtle stereochemical differences, such as those between the anomers maltose and cellobiose, lead to distinct properties due to their differing glycosidic bonds; the former is digestible by humans, while the latter is not. This underscores the importance of precise structural determination of individual carbohydrate molecules for deeper functional insights. However, their structural complexity and conformational flexibility, combined with the high spatial resolution needed, have hindered direct imaging of carbohydrate stereochemistry. Here, we employ non-contact atomic force microscopy integrated with a data-efficient, multi-fidelity structure search approach accelerated by machine learning integration to determine the precise 3D atomic coordinates of two carbohydrate anomers. We observe that glycosidic bond stereochemistry regulates on-surface chiral selection in carbohydrate self-assemblies. The reconstructed models, validated against experimental data, provide reliable atomic-scale structural evidence, uncovering the origin of on-surface chirality from carbohydrate anomerism. Our study confirms that nc-AFM is a reliable technique for real-space discrimination of carbohydrate stereochemistry at the single-molecule level, providing a pathway for bottom-up investigations into the structure-property relationships of carbohydrates in biological research and materials science.

cond-mat.mtrl-sci↗

arXiv2023

Accelerated lignocellulosic molecule adsorption structure determination

Here, we present a study combining Bayesian optimisation structural inference with the machine learning interatomic potential NequIP to accelerate and enable the study of the adsorption of the conformationally flexible lignocellulosic molecules $β$-D-xylose and 1,4-$β$-D-xylotetraose on a copper surface. The number of structure evaluations needed to map out the relevant potential energy surfaces are reduced by Bayesian optimisation, while NequIP minimises the time spent on each evaluation, ultimately resulting in cost-efficient and reliable sampling of large systems and configurational spaces. Although the applicability of Bayesian optimisation for the conformational analysis of the more flexible xylotetraose molecule is restricted by the sample complexity bottleneck, the latter can be effectively bypassed with external conformer search tools, such as the Conformer-Rotamer Ensemble Sampling Tool, facilitating the subsequent lower dimensional global minimum adsorption structure determination. Finally, we demonstrate the applicability of the described approach to find adsorption structures practically equivalent to the density functional theory counterparts at a fraction of the computational cost.

physics.chem-ph↗