Patrick Rinke

We present a velocity gauge implementation for real-time time-dependent density functional theory (RT-TDDFT) calculations of electronic circular dichroism (ECD) spectra within the linear combination of atomic orbitals framework of the GPAW code. In contrast to the commonly used length gauge, which exhibits a pronounced dependence on the origin of the coordinate system, our velocity gauge implementation produces origin-independent ECD spectra already for moderately sized basis sets. The computational efficiency of the RT-TDDFT implementation in GPAW enables efficient ECD simulations of large finite systems, making chiroptical analyses of molecular and molecule–nanoparticle assemblies exceeding 1000 atoms feasible.

Coupled optical and magnetic excitations can give rise to remarkably strong magneto-optic responses. This is particularly evident in van der Waals magnets, such as the antiferromagnet CrSBr, where excitons and magnons emerge from the same electronic orbitals. While previous work has primarily focused on uncovering the origin of the resulting exciton-magnon interactions, the influence of photonic effects has received comparatively little attention. Here, we use numerical simulations to disentangle exciton-magnon coupling from the exciton-mediated magnon-photon interactions observed in optical experiments. By simulating realistic scenarios using parameters determined by experiments, we illustrate the strong dependence of these interactions on photonic interference and dispersion effects near excitonic resonances. Such effects shape the optical response to coherent magnons and make it intrinsically nonlinear in the magnon-induced exciton energy shift. Thermal magnons, which have a particularly pronounced impact on excitons, are found to even produce qualitatively different trends in optical signatures. Depending on weak or strong coupling of excitons and photons, the same exciton-magnon interaction can lead to a redshift of optical modes, a nearly vanishing response, or their blueshift. Finally, we demonstrate steps towards optimizing the multiparameter problem of efficient magnon-photon transduction using a machine-learning approach.

Materials science is at the crossroad between fundamental and applied sciences. Whether enabling clean energy, next-generation computing, or advanced manufacturing, it shapes the tools we use and the systems we build. As our societies undergo rapid digital, environmental, and technological transitions, materials science becomes even more central. It's a space where innovation can respond to practical challenges while aligning with broader social values. In the European context, that means supporting sustainability, openness, and solidarity -while also strengthening competitiveness.This roadmap explores how digital tools-especially simulation, data science, and AI-are transforming materials research, in connection with the twin digital and energy transition. The digital transition refers to the widespread adoption of digital technologies and data-driven methods across sectors, while the green (or energy) transition focuses on shifting toward sustainable, lowcarbon energy systems-together forming what is often called the twin transition, a joint effort to make economies both smarter and more sustainable.The Roadmap outlines both the technical directions and the cultural shifts needed to make this transformation inclusive and effective. Chapters span from atomic-scale simulations to advanced experimentation, from reproducibility to intelligent optimization, and from institutional reform to education for the next generation.Importantly, this isn't a single viewpoint. The document brings together a wide range of voices: researchers from different disciplines, working across length and time scales. It includes early-career scientists and senior experts. Enabled by activities supported by European Cooperation in Science and Technology (COST), it reflects a commitment to gender and geographic diversity. This plurality doesn't just enrich the content-it makes the vision more robust and relevant.We hope this collection serves not just as a guide, but as an invitation to collaborate-across fields, sectors, and borders-as we reimagine the future of materials science in a digital age.

The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to investigate the electronic properties of defective semiconductors at finite temperatures efficiently. We develop an active learning approach that integrates two advanced equivariant graph neural networks: MACE for atomic energies and forces and DeepH-E3 for the electronic Hamiltonian. Focusing on representative point defects in GaAs, we demonstrate computational accuracy comparable to density functional theory at a fraction of the computational cost, predicting the temperature-dependent band gap of defective GaAs directly from larger scale molecular dynamics trajectories with an accuracy of few tens of meV. Our results highlight the potential of equivariant neural networks for accurate atomic-scale predictions in complex, dynamically evolving materials.

The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are particularly affected because they often rely on dictionary-based descriptors tied to specific molecular substructures, which currently fail to capture the full range of organic atmospheric compounds, including large, highly oxidized molecules common in the atmosphere. We introduce ATMOMACCS, an interpretable descriptor combining the 166 binary keys of the MACCS fingerprint with motifs inspired by the SIMPOL method for estimating saturation vapor pressures. We show that ATMOMACCS outperforms the RDKit topological fingerprint in kernel ridge regression models, improving predictions of saturation vapor pressures (7%, 8%, 29%, and 43% error reduction), equilibrium partition coefficients (5% and 9% error reduction), glass transition temperatures (22% error reduction), and enthalpies of vaporization (61% error reduction) on six datasets with atmospheric compounds. Feature analysis shows that saturation vapor pressure and partition coefficients are governed by carbon number and oxygen-related features, whereas other phase-transition properties (e.g., enthalpy of vaporization and glass transition temperature) depend on carbon–hydrogen bond types and the presence of heteroatoms other than oxygen. This highlights the generalizability of ATMOMACCS across different datasets and properties as an interpretable molecular descriptor.

Data-driven materials discovery to accelerate the development of new catalysts for the green transition shows great promise, but requires machine-interpretable experimental data. For this purpose, we compiled the TheMeCat dataset for the thermocatalytic conversion of CO₂ to methanol. TheMeCat is curated from experimental literature sources, and designed for hybrid computational and experimental catalyst workflows. The dataset captures key catalyst features, process parameters, and methanol conversion efficiencies. Upon detailed analysis of the generated dataset and the associated variability in reported values, we perform targeted error analysis to assess consistency and reinforce the need for clear, structured reporting.

The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to investigate the electronic properties of defective semiconductors at finite temperatures efficiently. We develop an active learning approach that integrates two advanced equivariant graph neural networks: MACE for atomic energies and forces and DeepH-E3 for the electronic Hamiltonian. Focusing on representative point defects in GaAs, we demonstrate computational accuracy comparable to density functional theory at a fraction of the computational cost, predicting the temperature-dependent band gap of defective GaAs directly from larger scale molecular dynamics trajectories with an accuracy of few tens of meV. Our results highlight the potential of equivariant neural networks for accurate atomic-scale predictions in complex, dynamically evolving materials.

Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However, interpreting IR spectra often requires high-fidelity simulations, such as density functional theory based ab-initio molecular dynamics, which are computationally expensive and therefore limited in the tractable system size and complexity. In this work, we present a novel active learning-based framework, implemented in the open-source software package PALIRS, for efficiently predicting the IR spectra of small catalytically relevant organic molecules. PALIRS leverages active learning to train a machine-learned interatomic potential, which is then used for machine learning-assisted molecular dynamics simulations to calculate IR spectra. PALIRS reproduces IR spectra computed with ab-initio molecular dynamics accurately at a fraction of the computational cost. PALIRS further agrees well with available experimental data not only for IR peak positions but also for their amplitudes. This advancement with PALIRS enables high-throughput prediction of IR spectra, facilitating the exploration of larger and more intricate catalytic systems and aiding the identification of novel reaction pathways.

New particle formation (NPF) is a major source of atmospheric aerosol particles, significantly influencing particle number concentrations in urban environments. High condensation and coagulation sinks at highly trafficked roadside sites should suppress NPF due to the low survival probability of clusters and new particles, however, observations show that roadside NPF is frequent and intense. Here, we investigate NPF at an urban background and roadside site in Central Europe using simultaneous measurements of sulfuric acid, amines, highly oxygenated organic molecules (HOMs), and particle number size distributions. We demonstrate that sulfuric acid and amines, particularly traffic-derived C₂-amines, are the primary participants in particle formation. C₂-amine concentrations at the roadside are enhanced by over a factor of 4 relative to the background, overcoming the effect of enhanced coagulation and condensation sinks. Using machine learning we identify a further but uncertain enhancing role of HOMs. These findings reveal the critical role of traffic emissions in urban NPF.