黑料福利网

Imagine a future where light itself powers our devices 鈥� a crystal charging your smartphone, or one that doubles solar panel efficiency.

These semiconductors capture light and turn it into energy as excited electrons. To work best, they must absorb a broad spectrum of light and conduct charge efficiently.

Our research uses computer simulations accelerated by machine learning to predict and design such materials before they exist, accelerating the path toward devices powered directly by sunlight.

Our research integrates first-principles theory and methods with artificial intelligence to accelerate the discovery and design of functional materials for energy and information technologies.

Using advanced electronic-structure theory and methods, we link atomic structures, chemical and physical responses, and device performance. Machine learning extends this predictive framework to longer timescales, more complex materials, and richer physics beyond conventional simulations, while also strengthening connections to experiment.

This synergy allows us to uncover and control emergent quantum phenomena in semiconducting and optoelectronic materials, guiding their optimization for next-generation energy and information technologies.

Emergent optoelectronic properties in novel semiconductors
We investigate how light鈥搈atter interactions, defects, and phase stability govern the optoelectronic behavior of hybrid semiconductors such as perovskites. Our insights guide experimental strategies to improve stability and efficiency and inform industrial R&D toward scalable photovoltaics and LEDs.

Spin transport in chiral and quantum materials
We explore how chirality, symmetry breaking, and non-equilibrium effects drive spin-polarized transport. This research advances chiral-induced spin selectivity and spin-optoelectronics, enabling devices such as spin LEDs and chiral photodetectors, while uncovering how chirality shapes quantum coherence and information flow.

Spin electrochemistry for sustainable catalysis
We study how spin states in correlated, alter-magnetic, and chiral materials influence catalytic reactions in electrochemical systems. By combining spin transport theory, surface chemistry, and machine learning, we seek to enhance efficiency and selectivity in e.g., water splitting and CO鈧� reduction, laying the groundwork for spin-aware catalysts in sustainable energy technologies.

We are constantly looking for enthusiastic and bright researchers at all levels (BSc, MSc and PhD and Postdoctoral). We invite motivated students to discuss with the PI  and our  for the latest research projects as the field develops fast and new exciting research questions emerge quickly.

The courses we offer are:
Advanced Materials Modelling using Multiscale Methods (3MQ110), Nanomaterials: Physics and Characterization (34NPC), and Fundamentals of Energy Harvesting and Storage (34FEH).

Metal halide perovskites have been the focus of many computational studies over the past few years. The bulk of these investigations were done using methods based on quantum mechanics (QM). However, the computational cost of QM methods is high, limiting the investigation of the dynamical properties, as the materials are much 鈥渟ofter鈥� than traditional inorganic semiconductors.  Such softness gives rise to unusual lattice dynamics and novel behaviors. All chemical and physical properties evolve and affect one another constantly under external stimuli (heat, light, electrical bias).

To capture the complex dynamical properties, we perform molecular dynamics (MD) simulations with several levels of efficiency. Ab-initio MD based on DFT, MD base on DFTB as well as MD based on force fields. We have recently developed a set of transferable and successfully applied it to simulate ion migration. To be able to simulate chemistry reactions during the degradation, we have extended our effort to reactive force field (ReaxFF). On the left-hand side, phase transition of a common perovskite (CsPbI3) under thermal stress is demonstrated using our newly developed .

Extensive methodology development is still required. Advances in machine learning algorithms and computational power have opened the way towards accelerated force field development as well as high-throughput screening.

Perovskite solar cells have achieved an impressive power conversion efficiency of 25.2% in 2019. While its efficiency has become comparable with Si ones, their long-term stability are still far behind. Beside the perovskite absorbers, the interfaces between the perovskite and the charge transport layers are recognized as another important factor in determining both the efficiency and long-term stability.

Using DFT, we optimize energy level alignment in the whole devices, including those of charge transport layer, the perovskite absorbers, the electrodes. By or applying , better energy alignment can be achieved. We also study the roles of defects, and redox chemistry at several interfaces in the devices.

Our long-term ambition is to apply our multiscale approach to study dynamical properties that governs the long-term stability of the devices. Our goal is to design multifunctional interfaces combining the optimal optoelectronic properties with prolonged stability. Many inspiration of our research comes from our close interactions with experimental researchers at 黑料福利网 (PMP group) and knowledge institutes and industrial partners () . 

The potential of solar power is enormous: our planet intercepts some 173,000 terawatts of radiation from the sun at any time, 10,000 times more power than the planet鈥檚 population uses. Harnessing this almost endless power source has been the driving force of much at the Eindhoven University of Technology. The research covers a broad terrain of expertise and interests, ranging from the elemental building blocks of solar cells and upscaling of technology to industrial production, to enhancing the aesthetics of solar panels or application in solar-powered cars. And with success: it is estimated that almost one third of all solar cells worldwide contain technology pioneered by our researchers. We take you step by step through the whole chain: from fundamental research in the lab to the application in everyday life.