The institute was founded in 1983 by Erhard W. Fischer and Gerhard Wegner. Belonging to the Chemistry, Physics and Technology Section, it is one of the 80 institutes in the Max Planck Society (Max-Planck-Gesellschaft).
Layered two-dimensional conjugated polymers (2D CPs), or 2D conjugated covalent organic frameworks, are promising semiconductor materials for (opto)electronics and photocatalysis, but their performance is often limited by insufficient in-plane conjugation and poor charge transport. Guided by density functional theory calculations, we report two donor-acceptor-type 2D poly(arylene vinylene)s constructed from thienyl-benzodithiophene and diketopyrrolopyrrole units. These materials are predicted to exhibit strongly dispersive energy bands with ultralow in-plane effective masses (0.036 - 0.159 m0), enabling intrinsic charge mobilities approaching 2000 cm2 V-1 s-1. Solid-state Aldol-type 2D polycondensation yields crystalline materials with optical band gaps as narrow as 1.0 eV. Terahertz spectroscopy reveals long charge carrier scattering times of 76 fs and a high room-temperature mobility of 310 cm2 V-1 s-1, surpassing previously reported linear and 2D CP powder samples. This work highlights donor-acceptor engineering as an effective strategy to enhance charge transport in 2D CPs.
Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range electrostatic effects. We present a new electrostatic foundation model for molecular chemistry that extends the MACE architecture with explicit treatment of long-range interactions and electrostatic induction. Our approach combines local many-body geometric features with a non-self-consistent field formalism that updates learnable charge and spin densities through polarisable iterations to model induction, followed by global charge equilibration via learnable Fukui functions to control total charge and total spin. This design enables an accurate and physical description of systems with varying charge and spin states while maintaining computational efficiency. Trained on the OMol25 dataset of 100 million hybrid DFT calculations, our models achieve chemical accuracy across diverse benchmarks, with accuracy competitive with hybrid DFT on thermochemistry, reaction barriers, conformational energies, and transition metal complexes. Notably, we demonstrate that the inclusion of long-range electrostatics leads to a large improvement in the description of non-covalent interactions and supramolecular complexes over non-electrostatic models, including sub-kcal/mol prediction of molecular crystal formation energy in the X23-DMC dataset and a fourfold improvement over short-ranged models on protein-ligand interactions. The model's ability to handle variable charge and spin states, respond to external fields, provide interpretable spin-resolved charge densities, and maintain accuracy from small molecules to protein-ligand complexes positions it as a versatile tool for computational molecular chemistry and drug discovery.
Hydrophobic solid-water interfaces underpin processes in nanofluidics, electrochemistry, and energy technologies. Microscopic insights into these systems are often inferred from our understanding of the air-water interface, which is assumed to exhibit similar behavior. Here, we challenge this paradigm by combining heterodyne-detected vibrational sum-frequency generation spectroscopy with machine-learning molecular dynamics simulations at first-principles accuracy to investigate the graphene-NaCl(aq) interface as a prototypical hydrophobic solid-water system. Spectroscopic results suggest that ions have a minimal effect on the structure of the interfacial water, while simulations reveal that Na+ and Cl- accumulate densely at the surface. Together, these findings reveal a new adsorption mechanism that departs from the established air-water interface paradigm, where interfacial ion adsorption is typically associated with, and often detected through, a pronounced alteration of the interfacial water alignment and orientation. This difference arises because ions cannot penetrate the solid boundary and reside at a similar depth as the interfacial water molecules. As a consequence, large ion populations can be accommodated within the extended two-dimensional hydrogen-bond network at the interface, causing only minor local distortions but significant changes to its longer-range connectivity. These results reveal a distinct mechanism of electrolyte organization at aqueous-carbon interfaces, relevant to energy applications, where performance is highly sensitive to the local organization of interfacial water.
The training of foundational machine learning interatomic potentials (fMLIPs) relies on diverse databases with energies and forces calculated using ab initio methods. We show that fMLIPs trained on large datasets such as MPtrj, Alexandria, and OMat24 encode inconsistencies from the materials project's (MPs) selective use of the Hubbard U correction, which is applied to certain transition metals only if O or F atoms are present in the simulation cell. This inconsistent use of + U creates two incompatible potential energy surfaces (PES): a lower-energy GGA surface and a higher-energy GGA+ U one. When trained on both, MLIPs interpolate between them, leading to systematic underbinding, or even spurious repulsion, between U-corrected metals and oxygen- or fluorine-containing species. Models such as MACE-OMAT and -MPA exhibit repulsion between U-corrected metals and their oxides, limiting their value for studying catalysis and oxidation. We link the severity of this pathology to the oxygen number density in U-corrected training configurations. This explains why OMAT-trained models are most affected and suggests the issue might worsen as expanding future datasets increasingly include configurations with low oxygen content, such as those generated through combinatorial exploration of multi-element or defect-containing systems. Our simple per- U-corrected-atom shift aligns Perdew-Burke-Ernzerhof (PBE)+ U and PBE energies for identical structures, yielding a smoother PES compared to existing correction schemes, which target phase diagram accuracy. As a result, models trained on datasets with our shift applied exhibit smaller mean absolute errors for the adsorption energies of oxygen on U-corrected elemental slabs. Since datasets omitting + U entirely (e.g. MatPES, MP-ALOE) avoid these pathologies, we recommend excluding + U in future fMLIP datasets. For existing datasets, our post-hoc correction provides a low-cost improvement.
Electrochemical nitrate reduction represents a promising route for sustainable ammonia (NH3) production, yet its practical deployment is constrained by the limited efficiency of state-of-the-art electrocatalysts and immature system architectures. Here, we report a generalist copper-nickel-tungsten tri-component tandem electrocatalyst via a sequential microwave-hydrothermal deposition route. Under pulsed electrolysis conditions, the catalyst delivers a remarkable Faradaic efficiency of 97.1% and a record-high ammonia yield rate of 43.87 mg h-1 cm-2. Online differential electrochemical mass spectrometry (DEMS) identifies key intermediates and associated pathways, while density functional theory (DFT) calculations elucidate the cooperative roles of each component: the copper component facilitates nitrate adsorption and deoxygenation, the nickel component promotes water dissociation for steady *H supply, and the tungsten component serves as a dynamic *H reservoir. This synergy efficiently suppresses hydrogen evolution and enhances ammonia selectivity. Furthermore, coupling with glycerol valorization (to formic acid) as the anodic reaction demonstrates the potential for energy-efficient ammonia electrosynthesis. Collectively, this work offers both design strategies and mechanistic understanding for next-generation multi-component tandem electrocatalysts targeting advanced nitrogen-based chemical synthesis.
