Connector theory for reusing model results to determine materials properties

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The CO2/CO electroreduction reaction (CO2RR/CORR) to liquid products presents an enticing pathway to store intermittent renewable electricity. However, the selectivity for desirable high-value C3 products, such as n-propanol, remains unsatisfactory in the ...

The electron density is an important object in quantum chemistry that is crucial for many downstream tasks in drug design. Recent deep learning approaches predict the electron density around a molecule from atom types and atom positions. Most of these meth ...

Quantum spin liquids (QSLs) have become a key area of research in magnetism due to their remarkable properties, such as long-range entanglement, fractional excitations, and topologically protected phenomena. Recently, the search for QSLs has expanded into ...

Our understanding of quantum materials is commonly based on precise determinations of their electronic spectrum by spectroscopic means, most notably angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy. Both require atomicall ...

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

Magnetic materials can display many solutions to the electronic-structure problem, corresponding to different local or global minima of the energy functional. In Hartree-Fock or density-functional theory different single-determinant solutions lead to diffe ...

Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical and computational challenge. Our wor ...

Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemic ...

Topological materials have been a main focus of studies in the past decade due to their protected properties that can be exploited for the fabrication of new devices. Among them, Weyl semimetals are a class of topological semimetals with nontrivial linear ...

Connector theory for reusing model results to determine materials properties

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Cooperative promotion of electroreduction of CO to n-propanol by *CO enrichment and proton regulation

Lagnet: Better Electron Density Prediction for Lcao-based Data and Drug-like Substances

Dynamics of K2Ni2(SO4)3 governed by proximity to a 3D spin liquid model

Controlling crystal cleavage in focused ion beam shaped specimens for surface spectroscopy

Electronic Excited States from Physically Constrained Machine Learning

The energy landscape of magnetic materials

Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation

Leveraging large language models for predictive chemistry

Type-I antiferromagnetic Weyl semimetal InMnTi2