Publications related to Connector theory for reusing model results to determine materials properties

Related publications (9)

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

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 ...

ROYAL SOC CHEMISTRY2025

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

Egor Rumiantsev

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 ...

BMC2025

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

Henrik Moodysson Rønnow, Arnaud Magrez, Ivica Zivkovic, Jian Rui Soh, Aman Sharma, Virgile Yves Favre

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 ...

Nature Research2024

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

Philip Johannes Walter Moll, Matthias Carsten Putzke

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 ...

Aip Publishing2024

Electronic Excited States from Physically Constrained Machine Learning

Michele Ceriotti, Jigyasa Nigam, Divya Suman

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 ...

2024

The energy landscape of magnetic materials

Nicola Marzari, Louis Maurice Ponet, Enrico Di Lucente

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 ...

2024

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

Giuseppe Carleo, Jannes Willy E. Nys, Gabriel Maria Pescia, Alessandro Sinibaldi

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 ...

Nature Research2024

Leveraging large language models for predictive chemistry

Berend Smit, Philippe Schwaller, Kevin Maik Jablonka, Andres Adolfo Ortega Guerrero

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 ...

Nature Portfolio2024

Type-I antiferromagnetic Weyl semimetal InMnTi2

Nicola Marzari, Luca Binci, Davide Grassano

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 ...

Amer Physical Soc2024