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Electronic excited states from physically-constrained machine learning
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... -
Electronic excited states from physically-constrained machine learning
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... -
SPAᴴM(a,b): encoding the density information from guess Hamiltonian in quantu...
Recently, we introduced a class of molecular representations for kernel-based regression methods — the spectrum of approximated Hamiltonian matrices (SPAᴴM) — that takes... -
Hidden orders and (anti-)magnetoelectric effects in Cr₂O₃ and α-Fe₂O₃
We present ab initio calculations of hidden magnetoelectric multipolar order in Cr₂O₃ and its iron-based analogue, α-Fe₂O₃. We show the presence of hidden... -
Building a consistent and reproducible database for adsorption evaluation in ...
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal’s postcombustion flue... -
Building a consistent and reproducible database for adsorption evaluation in ...
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal’s postcombustion flue... -
Physics-inspired equivariant descriptors of non-bonded interactions
One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size... -
Exploring energy landscapes of charge multipoles using constrained density fu...
We present a method to constrain local charge multipoles within density-functional theory. Such multipoles quantify the anisotropy of the local charge distribution around atomic... -
Robustness of local predictions in atomistic machine learning models
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is... -
Hund excitations and the efficiency of Mott solar cells
We study the dynamics of photoinduced charge carriers in semirealistic models of LaVO3 and YTiO3 polar heterostructures. It is shown that two types of impact ionization... -
Learning the exciton properties of azo-dyes
The ab initio determination of the character and properties of electronic excited states (ES) is the cornerstone of modern theoretical photochemistry. Yet, traditional ES... -
Ab-initio phase diagram and nucleation of gallium
Elemental gallium possesses several intriguing properties such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features... -
Structure-property maps with kernel principal covariates regression
Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building... -
The role of water in host-guest interaction
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field... -
How robust is the reversible steric shielding strategy for photoswitchable or...
A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and non-invasive way is to incorporate a photoresponsive unit into a catalytically... -
Diversifying databases of metal organic frameworks for high-throughput comput...
By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical... -
Helicity-dependent photocurrents in the chiral Weyl semimetal RhSi
Weyl semimetals are crystals in which electron bands cross at isolated points in momentum space. Associated with each crossing point (or Weyl node) is an integer topological... -
Incorporating long-range physics in atomic-scale machine learning
The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a... -
Quantum mechanical dipole moments in the QM7b, 21k molecules of QM9, and MuML...
Molecular dipole moments of the QM7b dataset, a random sample of 21'000 molecules from the QM9 dataset, and the MuML showcase set (including the four challenge series) described... -
Local kernel regression and neural network approaches to the conformational l...
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of...