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Topics in computational chemistry
Related publications (1,000)
Efficient and insightful descriptors for representing molecular and material space
Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...
EPFL2024Scanning Tunneling Microscopy for Molecules: Effects of Electron Propagation into Vacuum
Using scanning tunneling microscopy (STM), we experimentally and theoretically investigate isolated platinum phthalocyanine (PtPc) molecules adsorbed on an atomically thin NaCl(100) film vapor deposited on Au(111). We obtain good agreement between theory a ...
Amer Chemical Soc2024Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties
Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...
EPFL2024Machine learning-aided generative molecular design
Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...
Nature Portfolio2024Thermal transport of glasses via machine learning driven simulations
Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...
Lausanne2024Orbital-Resolved DFT plus U for Molecules and Solids
Nicola Marzari, Iurii Timrov, Eric Macke
We present an orbital-resolved extension of the Hubbard U correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly improved, particula ...
Amer Chemical Soc2024Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis
We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...
Amer Chemical Soc2024Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder
Ursula Röthlisberger, Justin Villard, Martin Peter Bircher
The accurate representation of the structural and dynamical properties of water is essential for simulating the unique behavior of this ubiquitous solvent. Here we assess the current status of describing liquid water using ab initio molecular dynamics, wit ...
Cambridge2024Solvation Free Energies from Machine Learning Molecular Dynamics
Nicola Marzari, Nicéphore Arthur François Bonnet
The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...
Amer Chemical Soc2024Excited State-Specific CASSCF Theory for the Torsion of Ethylene
State-specific complete active space self-consistent field (SS-CASSCF) theory has emerged as a promising route to accurately predict electronically excited energy surfaces away from molecular equilibria. However, its accuracy and practicality for chemical ...
Amer Chemical Soc2024