333 citations · 833 across the 12 of their papers we have counts for
12 papers
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao +8
We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calcul…
Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry
Zhuoran Qiao, Anders S. Christensen, Matthew Welborn +3
Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. By developing a physic…
An assessment of the structural resolution of various fingerprints commonly used in machine learning
Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen +6
Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurat…
On the role of gradients for machine learning of molecular energies and forces
Anders S. Christensen, O. Anatole von Lilienfeld
The accuracy of any machine learning potential can only be as good as the data used in the fitting process. The most efficient model therefore selects the training data that will y…
Operators in Machine Learning: Response Properties in Chemical Space
Anders S. Christensen, Felix A. Faber, O. Anatole von Lilienfeld
The role of response operators is well established in quantum mechanics. We investigate their use for universal quantum machine learning models of response properties in molecules.…
Alchemical and structural distribution based representation for improved QML
Felix A. Faber, Anders S. Christensen, Bing Huang +1
We introduce a representation of any atom in any chemical environment for the generation of efficient quantum machine learning (QML) models of common electronic ground-state proper…
Inferential protein structure determination and refinement using fast, electronic structure based backbone amide chemical shift predictions
Anders S. Christensen
This report covers the development of a new, fast method for calculating the backbone amide proton chemical shifts in proteins. Through quantum chemical calculations, structure-bas…
Improving intermolecular interactions in DFTB3 using extended polarization from chemical-potential equalization
Anders S. Christensen, Marcus Elstner, Qiang Cui
Semi-empirical quantum mechanical methods traditionally expand the electron density in a minimal, valence-only electron basis set. The minimal-basis approximation causes molecular…
Protein Structure Determination Using Chemical Shifts
Anders S. Christensen
In this PhD thesis, a novel method to determine protein structures using chemical shifts is presented.
Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics
Anders S. Christensen, Troels E. Linnet, Mikael Borg +4
We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts - sensitive probes of the geometry of key hydrogen bonds that det…
Hybrid RHF/MP2 geometry optimizations with the Effective Fragment Molecular Orbital Method
Anders S. Christensen, Casper Steinmann, Dmitri G. Fedorov +1
The frozen domain effective fragment molecular orbital method is extended to allow for the treatment of a single fragment at the MP2 level of theory. The approach is applied to the…
Interface of the polarizable continuum model of solvation with semi-empirical methods in the GAMESS program
Casper Steinmann, Kristoffer L. Blædel, Anders S. Christensen +1
An interface between semi-empirical methods and the polarized continuum model (PCM) of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem…