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Found 21 papers, 8 papers with code
Encrypted machine learning of molecular quantum properties
However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption.
Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids
The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology.
Chemical Physics
Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
To generate the input representation for a new query compound, FML requires approximate and short molecular dynamics runs.
Chemical Physics
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal.
Chemical Physics
Simplifying inverse material design problems for fixed lattices with alchemical chirality
Massive brute-force compute campaigns relying on demanding ab initio calculations routinely search for novel materials in chemical compound space, the vast virtual set of all conceivable stable combinations of elements and structural configurations which form matter.
Chemical Physics
Effects of perturbation order and basis set on alchemical predictions
Alchemical perturbation density functional theory has been shown to be an efficient and computationally inexpensive way to explore chemical compound space.
Chemical Physics
Neural networks and kernel ridge regression for excited states dynamics of CH$_2$NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models
As an ultimate test for our machine learning models, we carry out excited-state dynamics simulations based on the predicted energies, forces and couplings and, thus, show the scopes and possibilities of machine learning for the treatment of electronically excited states.
Rapid and accurate molecular deprotonation energies from quantum alchemy
We assess the applicability of Alchemical Perturbation Density Functional Theory (APDFT) for quickly and accurately estimating deprotonation energies.
Chemical Physics
Atoms in molecules from alchemical perturbation density functional theory
Based on thermodynamic integration we introduce atoms in molecules (AIM) using the orbital-free framework of alchemical perturbation density functional theory (APDFT).
Chemical Physics
Alchemical perturbation density functional theory (APDFT)
We introduce an orbital free electron density functional approximation based on alchemical perturbation theory.
Chemical Physics
Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited
Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multi-level combination (C) technique, to combine various levels of approximations made when calculating molecular energies within quantum chemistry.
Chemical Physics
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters---optimized once and for all across compounds.
Chemical Physics
The "DNA" of chemistry: Scalable quantum machine learning with "amons"
In analogy to the DNA sequence in a gene encoding its function, constituting amons encode a query molecule's properties.
Chemical Physics
Machine learning prediction errors better than DFT accuracy
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules.
Ranked #18 on
Formation Energy
on QM9
Constant Size Molecular Descriptors For Use With Machine Learning
A set of molecular descriptors whose length is independent of molecular size is developed for machine learning models that target thermodynamic and electronic properties of molecules.
Understanding molecular representations in machine learning: The role of uniqueness and target similarity
The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation.
Chemical Physics
Machine Learning, Quantum Mechanics, and Chemical Compound Space
We review recent studies dealing with the generation of machine learning models of molecular and solid properties.
Chemical Physics
Machine learning for many-body physics: efficient solution of dynamical mean-field theory
Machine learning methods for solving the equations of dynamical mean-field theory are developed.
Electronic Spectra from TDDFT and Machine Learning in Chemical Space
For a training set of 10 thousand molecules, CC2 excitation energies can be reproduced to within $\pm$0. 1 eV for the remaining molecules.
Chemical Physics
Crystal Structure Representations for Machine Learning Models of Formation Energies
We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids.
Chemical Physics
Machine learning for many-body physics: The case of the Anderson impurity model
Machine learning methods are applied to finding the Green's function of the Anderson impurity model, a basic model system of quantum many-body condensed-matter physics.
