29 citations · 162 across the 15 of their papers we have counts for
15 papers
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Generalized convolutional many body distribution functional representations
Danish Khan, O. Anatole von Lilienfeld
Modern machine learning (ML) models of chemical and materials systems with billions of parameters require vast training datasets and considerable computational efforts. Lightweight…
Elucidating atmospheric brown carbon -- Supplanting chemical intuition with exhaustive enumeration and machine learning
Enrico Tapavicza, Guido Falk von Rudorff, David O. De Haan +4
To unravel the structures of C12H12O7 isomers, identified as light-absorbing photooxidation products of syringol in atmospheric chamber experiments, we apply a graph-based molecule…
Dictionary of 140k GDB and ZINC derived AMONs
Bing Huang, O. Anatole von Lilienfeld
We present all {\bf A}mons for {\bf G}DB and {\bf Z}inc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based o…
Large yet bounded: Spin gap ranges in carbenes
Max Schwilk, Diana N. Tahchieva, O. Anatole von Lilienfeld
Despite its relevance for chemistry, the electronic structure of free carbenes throughout chemical space has not yet been studied in a systematic manner. We explore a large and sys…
Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: Separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase
Ardita Kilaj, Hong Gao, Diana Tahchieva +6
The Diels-Alder cycloaddition, in which a diene reacts with a dienophile to form a cyclic compound, counts among the most important tools in organic synthesis. Achieving a precise…
Atoms in molecules from alchemical perturbation density functional theory
Guido Falk von Rudorff, O. Anatole von Lilienfeld
Based on thermodynamic integration we introduce atoms in molecules (AIM) using the orbital-free framework of alchemical perturbation density functional theory (APDFT). Within APDFT…
Alchemical normal modes unify chemical space
Stijn Fias, K. Y. Samuel Chang, O. Anatole von Lilienfeld
In silico design of new molecules and materials with desirable quantum properties by high-throughput screening is a major challenge due to the high dimensionality of chemical space…
The fundamentals of quantum machine learning
Bing Huang, Nadine O. Symonds, O. Anatole von Lilienfeld
Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving a…
AlGaAs crystals with direct 2 eV band gaps from computational alchemy
K. Y. Samuel Chang, O. Anatole von Lilienfeld
We use alchemical first order derivatives for the rapid yet robust prediction of band structures. The power of the approach is demonstrated for the design challenge of finding Al$_…
Torsional potentials of glyoxal, oxalyl halides and their thiocarbonyl derivatives: Challenges for popular density functional approximations
D. Tahchieva, D. Bakowies, R. Ramakrishnan +1
The reliability of popular density functionals was studied for the description of torsional profiles of 36 molecules: glyoxal, oxalyl halides and their thiocarbonyl derivatives. HF…
Rapid yet accurate first principle based predictions of alkali halide crystal phases using alchemical perturbation
Alisa Solovyeva, O. Anatole von Lilienfeld
We assess the predictive power of alchemical perturbations for estimating fundamental properties in ionic crystals. Using density functional theory we have calculated formation ene…
Machine learning for many-body physics: efficient solution of dynamical mean-field theory
Louis-François Arsenault, O. Anatole von Lilienfeld, Andrew J. Millis
Machine learning methods for solving the equations of dynamical mean-field theory are developed. The method is demonstrated on the three dimensional Hubbard model. The key technica…
Crystal Structure Representations for Machine Learning Models of Formation Energies
Felix Faber, Alexander Lindmaa, O. Anatole von Lilienfeld +1
We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization e…