2.4k citations · 8.1k across the 75 of their papers we have counts for
77 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Approximate label symmetries improve data efficiency
Scott Y. H. Kim, Mathis Lechaume-Robert, O. Anatole von Lilienfeld
Enforcing feature symmetries in machine learning (ML) models is a common strategy to mitigate data scarcity. Confirming expectations from statistical learning theory, we show that…
Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Morris Trestman, Stefan Gugler, Felix A. Faber +1
Training set sampling methods are used to improve model performance and lower data costs in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest P…
Antisymmetry rules of response properties in certain chemical spaces
Takafumi Shiraogawa, Simon León Krug, Masahiro Ehara +1
Understanding chemical compound space (CCS), a set of molecules and materials, is crucial for the rational discovery of molecules and materials. Concepts of symmetry have recently…
Alchemical insights into approximately quadratic energies of iso-electronic atoms
Simon León Krug, O. Anatole von Lilienfeld
Accurate quantum mechanics based predictions of property trends are so important for materials design and discovery that even inexpensive approximate methods are valuable. We use t…
High-Tc superconductor candidates proposed by machine learning
Siwoo Lee, Jason Hattrick-Simpers, Young-June Kim +1
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced com…
Combining Hammett constants for -machine learning and catalyst discovery
V. Diana Rakotonirina, Marco Bragato, Stefan Heinen +1
We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning and to lig…
Quantum mechanical dataset of 836k neutral closed shell molecules with upto 5 heavy atoms from CNOFSiPSClBr
Danish Khan, Anouar Benali, Scott Y. H. Kim +2
We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (\textit{p}-bloc…
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules
Vanni Doffini, O. Anatole von Lilienfeld, Michael A. Nash
We investigate trends in the data-error scaling laws of machine learning (ML) models trained on discrete combinatorial spaces that are prone-to-mutation, such as proteins or organi…
Adaptive atomic basis sets
Danish Khan, Maximilian L. Ach, O. Anatole von Lilienfeld
Atomic basis sets are widely employed within quantum mechanics based simulations of matter. We introduce a machine learning model that adapts the basis set to the local chemical en…
Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems
Alessandro Coretti, Sebastian Falkner, Jan Weinreich +2
The paper by Noé et al. [F. Noé, S. Olsson, J. Köhler and H. Wu, Science, 365:6457 (2019)] introduced the concept of Boltzmann Generators (BGs), a deep generative model that can pr…
Adaptive hybrid density functionals
Danish Khan, Alastair James Arthur Price, Maximilian L. Ach +1
Exact exchange contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging t…
The Alchemical Integral Transform revisited
Simon León Krug, O. Anatole von Lilienfeld
We recently introduced the Alchemical Integral Transform (AIT) enabling the prediction of energy differences, and guessed an Ansatz to parametrize space in some alchemica…
Transferability of atomic energies from alchemical decomposition
Michael J. Sahre, Guido Falk von Rudorff, Philipp Marquetand +1
We study alchemical atomic energy partitioning as a method to estimate atomisation energies from atomic contributions which are defined in physically rigorous and general ways thro…
Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
Stefan Heinen, Danish Khan, Guido Falk von Rudorff +7
For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Corre…
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
Siwoo Lee, Stefan Heinen, Danish Khan +1
We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed liter…
Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design
Konstantin Karandashev, Jan Weinreich, Stefan Heinen +4
Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast…
Impact of noise on inverse design: The case of NMR spectra matching
Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Despite its fundamental importance and widespread use for assessing reaction success in organic chemistry, deducing chemical structures from nuclear magnetic resonance (NMR) measur…
Alchemical diastereomers from antisymmetric alchemical perturbations
O. Anatole von Lilienfeld, Giorgio Domenichini
The energy difference between two iso-electronic systems can be accurately approximated by the alchemical first order Hellmann-Feynmann derivative for the averaged Hamiltonian. Thi…
Hammett-Inspired Product Baseline for Data-efficient -ML in Chemical Space
V. Diana Rakotonirina, Marco Bragato, Guido Falk von Rudorff +1
Data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe…
Towards self-driving laboratories: The central role of density functional theory in the AI age
Bing Huang, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Density functional theory (DFT) plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and computationa…
Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions
Ardita Kilaj, Silvan Käser, Jia Wang +7
Recent advances in experimental methodology enabled studies of the quantum-state and conformational dependence of chemical reactions under precisely controlled conditions in the ga…
Kernel based quantum machine learning at record rate : Many-body distribution functionals as compact representations
Danish Khan, Stefan Heinen, O. Anatole von Lilienfeld
The feature vector mapping used to represent chemical systems is a key factor governing the superior data-efficiency of kernel based quantum machine learning (QML) models applicabl…
Encrypted machine learning of molecular quantum properties
Jan Weinreich, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary with…