Publications by Ksenia Briling

Related publications (10)

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

Anne-Clémence Corminboeuf, Alberto Fabrizio, Andrea Grisafi, Ksenia Briling

Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density ...

2020

Learning the Exciton Properties of Azo-dyes

Anne-Clémence Corminboeuf, Alberto Fabrizio, Sergio Vela Llausi, Ksenia Briling

The ab initio determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine l ...

AMER CHEMICAL SOC2021

Impact of quantum-chemical metrics on the machine learning prediction of electron density

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling

Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics ...

AMER INST PHYSICS2021

Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling, David Daniel Girardier

The on-top pair density is a local quantum-chemical property that reflects the probability of two electrons of any spin to occupy the same position in space. Being the simplest quantity related to the two-particle density matrix, the on-top pair density is ...

AMER INST PHYSICS2020

SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling, Yannick Calvino Alonso

Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPAHM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent ...

2024

SPAHM: the spectrum of approximated Hamiltonian matrices representations

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling

Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all r ...

2022

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

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 ...

EPFL2024

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Jan Weinreich, Matthieu Nicolas Haeberle

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

IOP Publishing Ltd2025

3DReact: Geometric Deep Learning for Chemical Reactions

Vignesh Ram Somnath, Andreas Krause, Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Charlotte Cäcilie Gertrud Bunne

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we int ...

2024

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

Anne-Clémence Corminboeuf, Puck Elisabeth van Gerwen, Ksenia Briling, Yannick Calvino Alonso

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based ...

Royal Soc Chemistry2024