Publications de Alberto Fabrizio

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

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

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

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

Anne-Clémence Corminboeuf, Alberto Fabrizio, Puck Elisabeth van Gerwen, Sergio Vela Llausi, Simone Gallarati, Rubén Laplaza Solanas

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the fi ...

ROYAL SOC CHEMISTRY2022

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Anne-Clémence Corminboeuf, Alberto Fabrizio, Michele Ceriotti, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova, Edgar Albert Engel

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...

2022

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

Physics-based representations for machine learning properties of chemical reactions

Anne-Clémence Corminboeuf, Matthew Wodrich, Alberto Fabrizio, Puck Elisabeth van Gerwen

Physics-based representations constructed using only atomic positions and nuclear charges (also known as quantum machine learning, QML) allow for the reliable and efficient inference of molecular properties from training data. Chemistry is a science rooted ...

2022

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 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

Balancing Density Functional Theory Interaction Energies in Charged Dimers Precursors to Organic Semiconductors

Anne-Clémence Corminboeuf, Alberto Fabrizio, Riccardo Petraglia

The pursuit of an increasingly accurate description of intermolecular interactions within the framework of Kohn-Sham density functional theory (KS-DFT) has motivated the construction of numerous benchmark databases over the past two decades. By far, the la ...

AMER CHEMICAL SOC2020

Deterministic and Statistical Approaches to Quantum Chemistry

Alberto Fabrizio

The field of quantum chemistry has recently undergone a series of paradigm shifts, including a boom in machine learning applications that target the electronic structure problem. Along with these technological innovations, the community continues to identi ...

EPFL2020

Data-powered augmented volcano plots for homogeneous catalysis

Anne-Clémence Corminboeuf, Matthew Wodrich, Alberto Fabrizio, Benjamin André René Meyer

Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithm ...

2020

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

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Daniel Hollas

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high ab initio level. We offer a modular envir ...

AMER CHEMICAL SOC2020

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer

The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory. Related to the lack of derivative discontinuity in ...

AMER INST PHYSICS2020

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

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

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

Data Mining the C-C Cross-Coupling Genome

Anne-Clémence Corminboeuf, Matthew Wodrich, Alberto Fabrizio, Benjamin André René Meyer, Boodsarin Sawatlon

The speed and precision of machine-learning (ML) techniques in determining quantum chemical properties has resulted in a considerable computational speed up in comparison to traditional quantum chemical methods, and now allows a desired property of thousan ...

Wiley-VCH Verlag2019

The role of bridging ligands in dinitrogen reduction and functionalization by uranium multimetallic complexes

Kay Severin, Marinella Mazzanti, Ivica Zivkovic, Alberto Fabrizio, Farzaneh Fadaei Tirani, Julie Renée Michelle Andrez, Marta Falcone, Luciano Barluzzi

2019

Highly‐Substituted 1,2,3‐Triazolines: a New Class of Solid State Emitters with Electrofluorochromic Behavior

Andrzej Sienkiewicz, Kay Severin, Rosario Scopelliti, Anne-Sophie Chauvin, Albert Ruggi, Alberto Fabrizio, Farzaneh Fadaei Tirani, Abdusalom Suleymanov, Ophélie Marie Planes

2019

Synthesis of aminyl biradicals by base-induced Csp3–Csp3 coupling of cationic azo dyes

Andrzej Sienkiewicz, Kay Severin, Rosario Scopelliti, Euro Solari, Léonard Yannick Maurice Eymann, Alexander Tskhovrebov, Alberto Fabrizio, Farzaneh Fadaei Tirani, Yizhu Liu, Ophélie Marie Planes, Paul Varava

2019

Open-Shell Nonbenzenoid Nanographenes Containing Two Pairs of Pentagonal and Heptagonal Rings

Anne-Clémence Corminboeuf, Alberto Fabrizio, Daniele Passerone

Nonbenzenoid carbocyclic rings are postulated to serve as important structural elements toward tuning the chemical and electronic properties of extended polycyclic aromatic hydrocarbons (PAHs, or namely nanographenes), necessitating a rational and atomical ...

AMER CHEMICAL SOC2019

Electron density learning of non-covalent systems

Anne-Clémence Corminboeuf, Alberto Fabrizio, Michele Ceriotti, Andrea Grisafi, Benjamin André René Meyer

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origi ...

2019