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Alberto Fabrizio

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Related publications (28)

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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

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

Tuning the structure, reactivity and magnetic communication of nitride-bridged uranium complexes with the ancillary ligands

Rosario Scopelliti, Anne-Clémence Corminboeuf, Marinella Mazzanti, Ivica Zivkovic, Alberto Fabrizio, Luciano Barluzzi

Molecular uranium nitride complexes were prepared to relate their small molecule reactivity to the nature of the U & xe001;N & xe001;U bonding imposed by the supporting ligand. The U4+-U4+ nitride complexes, [NBu4][{(((BuO)-Bu-t)(3)SiO)(3)U}(2)(mu-N)], [NB ...

2019

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

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

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

Exploring the Limitation of Molecular Water Oxidation Catalysts

Anne-Clémence Corminboeuf, Alberto Fabrizio, Michael Markus Busch

Linear free energy scaling relationships (LFESRs) and volcano plots are routinely used to assess the performance of heterogeneous electrocatalysts and have only recently been concretely exploited in homogeneous catalysis. These tools efficiently compare an ...

AMER CHEMICAL SOC2018

Transferable Machine-Learning Model of the Electron Density

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

The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework t ...

2019

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

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

Quantum Chemical Study of the Water Exchange Mechanism of the Americyl(VI) Aqua Ion

François Rotzinger, Alberto Fabrizio

The water exchange reaction of the americyl(VI) aqua ion was investigated with quantum chemical methods, density functional theory (DFT), and wave function theory (WFT). Associative and dissociative substitution mechanisms were studied, whereby DFT produce ...

American Chemical Society2016

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

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

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

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

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

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

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

2019

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

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

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

Quantum Chemistry Meets Machine Learning

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

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the ...

SWISS CHEMICAL SOC2019

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

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