Publications de Alberto Fabrizio

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

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

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

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

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

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

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

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

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, Ksenia Briling, Alberto Fabrizio

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, Ksenia Briling, Alberto Fabrizio, Sergio Vela Llausi

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

Matthew Wodrich, Anne-Clémence Corminboeuf, 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