Publications by Alberto Fabrizio

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Alberto Fabrizio

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

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

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

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

Deterministic and Statistical Approaches to Quantum Chemistry

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

Exploring the Limitation of Molecular Water Oxidation Catalysts

Transferable Machine-Learning Model of the Electron Density

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

Physics-based representations for machine learning properties of chemical reactions

Data-powered augmented volcano plots for homogeneous catalysis

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

SPAHM: the spectrum of approximated Hamiltonian matrices representations

Learning the Exciton Properties of Azo-dyes

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

Quantum Chemistry Meets Machine Learning

Data Mining the C-C Cross-Coupling Genome

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

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

Electron density learning of non-covalent systems