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

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SPAHM: the spectrum of approximated Hamiltonian matrices representations

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

A general and efficient framework for atomistic machine learning

Félix Benedito Clément Musil

Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

EPFL2021

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

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

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Jinzhi Lu

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire ...

2021

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The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical ...

2021

Efficient implementation of atom-density representations

Michele Ceriotti, Till Junge, Guillaume André Jean Fraux, Félix Benedito Clément Musil, Michael John Willatt, Max David Veit, Alexander Jan Goscinski

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materi ...

2021

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in ...

2021

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

Ksenia Briling, Alberto Fabrizio, Andrea Grisafi

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

We present an overview of the onetep program for linear-scaling density functional theory (DFT) calculations with large basis set (plane-wave) accuracy on parallel computers. The DFT energy is computed from the density matrix, which is constructed from spa ...

AMER INST PHYSICS2020