Atom-density representations for machine learning

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The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

This thesis presents the first cavity quantum electrodynamics experiments performed with a degenerate gas of

^6

Li with strong atom-atom interactions. The first part of this manuscript describes the design and the building of the apparatus that has been e ...

The electron density is an important object in quantum chemistry that is crucial for many downstream tasks in drug design. Recent deep learning approaches predict the electron density around a molecule from atom types and atom positions. Most of these meth ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Over the last two decades, data-powered machine learning (ML) tools have profoundly transformed numerous scientific fields. In computational chemistry, machine learning applications have permitted faster predictions of chemical properties and provided powe ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

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 ...

Building on recent advances of the embedded cluster approach combined with multiconfigurational theory, this work investigates the electronic states in thorium-doped CaF2 crystals. Th:CaF2 is established as a promising material for solid-state nuclear cloc ...

Nitrous oxide (N2O) has gained much interest because of its physiological effects ("laughing gas") and its negative environmental impact ("greenhouse gas", "ozone-depleting substance"): It has a lifetime of more than 100 years in the atmosphere. Its persis ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

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 atomic configuration of phases and their interfaces is fundamental to materials design and engineering. Here, we unveil a transition metal oxide interface, whose formation is driven by energetic influences─epitaxial tensile strain versus oxygen octahed ...

Theoretical and computational approaches to the study of materials and molecules have, over the last few decades, progressed at an exponential rate. Yet, the possibility of producing numerical predictions that are on par with experimental measurements is t ...

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...

Electronic nearsightedness is one of the fundamental principles that governs the behavior of condensed matter and supports its description in terms of local entities such as chemical bonds. Locality also underlies the tremendous success of machine-learning ...

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 ...

Atom-density representations for machine learning

Chattez avec Graph Search

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Roadmap on Machine learning in electronic structure

Cavity Quantum Electrodynamics with strongly correlated fermions

Lagnet: Better Electron Density Prediction for Lcao-based Data and Drug-like Substances

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Kernel Methods and Similarity Learning Applied in Computational Chemistry

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

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

Embedded cluster approach for accurate electronic structure calculations of 229Th:CaF2

Synthetic chemistry with nitrous oxide and triazenes

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

SPAHM: the spectrum of approximated Hamiltonian matrices representations

Engineering Symmetry Breaking Interfaces by Nanoscale Structural-Energetics in Orthorhombic Perovskite Thin Films

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Optimal radial basis for density-based atomic representations

A phase transition in diffusion models reveals the hierarchical nature of data

Multi-scale approach for the prediction of atomic scale properties

A general and efficient framework for atomistic machine learning