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Efficient implementation of atom-density representations
Publications associées (37)
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Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...
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Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...
EPFL2024Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches
Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...
EPFL2023A recipe for cracking the quantum scaling limit with machine learned electron densities
A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...
IOP Publishing Ltd2023Assessing the persistence of chalcogen bonds in solution with neural network potentials
Rubén Laplaza Solanas, Frédéric Célerse, Veronika Juraskova
Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environme ...
2022Physics-enhanced machine learning with symmetry-adapted and long-range representations
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 ...
EPFL2021A general and efficient framework for atomistic 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 ...
EPFL2021Learning (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 ...
2020Deterministic and Statistical Approaches to Quantum Chemistry
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 ...
EPFL2020The ONETEP linear-scaling density functional theory program
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