9 citations · 13 across the 5 of their papers we have counts for
5 papers
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
Christoph Brunken, Titouan Cormier, Lucien Walewski +15
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is…
Bayesian perspectives for quantum states and application to ab initio quantum chemistry
Yannic Rath, Massimo Bortone, George H. Booth
The quantum many-electron problem is not just at the heart of condensed matter phenomena, but also essential for first-principles simulation of chemical phenomena. Strong correlati…
Simple Fermionic backflow states via a systematically improvable tensor decomposition
Massimo Bortone, Yannic Rath, George H. Booth
We present an effective ansatz for the wave function of correlated electrons that brings closer the fields of machine learning parameterizations and tensor rank decompositions. We…
Impact of conditional modelling for a universal autoregressive quantum state
Massimo Bortone, Yannic Rath, George H. Booth
We present a generalized framework to adapt universal quantum state approximators, enabling them to satisfy rigorous normalization and autoregressive properties. We also introduce…
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
Martino Sorbaro, Qian Liu, Massimo Bortone +1
In the last few years, spiking neural networks have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a…