activity
20172023
most citedHigh pressure hydrogen by machine learning and quantum Monte Carlo

40 citations · 120 across the 8 of their papers we have counts for

collaborators

8 papers

cond-mat.str-el2023★ 14 cited

Principal deuterium Hugoniot via Quantum Monte Carlo and -learning

Giacomo Tenti, Kousuke Nakano, Andrea Tirelli +2

We present a study of the principal deuterium Hugoniot for pressures up to GPa, using Machine Learning potentials (MLPs) trained with Quantum Monte Carlo (QMC) energies, forc…

cond-mat.mtrl-sci2022★ 11 cited

Topological data analysis for revealing structural origin of density anomalies in silica glass

Andrea Tirelli, Kousuke Nakano

Topological data analysis (TDA) is a new emerging and powerful tool to understand the medium range structure ordering of multi-scale data. This study investigates the density anoma…

cond-mat.str-el2021★ 40 cited

High pressure hydrogen by machine learning and quantum Monte Carlo

Andrea Tirelli, Giacomo Tenti, Kousuke Nakano +1

We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a Machine Learning Potential (MLP). We use…

cond-mat.stat-mech2021★ 15 cited

Unsupervised machine learning approaches to the -state Potts model

Andrea Tirelli, Danyella O. Carvalho, Lucas A. Oliveira +3

In this paper with study phase transitions of the -state Potts model, through a number of unsupervised machine learning techniques, namely Principal Component Analysis (PCA), $k…

cond-mat.mtrl-sci2021★ 15 cited

Application of canonical augmentation to the atomic substitution problem

Genki I. Prayogo, Andrea Tirelli, Keishu Utimula +3

A common approach for studying a solid solution or disordered system within a periodic ab-initio framework is to create a supercell in which a certain amount of target elements is…

cond-mat.str-el2021★ 21 cited

Learning quantum phase transitions through Topological Data Analysis

Andrea Tirelli, Natanael C. Costa

We implement a computational pipeline based on a recent machine learning technique, namely the Topological Data Analysis (TDA), that has the capability of extracting powerful infor…

math.AG2018★ 2 cited

Symplectic resolutions for multiplicative quiver varieties and character varieties for punctured surfaces

Travis Schedler, Andrea Tirelli

We study the algebraic symplectic geometry of multiplicative quiver varieties, which are moduli spaces of representations of certain quiver algebras, introduced by Crawley-Boevey a…

math.AG2017★ 2 cited

Symplectic resolutions for Higgs moduli spaces

Andrea Tirelli

In this paper, we study the algebraic symplectic geometry of the singular moduli spaces of Higgs bundles of degree and rank on a compact Riemann surface of genus . I…