Francesco Montalenti

Professor

University of Milano-Bicocca

Italy

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Francesco Montalenti is a Professor at Universita degli Studi di Milano-Bicocca, Italy. His research focuses on deep learning methodologies applied to the simulation and analysis of condensed matter systems, particularly in the context of materials science. Recent articles highlight his work on topics such as neural networks for predicting microstructure evolution, the impact of phase transitions in nanoindentation, and the modeling of strained-film growth.

Articles (17)

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2025

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2020

Collaborators (7)

Luciana Di Gaspare

Associate Professor

Università degli Studi di Roma La Sapienza

ITALY

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Leo Miglio

University of Milano-Bicocca

ITALY

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Marcel Verheijen

NETHERLANDS

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Emilio Scalise

Assistant Professor

University of Milano-Bicocca

ITALY

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Roberto Bergamaschini

Assistant Professor

University of Milano-Bicocca

ITALY

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Stefano Sanguinetti

Full Professor

University of Milano-Bicocca

ITALY

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Anna Marzegalli

University of Milano-Bicocca

ITALY

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Francesco Montalenti

Research Interests

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About

Articles (17)

Deep Learning for simulating the evolution of condensed matter systems at the continuum scale: methods and applications

Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film

Silicon phase transitions in nanoindentation: Advanced molecular dynamics simulations with machine learning phase recognition

Accelerating simulations of strained-film growth by deep learning: Finite element method accuracy over long time scales

Polytypic quantum wells in Si and Ge: impact of 2D hexagonal inclusions on electronic band structure

Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired neural networks for the 3D microstructure evolution of materials via Cahn–Hilliard flow

In‐Plane Nanowire Growth of Topological Crystalline Insulator Pb1 − xSnxTe

Accurate generation of stochastic dynamics based on multi-model generative adversarial networks

Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty

Phase-field modeling of the morphological evolution of ringlike structures during growth: Thermodynamics, kinetics, and template effects

Nature and Shape of Stacking Faults in 3C‐SiC by Molecular Dynamics Simulations

Reentrant Behavior of the Density vs. Temperature of Indium Islands on GaAs(111)A

Self-Assembly of Nanovoids in Si Microcrystals Epitaxially Grown on Deeply Patterned Substrates

Kinetic Control of Morphology and Composition in Ge/GeSn Core/Shell Nanowires

Controlling the relaxation mechanism of low strain Si<sub>1- x </sub>Ge<sub>x </sub>/Si(001) layers and reducing the threading dislocation density by providing a preexisting dislocation source

Computational Analysis of Low-Energy Dislocation Configurations in Graded Layers

Molecular dynamics simulations of extended defects and their evolution in 3C–SiC by different potentials

Collaborators (7)

Luciana Di Gaspare

Leo Miglio

Marcel Verheijen

Emilio Scalise

Roberto Bergamaschini

Stefano Sanguinetti

Anna Marzegalli

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