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Convergence analysis of Anderson‐type acceleration of Richardson's iteration

Numerical linear algebra with applications, 2019-08, Vol.26 (4), p.n/a

Lupo Pasini, Massimiliano

2019

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Deep learning workflow for the inverse design of molecules with specific optoelectronic properties

Scientific reports, 2023-11, Vol.13 (1), p.20031-13, Article 20031

2023

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A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Journal of phase equilibria and diffusion, 2022-12, Vol.43 (6), p.916-930

2022

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Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Journal of cheminformatics, 2022-10, Vol.14 (1), p.70-10, Article 70

2022

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Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

Machine learning: science and technology, 2022-06, Vol.3 (2), p.25007

2022

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Scalable balanced training of conditional generative adversarial neural networks on image data

The Journal of supercomputing, 2021-11, Vol.77 (11), p.13358-13384

2021

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First-principles data for solid solution niobium-tantalum-vanadium alloys with body-centered-cubic structures

Scientific data, 2024-08, Vol.11 (1), p.907-16

2024

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Analysis of Monte Carlo accelerated iterative methods for sparse linear systems

Numerical linear algebra with applications, 2017-05, Vol.24 (3), p.np-n/a

2017

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Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data

Journal of scientific computing, 2023-01, Vol.94 (2), p.36, Article 36

2023

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Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Computational materials science, 2023-05, Vol.224, p.112141, Article 112141

2023

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Stable parallel training of Wasserstein conditional generative adversarial neural networks

The Journal of supercomputing, 2023-02, Vol.79 (2), p.1856-1876

2023

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Anderson acceleration with approximate calculations: Applications to scientific computing

Numerical linear algebra with applications, 2024-10, Vol.31 (5), p.n/a

2024

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Invariant Features for Accurate Predictions of Quantum Chemical UV-vis Spectra of Organic Molecules

SoutheastCon 2024, 2024, p.311-320

2024

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A scalable algorithm for the optimization of neural network architectures

Parallel computing, 2021-07, Vol.104-105, p.102788, Article 102788

2021

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A parallel strategy for density functional theory computations on accelerated nodes

Parallel computing, 2020-12, Vol.100, p.102703, Article 102703

2020

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Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules

Scientific data, 2023-08, Vol.10 (1), p.546-14, Article 546

2023

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Transferring predictions of formation energy across lattices of increasing size

Machine learning: science and technology, 2024-06, Vol.5 (2), p.25015

2024

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Hierarchical model reduction driven by a proper orthogonal decomposition for parametrized advection-diffusion-reaction problems

Electronic transactions on numerical analysis, 2022-01, Vol.55 (1), p.187-212

2022

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Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials

Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, p.75-86

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Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.79-98

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Stable parallel training of Wasserstein conditional generative adversarial neural networks

The Journal of supercomputing, 2022-08, Vol.79 (2)

2022

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Convergence analysis of Anderson-type acceleration of Richardson's iteration

Numerical linear algebra with applications, 2019-04, Vol.26 (4)

Lupo Pasini, Massimiliano

2019

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Fast and stable deep-learning predictions of material properties for solid solution alloys

Journal of physics. Condensed matter, 2021-02, Vol.33 (8), p.084005-084005

2021

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A scalable algorithm for the optimization of neural network architectures

Parallel computing, 2021-04, Vol.104-105

2021

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Fast and stable deep-learning predictions of material properties for solid solution alloys This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do s

Journal of physics. Condensed matter, 2020-12, Vol.33 (8)

2020

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Convergence analysis of Anderson‐type acceleration of Richardson's iteration

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

Scalable balanced training of conditional generative adversarial neural networks on image data

First-principles data for solid solution niobium-tantalum-vanadium alloys with body-centered-cubic structures

Analysis of Monte Carlo accelerated iterative methods for sparse linear systems

Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Stable parallel training of Wasserstein conditional generative adversarial neural networks

Anderson acceleration with approximate calculations: Applications to scientific computing

Invariant Features for Accurate Predictions of Quantum Chemical UV-vis Spectra of Organic Molecules

A scalable algorithm for the optimization of neural network architectures

A parallel strategy for density functional theory computations on accelerated nodes

Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules

Transferring predictions of formation energy across lattices of increasing size

Hierarchical model reduction driven by a proper orthogonal decomposition for parametrized advection-diffusion-reaction problems

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

Stable parallel training of Wasserstein conditional generative adversarial neural networks

Convergence analysis of Anderson-type acceleration of Richardson's iteration

Fast and stable deep-learning predictions of material properties for solid solution alloys

A scalable algorithm for the optimization of neural network architectures

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