Martin Schwade

TheoFEM-TUM/Hamster.jl: v0.3.1

Martin Schwade, K Chen, actions-user, frevon

What's Changed fixed issue that parameter order was not respected when copying from train to val model by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/96 Added onsite correction using Ewald summation by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/97 Fixed problems in soc and ewald model when no orbitals are …

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TheoFEM-TUM/Hamster.jl: v0.3.1

Martin Schwade, Kaiwen Chen, actions-user, frevon

What's Changed fixed issue that parameter order was not respected when copying from train to val model by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/96 Added onsite correction using Ewald summation by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/97 Fixed problems in soc and ewald model when no orbitals are …

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TheoFEM-TUM/Hamster.jl: v0.3.0

Martin Schwade, Kaiwen Chen, actions-user

What's Changed added onsite orthogonality and bug fixes by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/94 added functionality to write orbital basis to ham.h5 file by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/95 Full Changelog: https://github.com/TheoFEM-TUM/Hamster.jl/compare/v0.2.4...v0.3.0

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Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction

Martin Schwade, Shaoming Zhang, Frederik Vonhoff, Frederico P. Delgado et autres

Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. …

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TheoFEM-TUM/Hamster.jl: v0.2.4

Martin Schwade, kaiwenchen2003, actions-user

What's Changed Added functionality to automatically print config tags and their values to hamster.out file by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/89 fixed output file being written on all mpi ranks by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/90 fixed writing to output file by @mschwade-code in https://github.com/TheoFEM-TUM/Hamster.jl/pull/91 fixed …

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Hamster.jl-v0.2.1

Martin Schwade, Shaoming Zhang, Frederik Vonhoff, Frederico P. Delgado et autres

This release of Hamster.jl (v0.2.1-zenodo) was generated specifically for Zenodo archiving to provide a permanent DOI for citation. It contains the same functionality as the corresponding GitHub release. Users can reference this version via the Zenodo DOI for reproducibility in publications.

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Data for Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction

Martin Schwade, Shaoming Zhang, Frederik Vonhoff, David A. Egger

Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. …

Temperature-transferable tight-binding model using a hybrid-orbital basis

Martin Schwade, Maximilian J. Schilcher, Christian Reverón Baecker, Manuel Grumet et autres

Finite-temperature calculations are relevant for rationalizing material properties, yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many explicit first-principles calculations, tight-binding and machine-learning models for the electronic structure emerged …

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Temperature-transferable tight-binding model using a hybrid-orbital basis

Martin Schwade, Maximilian J. Schilcher, Christian Reverón Baecker, Manuel Grumet et autres

Finite-temperature calculations are relevant for rationalizing material properties yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many explicit first-principles calculations, tight-binding and machine-learning models for the electronic structure emerged …

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Dynamical aspects of supercooled TIP3P–water in the grooves of DNA

M. A. F. dos Santos, Marco A. Habitzreuter, Martin Schwade, R. Borrasca et autres

We investigate by molecular dynamics simulations the mobility of the water located at the DNA minor and major grooves. We employ the TIP3P water model, and our system is analyzed for a range of temperatures 190-300 K. For high temperatures, the water …

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