https://pypi.org/project/multisom/ Recent updates to the Python Package Index for multisom en https://pypi.org/project/multisom/0.1.5/ Multilayer algorithm of Self Organising Maps (also known as Kohonen Networks) implemented in Python for clustering of atomistic samples through unsupervised learning. The program allows the user to select wich per-atom quantities to use for training and application of the network, this quantities must be specified in the LAMMPS input file that is being analysed. tatoaquista@gmail.com Thu, 31 Jul 2025 12:26:02 GMT https://pypi.org/project/multisom/0.1.4/ Multilayer algorithm of Self Organising Maps (also known as Kohonen Networks) implemented in Python for clustering of atomistic samples through unsupervised learning. The program allows the user to select wich per-atom quantities to use for training and application of the network, this quantities must be specified in the LAMMPS input file that is being analysed. tatoaquista@gmail.com Wed, 30 Jul 2025 20:12:30 GMT https://pypi.org/project/multisom/0.1.3/ Multilayer algorithm of Self Organising Maps (also known as Kohonen Networks) implemented in Python for clustering of atomistic samples through unsupervised learning. The program allows the user to select wich per-atom quantities to use for training and application of the network, this quantities must be specified in the LAMMPS input file that is being analysed. tatoaquista@gmail.com Wed, 30 Jul 2025 20:09:10 GMT https://pypi.org/project/multisom/0.1.2/ Multilayer algorithm of Self Organising Maps (also known as Kohonen Networks) implemented in Python for clustering of atomistic samples through unsupervised learning. The program allows the user to select wich per-atom quantities to use for training and application of the network, this quantities must be specified in the LAMMPS input file that is being analysed. tatoaquista@gmail.com Wed, 30 Jul 2025 20:07:48 GMT https://pypi.org/project/multisom/0.1.1/ Multilayer algorithm of Self Organising Maps (also known as Kohonen Networks) implemented in Python for clustering of atomistic samples through unsupervised learning. The program allows the user to select wich per-atom quantities to use for training and application of the network, this quantities must be specified in the LAMMPS input file that is being analysed. tatoaquista@gmail.com Wed, 30 Jul 2025 20:00:37 GMT https://pypi.org/project/multisom/0.1.0/ Multilayer algorithm of Self Organising Maps (also known as Kohonen Networks) implemented in Python for clustering of atomistic samples through unsupervised learning. The program allows the user to select wich per-atom quantities to use for training and application of the network, this quantities must be specified in the LAMMPS input file that is being analysed. tatoaquista@gmail.com Wed, 30 Jul 2025 19:54:03 GMT https://pypi.org/project/multisom/0.0.0.8/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 20:38:27 GMT https://pypi.org/project/multisom/0.0.0.7/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 20:12:39 GMT https://pypi.org/project/multisom/0.0.0.6/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 20:08:23 GMT https://pypi.org/project/multisom/0.0.0.5/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 20:02:59 GMT https://pypi.org/project/multisom/0.0.0.4/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 19:58:08 GMT https://pypi.org/project/multisom/0.0.0.3/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 19:46:20 GMT https://pypi.org/project/multisom/0.0.0.2/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 19:36:59 GMT https://pypi.org/project/multisom/0.0.0.1/ Defect analysis and vacancy calculation for materials science tatoaquistapacce@gmail.com Fri, 04 Jul 2025 19:30:20 GMT