MatterGen: a generative model for inorganic materials design

no code implementations • 6 Dec 2023 • Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Ryota Tomioka, Tian Xie

We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset.

DADApy: Distance-based Analysis of DAta-manifolds in Python

1 code implementation • 4 May 2022 • Aldo Glielmo, Iuri Macocco, Diego Doimo, Matteo Carli, Claudio Zeni, Romina Wild, Maria d'Errico, Alex Rodriguez, Alessandro Laio

DADApy is a python software package for analysing and characterising high-dimensional data manifolds.

Clustering

Ranking the information content of distance measures

no code implementations • 30 Apr 2021 • Aldo Glielmo, Claudio Zeni, Bingqing Cheng, Gabor Csanyi, Alessandro Laio

Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure.

Building machine learning force fields for nanoclusters

2 code implementations • 5 Feb 2018 • Claudio Zeni, Kevin Rossi, Aldo Glielmo, Ádám Fekete, Nicola Gaston, Francesca Baletto, Alessandro De Vita

We assess Gaussian process (GP) regression as a technique to model interatomic forces in metal nanoclusters by analysing the performance of 2-body, 3-body and many-body kernel functions on a set of 19-atom Ni cluster structures.

Computational Physics

Efficient nonparametric $n$-body force fields from machine learning

2 code implementations • 15 Jan 2018 • Aldo Glielmo, Claudio Zeni, Alessandro De Vita

We provide a definition and explicit expressions for $n$-body Gaussian Process (GP) kernels which can learn any interatomic interaction occurring in a physical system, up to $n$-body contributions, for any value of $n$.

Computational Physics