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Found 6 papers, 5 papers with code
UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion
We validate the effectiveness of UniGenX on material and small molecule generation tasks, achieving a significant leap in state-of-the-art performance for material crystal structure prediction and establishing new state-of-the-art results for small molecule structure prediction, de novo design, and conditional generation.
MatterGen: a generative model for inorganic materials design
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
DADApy is a python software package for analysing and characterising high-dimensional data manifolds.
Ranking the information content of distance measures
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
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
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
