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Found 4 papers, 1 papers with code
LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design
Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models.
On the Interplay of Subset Selection and Informed Graph Neural Networks
However, learning on large datasets is strongly limited by the availability of computational resources and can be infeasible in some scenarios.
Parameterized Neural Networks for Finance
We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specific data sample.
Localized Coulomb Descriptors for the Gaussian Approximation Potential
We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix.
