35 citations · 74 across the 8 of their papers we have counts for
8 papers
Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications
Matteo Cobelli, Stefano Sanvito
Autoresearch offers a flexible paradigm for automating scientific tasks, in which an AI agent proposes, implements, evaluates, and refines candidate solutions against a quantitativ…
Sampling Latent Material-Property Information From LLM-Derived Embedding Representations
Luke P. J. Gilligan, Matteo Cobelli, Hasan M. Sayeed +2
Vector embeddings derived from large language models (LLMs) show promise in capturing latent information from the literature. Interestingly, these can be integrated into material e…
Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams
Hugo Rossignol, Michail Minotakis, Matteo Cobelli +1
In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of ab-initio calculations covering a wide range of compositions and str…
Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys
Michael Minotakis, Hugo Rossignol, Matteo Cobelli +1
The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired a…
A rule-free workflow for the automated generation of databases from scientific literature
Luke P. J. Gilligan, Matteo Cobelli, Valentin Taufour +1
In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-…
Cluster expansion constructed over Jacobi-Legendre polynomials for accurate force fields
Michelangelo Domina, Urvesh Patil, Matteo Cobelli +1
We introduce a compact cluster expansion method, constructed over Jacobi and Legendre polynomials, to generate highly accurate and flexible machine-learning force fields. The const…
A spectral-neighbour representation for vector fields: machine-learning potentials including spin
Michelangelo Domina, Matteo Cobelli, Stefano Sanvito
We introduce a translational and rotational invariant local representation for vector fields, which can be employed in the construction of machine-learning energy models of solids…
Inversion of the chemical environment representations
Matteo Cobelli, Paddy Cahalane, Stefano Sanvito
Machine-learning generative methods for material design are constructed by representing a given chemical structure, either a solid or a molecule, over appropriate atomic features,…