6 papers
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +3
This report extends our previous work (Part 1), which introduced an energy-based model for learning and decision-making under uncertainty. The model leverages stochastic Langevin d…
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +2
Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertaint…
Contrastive Regularization of Machine Learning Potentials
Dimitrios Tzivrailis, Georgios Sotiropoulos, Alberto Rosso +1
Machine learning interatomic potentials are trained to predict energies and forces but built to be sampled: their purpose is to drive molecular simulations whose observables averag…
Thermodynamic properties of chemically disordered compounds via AI-driven estimation of partition function with the PULSE method
Baptiste Bernard, Luca Messina, Eiji Kawasaki +1
In this article, we present an improved version of the PULSE method (Partition function Unsupervised Learning Sampling and Evaluation) for estimating the thermodynamic properties o…
Uncertainty in AI-driven Monte Carlo simulations
Dimitrios Tzivrailis, Alberto Rosso, Eiji Kawasaki
In the study of complex systems, evaluating physical observables often requires sampling representative configurations via Monte Carlo techniques. These methods rely on repeated ev…
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders
Maciej J. Karcz, Luca Messina, Eiji Kawasaki +1
Computing atomic-scale properties of chemically disordered materials requires an efficient exploration of their vast configuration space. Traditional approaches such as Monte Carlo…