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Spiking Neural Networks (SNNs) exhibit exceptional energy efficiency on neuromorphic hardware due to their sparse activation patterns. However, conventional training methods based on surrogate gradients and Backpropagation Through Time…
Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms. The classical SGD can be interpreted as a discretization of the stochastic gradient flow. In…
Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged…
A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data-driven. However, neural networks (NNs) often lead…
Machine learning (ML) techniques and atomistic modeling have rapidly transformed materials design and discovery. Specifically, generative models can swiftly propose promising materials for targeted applications. However, the predicted…
We propose using machine learning and artificial neural networks (ANNs) to enhance residual-based stabilization methods for advection-dominated differential problems. Specifically, in the context of the finite element method, we consider…
A framework previously introduced in [3] for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learning problems such as regression and classification. The…
A Laser-stimulated synthesis of large silver nanoaggregates consisting of hundreds to thousands of nanoparticles is investigated. It is shown that the morphology of the synthesized nanostructures can be controlled with light and monitored…
Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective.…
We develop a high-dimensional neural network potential (NNP) to describe the structural and energetic properties of borophene deposited on silver. This NNP has the accuracy of DFT calculations while achieving computational speedups of…
An iterative optimization algorithm with MD simulations in the loop is developed and applied to optimize Lennard-Jones (LJ) parameters specific for liquid tri-n-butyl phosphate (TBP). The optimization loop uses non-dominated sorting genetic…
Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical…
Silver nanoparticles (AgNP's) possess inherent biological potentials that have obliged an alternative, eco-friendly, sustainable approach to "Green Synthesis." In the present study, we synthesized Green Silver Nanoparticles (GAgNP's) using…
The search for new ultra strong materials has been a very active research area. With relation to metals, a successful way to improve their strength is by the creation of a gradient of nanograins (GNG) inside the material. Recently, R.…
Selective removal of heavy metals from water is a complex topic. We present a theoretical computational approach, supported by experimental evidences, to design a functionalized nanomaterial that is able to selectively capture metallic ions…
Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (ANNs) when deployed on edge devices such as neuromorphic…
Emerged as a biology-inspired method, Spiking Neural Networks (SNNs) mimic the spiking nature of brain neurons and have received lots of research attention. SNNs deal with binary spikes as their activation and therefore derive extreme…
Spiking Neural Networks (SNNs) have emerged as a promising alternative to traditional Deep Neural Networks for low-power computing. However, the effectiveness of SNNs is not solely determined by their performance but also by their energy…
Electron microscopy, while reliable, is an expensive, slow, and inefficient technique for thorough size distribution characterization of both mono- and polydisperse colloidal nanoparticles. If rapid in-situ characterization of colloid…
Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and…