125 citations · 388 across the 14 of their papers we have counts for
15 papers
EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model
S M Mahbub Murshed, Hayden Bright, Rezaul Karim +1
Deploying language models (LMs) on the edge presents unique memory, latency, and power challenges absent in high-performance computing. KV-cache compression emerges as a promising…
Experiment and kp analysis of the luminescence from modulation-doped CdTe/(Cd,Mg)Te quantum wells at magnetic field
W. Solarska, M. Grymuza, M. Kubisa +8
In spite of a large quantity of papers devoted to the mangetoluminescence from CdTe/(Cd,Mg)Te, quantum wells there have been no attempts to analyze it on the basis of the band-stru…
Guided Multi-objective Generative AI to Enhance Structure-based Drug Design
Amit Kadan, Kevin Ryczko, Erika Lloyd +2
Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in deep learning, existing models cannot generate molecules that satisfy all desired p…
Accelerated Organic Crystal Structure Prediction with Genetic Algorithms and Machine Learning
Amit Kadan, Kevin Ryczko, Andrew Wildman +3
We present a high-throughput, end-to-end pipeline for organic crystal structure prediction (CSP) -- the problem of identifying the stable crystal structures that will form from a g…
Machine Learning Diffusion Monte Carlo Energies
Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total). The first use…
Electronic Response Quantities of Solids and Deep Learning
Kevin Ryczko, Olivier Malenfant-Thuot, Michel Côté +1
We introduce a deep neural network (DNN) framework called the \textbf{r}eal-space \textbf{a}tomic \textbf{d}ecomposition \textbf{net}work (\textsc{radnet}), which is capable of mak…
Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning
Kevin Ryczko, Sebastian J. Wetzel, Roger G. Melko +1
We use voxel deep neural networks to predict energy densities and functional derivatives of electron kinetic energies for the Thomas-Fermi model and Kohn-Sham density functional th…
Neural evolution structure generation: High Entropy Alloys
Conrard Giresse Tetsassi Feugmo, Kevin Ryczko, Abu Anand +2
We propose a method of neural evolution structures (NESs) combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs) struc…
Twin Neural Network Regression
Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko +1
We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The s…
Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution
Kevin Ryczko, Pierre Darancet, Isaac Tamblyn
We introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very large configuration s…
Deep Learning and Density Functional Theory
Kevin Ryczko, David Strubbe, Isaac Tamblyn
We show that deep neural networks can be integrated into, or fully replace, the Kohn-Sham density functional theory scheme for multi-electron systems in simple harmonic oscillator…
Extensive deep neural networks for transferring small scale learning to large scale systems
Kyle Mills, Kevin Ryczko, Iryna Luchak +3
We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily…
Convolutional neural networks for atomistic systems
Kevin Ryczko, Kyle Mills, Iryna Luchak +2
We introduce a new method, called CNNAS (convolutional neural networks for atomistic systems), for calculating the total energy of atomic systems which rivals the computational cos…
Structural characterizations of water-metal interfaces
Kevin Ryczko, Isaac Tamblyn
We analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including Pt, graphite, and graphene. Our results are built…
Hashkat: Large-scale simulations of online social networks
Kevin Ryczko, Adam Domurad, Nicholas Buhagiar +1
Hashkat (http://hashkat.org) is a free, open source, agent based simulation software package designed to simulate large-scale online social networks (e.g. Twitter, Facebook, Linked…