1.1k citations · 8.7k across the 150 of their papers we have counts for
150 papers
LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
Siddharth Betala, Samuel P. Gleason, Ali Ramlaoui +12
Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented explorat…
MiAD: Mirage Atom Diffusion for De Novo Crystal Generation
Andrey Okhotin, Maksim Nakhodnov, Nikita Kazeev +3
In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However,…
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
Wei Nong, Ruiming Zhu, Zekun Ren +7
Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However,…
Inverse Design of Broadband Antennas for Terahertz Devices Based on 2D Materials
M. Lukianov, A. Maevskiy, N. Kazeev +5
Terahertz (THz) technology, a cornerstone of next-generation high-speed communication and sensing, has long been hindered by impedance mismatch challenges that limit device perform…
The LHCb ultra-fast simulation option, Lamarr: design and validation
Lucio Anderlini, Matteo Barbetti, Simone Capelli +9
Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CE…
Robust Neural Particle Identification Models
Aziz Temirkhanov, Artem Ryzhikov, Denis Derkach +3
The volume of data processed by the Large Hadron Collider experiments demands sophisticated selection rules typically based on machine learning algorithms. One of the shortcomings…