9 papers
RamanGPT: Bidirectional Mapping Between Crystal Structures and Raman Spectra with Graph Neural Networks and Generative Transformers
Frank M. Abel, Jaehyung Lee, Charles R. Campbell +1
Raman spectroscopy is one of the most accessible vibrational probes in materials laboratories, but its forward problem (structure to spectrum) is bottlenecked by the cost of densit…
AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D
Vsevolod Biryukov, Kamal Choudhary, Timur Bazhirov
Artificial intelligence (AI) and machine learning (ML) models in materials science are predominantly trained on ideal bulk crystals, limiting their transferability to real-world ap…
From Photons to Electrons: Accelerated Materials Discovery via Random Libraries and Automated Scanning Transmission Electron Microscopy
Boris Slautin, Kamyar Barakati, Utkarsh Pratiush +10
The real-world implementation of materials prediction algorithms remains limited by persistent characterization bottlenecks in materials discovery, where photon-based probe techniq…
CHIPS-TB: Evaluating Tight-Binding Models For Metals, Semiconductors, and Insulators
In Jun Park, Kamal Choudhary
As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method…
DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
Kamal Choudhary
Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This…
Lean CNNs for mapping electron charge density fields to material properties
Pranoy Ray, Kamal Choudhury, Surya R. Kalidindi
This work introduces a lean CNN (convolutional neural network) framework, with a drastically reduced number of fittable parameters (<81K) compared to the benchmarks in current lite…