1.2k citations · 3k across the 25 of their papers we have counts for
25 papers
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner +7
The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery,…
Fair GANs through model rebalancing for extremely imbalanced class distributions
Anubhav Jain, Nasir Memon, Julian Togelius
Deep generative models require large amounts of training data. This often poses a problem as the collection of datasets can be expensive and difficult, in particular datasets that…
Zero-shot racially balanced dataset generation using an existing biased StyleGAN2
Anubhav Jain, Nasir Memon, Julian Togelius
Facial recognition systems have made significant strides thanks to data-heavy deep learning models, but these models rely on large privacy-sensitive datasets. Further, many of thes…
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3
Nicholas Walker, John Dagdelen, Kevin Cruse +7
Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood…
Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms
Matthew Horton, Jimmy-Xuan Shen, Jordan Burns +21
Crystal Toolkit is an open source tool for viewing, analyzing and transforming crystal structures, molecules and other common forms of materials science data in an interactive way.…
Structured information extraction from complex scientific text with fine-tuned large language models
Alexander Dunn, John Dagdelen, Nicholas Walker +5
Intelligently extracting and linking complex scientific information from unstructured text is a challenging endeavor particularly for those inexperienced with natural language proc…
A Dataless FaceSwap Detection Approach Using Synthetic Images
Anubhav Jain, Nasir Memon, Julian Togelius
Face swapping technology used to create "Deepfakes" has advanced significantly over the past few years and now enables us to create realistic facial manipulations. Current deep lea…
Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities
Kevin Cruse, Amalie Trewartha, Sanghoon Lee +6
Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particl…
Dictionary Attacks on Speaker Verification
Mirko Marras, Pawel Korus, Anubhav Jain +1
In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce…
Machine-learning rationalization and prediction of solid-state synthesis conditions
Haoyan Huo, Christopher J. Bartel, Tanjin He +5
There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materia…
Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
Comparison of the Tetrahedron Method to Smearing Methods for the Electronic Density of States
M. Y. Toriyama, A. M. Ganose, M. Dylla +7
The electronic density of states (DOS) highlights fundamental properties of materials that oftentimes dictate their properties, such as the band gap and Van Hove singularities. In…
Non-destructive Characterization of Anti-Reflective Coatings on PV Modules
Todd Karin, David Miller, Anubhav Jain
Anti-reflective coatings (ARCs) are used on the vast majority of solar photovoltaic (PV) modules to increase power production. However, ARC longevity can vary from less than 1 year…
When Band Convergence is Not Beneficial for Thermoelectrics
Junsoo Park, Max Dylla, Yi Xia +3
Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances…
Optimal Band Structure for Thermoelectrics with Realistic Scattering and Bands
Junsoo Park, Yi Xia, Vidvuds Ozoliņš +1
Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified ban…
Efficient calculation of carrier scattering rates from first principles
Alex M. Ganose, Junsoo Park, Alireza Faghaninia +3
The electronic transport behaviour of materials determines their suitability for technological applications. We develop an efficient method for calculating carrier scattering rates…
Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
Alexander Dunn, Qi Wang, Alex Ganose +2
We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk mat…
A Model for Thermodynamic Properties of Monoatomic Liquids
Drew Lilly, Anubhav Jain, Ravi Prasher
We present an analytical model for calculating the thermodynamic properties of monoatomic liquids using a rough potential energy surface (PES). The PES is transformed into an equiv…
High Thermoelectric Performance and Defect Energetics of Multi-pocketed Full-Heusler Compounds
Junsoo Park, Yi Xia, Alex Ganose +2
We report first-principles density-functional study of electron-phonon interactions and thermoelectric transport properties of full-Heusler compounds SrBiAu and SrSbAu.…
A critical examination of compound stability predictions from machine-learned formation energies
Christopher J. Bartel, Amalie Trewartha, Qi Wang +3
Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of…
Benchmark Dataset for Timetable Optimization of Bus Routes in the City of New Delhi
Anubhav Jain, Avdesh Kumar, Saumya Balodi +1
Public transport is one of the major forms of transportation in the world. This makes it vital to ensure that public transport is efficient. This research presents a novel real-tim…
A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
Qi Wang, Anubhav Jain
When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures p…
On Detecting GANs and Retouching based Synthetic Alterations
Anubhav Jain, Richa Singh, Mayank Vatsa
Digitally retouching images has become a popular trend, with people posting altered images on social media and even magazines posting flawless facial images of celebrities. Further…
Predicting the Volumes of Crystals
Iek-Heng Chu, Sayan Roychowdhury, Daehui Han +2
New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis…