34 citations · 46 across the 10 of their papers we have counts for
10 papers
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
Mikołaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
Teddy Koker, Abhijeet Gangan, Mit Kotak +2
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on ene…
TorchSim: An efficient atomistic simulation engine in PyTorch
Orion Cohen, Janosh Riebesell, Rhys Goodall +6
We introduce TorchSim, an open-source atomistic simulation engine tailored for the Machine Learned Interatomic Potential (MLIP) era. By rewriting core atomistic simulation primitiv…
Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality
Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar +4
Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham density functional theory calculations, as i…
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali +32
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientifi…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
Electronic structure prediction of medium and high entropy alloys across composition space
Shashank Pathrudkar, Stephanie Taylor, Abhishek Keripale +6
We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated allo…
Force field optimization by end-to-end differentiable atomistic simulation
Abhijeet S. Gangan, Samuel S. Schoenholz, Ekin Dogus Cubuk +2
The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for re…
Vibrational Spectra of Pb2Bi2Te3, PbBi2Te4 and PbBi4Te7 Topological Insulators: Temperature Dependent Raman and Theoretical Insight from DFT Simulations
Priyanath Mal, Ganesh Bera, G. R. Turpu +5
We present temperature dependent frequency shift and line broadening of phonon modes by insertion of atomic layers of Pb and PbTe in the prototype 3D topological insulator Bi2Te3,…