activity
20232026
most citedA foundation model for atomistic materials chemistry

249 citations · 291 across the 9 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2026

Machine-learning octet -type binary compounds across chemical space with domain knowledge of the interatomic bond

Rohan Kumar, Mariano D. Forti, Aakash A. Naik +2

The prediction of the structural stability of octet -type binary compounds is a classical materials informatics problem. The challenge is to capture the relative stability of 4…

cond-mat.mtrl-sci2026★ 1 cited

A critical assessment of bonding descriptors for predicting materials properties

Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen +4

Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuabl…

cond-mat.mtrl-sci2025★ 1 cited

Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study

Joana Bustamante, Anupama Ghata, Aakash A. Naik +4

Argyrodite-type Ag-based sulfides combine exceptionally low lattice thermal and high ionic conductivity, making them promising candidates for thermoelectric and solid-state energy…

cs.LG2025★ 2 cited

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…

physics.comp-ph2024

An automated framework for exploring and learning potential-energy surfaces

Yuanbin Liu, Joe D. Morrow, Christina Ertural +6

Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing mach…

cs.LG2024★ 5 cited

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…

physics.chem-ph2024★ 249 cited

A foundation model for atomistic materials chemistry

Ilyes Batatia, Philipp Benner, Yuan Chiang +85

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…

cond-mat.mtrl-sci2023★ 33 cited

A Quantum-Chemical Bonding Database for Solid-State Materials

Aakash Ashok Naik, Christina Ertural, Nidal Dhamrait +2

An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important…

cond-mat.mtrl-sci2023

"Ultima Ratio": Simulating wide-range X-ray scattering and diffraction

Brian R. Pauw, Sofya Laskina, Aakash Naik +4

We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for…