activity
20222026
most citedSimilarity of materials and data-quality assessment by fingerprinting

16 citations · 20 across the 5 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci2026

FUCrIMODo: structure recovery from atomistic descriptors via multi-stage genetic algorithms

Louis Boehm, Martin Kuban, Claudia Draxl

Data-driven approaches to materials discovery rely on numerical representations of atomic structures as input for machine learning models. Inverting these descriptors - recovering…

cond-mat.mtrl-sci2026

An exciting approach to theoretical spectroscopy

Martí Raya-Moreno, Alexander Buccheri, Noah Alexy Dasch +27

Theoretical spectroscopy, and more generally, electronic-structure theory, are powerful concepts for describing the complex many-body interactions in materials. They comprise a var…

stat.ML2024

How big is Big Data?

Daniel T. Speckhard, Tim Bechtel, Luca M. Ghiringhelli +3

Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {\it big} means in the context of typical materials-science machi…

cond-mat.mtrl-sci2024★ 3 cited

MADAS -- A Python framework for assessing similarity in materials-science data

Martin Kuban, Santiago Rigamonti, Claudia Draxl

Computational materials science produces large quantities of data, both in terms of high-throughput calculations and individual studies. Extracting knowledge from this large and he…

cond-mat.mtrl-sci2023★ 1 cited

CELL: a Python package for cluster expansion with a focus on complex alloys

Santiago Rigamonti, Maria Troppenz, Martin Kuban +2

We present the Python package CELL, which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-,…

cond-mat.mtrl-sci2022★ 16 cited

Similarity of materials and data-quality assessment by fingerprinting

Martin Kuban, Šimon Gabaj, Wahib Aggoune +3

Identifying similar materials, i.e., those sharing a certain property or feature, requires interoperable data of high quality. It also requires means to measure similarity. We demo…

cond-mat.mtrl-sci2022

Density-of-states similarity descriptor for unsupervised learning from materials data

Martin Kuban, Santiago Rigamonti, Markus Scheidgen +1

We develop a materials descriptor based on the electronic density of states and investigate the similarity of materials based on it. As an application example, we study the Computa…