29 citations · 60 across the 9 of their papers we have counts for
10 papers
Inverse design of bespoke interatomic potentials via active learning by information-matching
Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…
Information Entropy is a General-Purpose Collective Variable for Enhanced Sampling
Xiangrui Li, Daniel Schwalbe-Koda
Enhanced sampling methods typically require predefined collective variables (CVs) that presuppose knowledge of reaction coordinates, restricting the discovery of unanticipated tran…
Generative Inversion of Spectroscopic Data for Amorphous Structure Elucidation
Jiawei Guo, Daniel Schwalbe-Koda
Determining atomistic structures from characterization data is one of the most common yet intricate problems in materials science. Particularly in amorphous materials, proposing st…
Automatic Identification of Compounds in Molecular Mixtures from Liquid-Phase Infrared Spectra
Yannah J. U. Melle, Thanh Nguyen, Jeffrey Lopez +1
Interpreting spectroscopy data is a critical bottleneck in automating chemical research and industrial characterization. Particularly within infrared (IR) spectroscopy, identifying…
Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
Benjamin Yu, Vincenzo Lordi, Daniel Schwalbe-Koda
Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the…
A Generative Diffusion Model for Amorphous Materials
Kai Yang, Daniel Schwalbe-Koda
Generative models show great promise for the inverse design of molecules and inorganic crystals, but remain largely ineffective within more complex structures such as amorphous mat…
MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
Jingru Gan, Peichen Zhong, Yuanqi Du +7
Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Langua…
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh +2
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification…
Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances
Daniel Schwalbe-Koda, Daniel E. Widdowson, Tuan Anh Pham +1
Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic…
mkite: A distributed computing platform for high-throughput materials simulations
Daniel Schwalbe-Koda
Advances in high-throughput simulation (HTS) software enabled computational databases and big data to become common resources in materials science. However, while computational pow…