Daniel Schwalbe-Koda

According to our database, Daniel Schwalbe-Koda authored at least 11 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Generative Inversion of Spectroscopic Data for Amorphous Structure Elucidation.

Jiawei Guo, Daniel Schwalbe-Koda

CoRR, March, 2026

2025

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory.

Benjamin Yu, Vincenzo Lordi, Daniel Schwalbe-Koda

CoRR, November, 2025

A comprehensive mapping of zeolite-template chemical space.

Mingrou Xie, Daniel Schwalbe-Koda, Yolanda Marcela Semanate-Esquivel, Estefanía Bello-Jurado, Alexander Hoffman, Omar Santiago-Reyes, Cecilia Paris, Manuel Moliner, Rafael Gómez-Bombarelli

Nat. Comput. Sci., August, 2025

A Generative Diffusion Model for Amorphous Materials.

Kai Yang, Daniel Schwalbe-Koda

CoRR, July, 2025

Large Language Models Are Innate Crystal Structure Generators.

Jingru Gan, Peichen Zhong, Yuanqi Du, Yanqiao Zhu, Chenru Duan, Haorui Wang, Carla P. Gomes, Kristin A. Persson, Daniel Schwalbe-Koda, Wei Wang

CoRR, February, 2025

2024

Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics.

Daniel Schwalbe-Koda, Sébastien Hamel, Babak Sadigh, Fei Zhou, Vincenzo Lordi

CoRR, 2024

2023

Data efficiency and extrapolation trends in neural network interatomic potentials.

Joshua A. Vita, Daniel Schwalbe-Koda

Mach. Learn. Sci. Technol., September, 2023

Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances.

Daniel Schwalbe-Koda, Daniel E. Widdowson, Tuan Anh Pham, Vitaliy A. Kurlin

CoRR, 2023

mkite: A distributed computing platform for high-throughput materials simulations.

Daniel Schwalbe-Koda

CoRR, 2023

2021

Adversarial Attacks on Uncertainty Enable Active Learning for Neural Network Potentials.

Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli

CoRR, 2021

2019

Generative Models for Automatic Chemical Design.

Daniel Schwalbe-Koda, Rafael Gómez-Bombarelli

CoRR, 2019