Generative Models for Automatic Chemical Design. - Daniel Schwalbe-Koda; Rafael Gómez-Bombarelli (CoRR, 2019)

Adversarial Attacks on Uncertainty Enable Active Learning for Neural Network Potentials. - Daniel Schwalbe-Koda; Aik Rui Tan; Rafael Gómez-Bombarelli (CoRR, 2021)

mkite: A distributed computing platform for high-throughput materials simulations. - Daniel Schwalbe-Koda (CoRR, 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)

Data efficiency and extrapolation trends in neural network interatomic potentials. - Joshua A. Vita; Daniel Schwalbe-Koda (Mach. Learn. Sci. Technol., 2023)

Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics. - Daniel Schwalbe-Koda; Sébastien Hamel; Babak Sadigh; Fei Zhou 002; Vincenzo Lordi (CoRR, 2024)

Large Language Models Are Innate Crystal Structure Generators. - Jingru Gan; Peichen Zhong; Yuanqi Du; Yanqiao Zhu 001; Chenru Duan; Haorui Wang; Carla P. Gomes; Kristin A. Persson; Daniel Schwalbe-Koda; Wei Wang 010 (CoRR, 2025)

A Generative Diffusion Model for Amorphous Materials. - Kai Yang; Daniel Schwalbe-Koda (CoRR, 2025)