BoltzWann: A code for the evaluation of thermoelectric and electronic transport properties with a maximally-localized Wannier functions basis. - Giovanni Pizzi; Dmitri Volja; Boris Kozinsky; Marco Fornari; Nicola Marzari (Comput. Phys. Commun., 2014)

An updated version of BoltzWann: A code for the evaluation of thermoelectric and electronic transport properties with a maximally-localized Wannier functions basis. - Giovanni Pizzi; Dmitri Volja; Boris Kozinsky; Marco Fornari; Nicola Marzari (Comput. Phys. Commun., 2014)

AiiDA: Automated Interactive Infrastructure and Database for Computational Science. - Giovanni Pizzi; Andrea Cepellotti; Riccardo Sabatini; Nicola Marzari; Boris Kozinsky (CoRR, 2015)

Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems. - Jonathan P. Mailoa; Mordechai Kornbluth; Simon L. Batzner; Georgy Samsonidze; Stephen T. Lam; Chris Ablitt; Nicola Molinari; Boris Kozinsky (CoRR, 2019)

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems. - Jonathan P. Mailoa; Mordechai Kornbluth; Simon L. Batzner; Georgy Samsonidze; Stephen T. Lam; Jonathan Vandermause; Chris Ablitt; Nicola Molinari; Boris Kozinsky (Nat. Mach. Intell., 2019)

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance. - Sebastiaan P. Huber; Spyros Zoupanos; Martin Uhrin; Leopold Talirz; Leonid Kahle; Rico Häuselmann; Dominik Gresch; Tiziano Müller; Aliaksandr V. Yakutovich; Casper W. Andersen; Francisco F. Ramirez; Carl S. Adorf; et al. (CoRR, 2020)

Fast Bayesian Force Fields from Active Learning: Study of Inter-Dimensional Transformation of Stanene. - Yu Xie; Jonathan Vandermause; Lixin Sun; Andrea Cepellotti; Boris Kozinsky (CoRR, 2020)

Multitask machine learning of collective variables for enhanced sampling of rare events. - Lixin Sun; Jonathan Vandermause; Simon L. Batzner; Yu Xie; David Clark; Wei Chen; Boris Kozinsky (CoRR, 2020)

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. - Simon L. Batzner; Tess E. Smidt; Lixin Sun; Jonathan P. Mailoa; Mordechai Kornbluth; Nicola Molinari; Boris Kozinsky (CoRR, 2021)

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics. - Albert Musaelian; Simon L. Batzner; Anders Johansson; Lixin Sun; Cameron J. Owen; Mordechai Kornbluth; Boris Kozinsky (CoRR, 2022)

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials. - Ilyes Batatia; Simon L. Batzner; Dávid Péter Kovács; Albert Musaelian; Gregor N. C. Simm; Ralf Drautz; Christoph Ortner; Boris Kozinsky; Gábor Csányi (CoRR, 2022)

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials. - Albert Zhu; Simon L. Batzner; Albert Musaelian; Boris Kozinsky (CoRR, 2022)

Scaling the Leading Accuracy of Deep Equivariant Models to Biomolecular Simulations of Realistic Size. - Boris Kozinsky; Albert Musaelian; Anders Johansson; Simon L. Batzner (SC, 2023)

Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials. - Mgcini Keith Phuthi; Archie Mingze Yao; Simon L. Batzner; Albert Musaelian; Boris Kozinsky; Ekin Dogus Cubuk; Venkatasubramanian Viswanathan (CoRR, 2023)

Learning Interatomic Potentials at Multiple Scales. - Xiang Fu 005; Albert Musaelian; Anders Johansson; Tommi S. Jaakkola; Boris Kozinsky (CoRR, 2023)

A Recipe for Charge Density Prediction. - Xiang Fu 005; Andrew S. Rosen; Kyle Bystrom; Rui Wang 086; Albert Musaelian; Boris Kozinsky; Tess E. Smidt; Tommi S. Jaakkola (NeurIPS, 2024)

Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi. - Jonathan Vandermause; Anders Johansson; Yucong Miao; Joost J. Vlassak; Boris Kozinsky (CoRR, 2024)

The design space of E(3)-equivariant atom-centred interatomic potentials. - Ilyes Batatia; Simon L. Batzner; Dávid Péter Kovács; Albert Musaelian; Gregor N. C. Simm; Ralf Drautz; Christoph Ortner; Boris Kozinsky; Gábor Csányi (Nat. Mac. Intell., 2025)

A practical guide to machine learning interatomic potentials - Status and future. - Ryan Jacobs; Dane Morgan; Siamak Attarian; Jun Meng; Chen Shen; Zhenghao Wu; Clare Yijia Xie; Julia H. Yang; Nongnuch Artrith; Ben Blaiszik; Gerbrand Ceder; Kamal Choudhary; et al. (CoRR, 2025)

High-performance training and inference for deep equivariant interatomic potentials. - Chuin Wei Tan; Marc L. Descoteaux; Mit Kotak; Gabriel de Miranda Nascimento; Seán R. Kavanagh; Laura Zichi; Menghang Wang; Aadit Saluja; Yizhong R. Hu; Tess E. Smidt; Anders Johansson; William C. Witt; et al. (CoRR, 2025)

Coupled reaction and diffusion governing interface evolution in solid-state batteries. - Jingxuan Ding; Laura Zichi; Matteo Carli; Menghang Wang; Albert Musaelian; Yu Xie; Boris Kozinsky (CoRR, 2025)