Publications

(* denotes equal contribution)

MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery

Lingyu Kong, Nima Shoghi, Guoxiang Hu, Pan Li, Victor Fung

Published in arXiv preprint arXiv:2504.10655, 2025

Introduces MatterTune, a modular platform that enables fine-tuning of pre-trained atomistic foundation models for materials science applications, allowing researchers to overcome data limitations and seamlessly integrate advanced machine learning into materials discovery workflows.

From molecules to materials: Pre-training large generalizable models for atomic property prediction

Nima Shoghi, Adeesh Kolluru, John Kitchin, Zachary Ulissi, C Zitnick, Brandon Wood

Published in International Conference on Learning Representations, 2024

Introduces a multi-domain pre-training strategy for molecular property prediction that learns simultaneously from diverse chemical datasets, demonstrating substantial improvements over previous methods and advancing the ability to accurately predict properties across molecules and materials.

Distribution Learning for Molecular Regression

Nima Shoghi, Pooya Shoghi, Anuroop Sriram, Abhishek Das

Published in arXiv preprint arXiv:2407.20475, 2024

Introduces a novel approach called Distributional Mixture of Experts (DMoE) for molecular property prediction that improves accuracy by training models to predict probability distributions rather than single values, demonstrating significant performance gains across multiple datasets and model architectures.

The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts

Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, others

Published in ACS Catalysis, 2023

Develops the Open Catalyst 2022 (OC22) dataset to fill a critical gap in machine learning training data for oxide electrocatalysts, demonstrating improved prediction accuracy and establishing benchmarks for future research in renewable energy materials.

Context-Aware Task Handling in Resource-Constrained Robots with Virtualization

Ramyad Hadidi, Nima Shoghi, Bahar Asgari, Hyesoon Kim

Published in 2023 IEEE International Conference on Edge Computing and Communications (EDGE), 2023

Develops a fast context-aware technique that enables resource-constrained robots to handle multiple tasks simultaneously with improved timeliness, demonstrating a 42% speedup in execution time compared to standard scheduling approaches.

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, Siddharth Goyal, Abhishek Das, C Zitnick, Zachary Ulissi

Published in The Journal of Chemical Physics, 2022

Introduces TAAG, an attention-based transfer learning approach for graph neural networks that effectively transfers knowledge across diverse atomic systems, improving performance for out-of-domain datasets while achieving up to 4× speedup in model training.

Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery

Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati, Nima Shoghi, Abhishek Das, Brandon Wood, C Zitnick, John Kitchin, Zachary Ulissi

Published in ACS Catalysis, 2022

Examines the challenges in developing machine learning models that work across different chemical systems for catalyst discovery, highlighting recent progress with the Open Catalyst 2020 Dataset and identifying critical areas for future research.

SmaQ: Smart quantization for DNN training by exploiting value clustering

Nima Shoghi, Andrei Bersatti, Moinuddin Qureshi, Hyesoon Kim

Published in IEEE Computer Architecture Letters, 2021

Introduces a smart quantization technique that reduces memory usage during neural network training by up to 6.7x while maintaining accuracy by exploiting the normal distribution properties of neural network values.

Quantifying the design-space tradeoffs in autonomous drones

Ramyad Hadidi, Bahar Asgari, Sam Jijina, Adriana Amyette, Nima Shoghi, Hyesoon Kim

Published in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021

Explores and quantifies the design-space tradeoffs in autonomous drone systems, revealing that optimizing SLAM algorithms on FPGA hardware is particularly beneficial while also providing an open-source customizable drone platform.

Understanding the software and hardware stacks of a general-purpose cognitive drone

Sam Jijina, Adriana Amyette, Nima Shoghi, Ramyad Hadidi, Hyesoon Kim

Published in 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2020

Characterizes a widely-used open source flight stack to understand the performance requirements of autonomous drones, revealing that optimizing the flight controller software can dramatically increase the drone's flying range.

Secure Location-Aware Authentication and Communication for Intelligent Transportation Systems

Nima Shoghi, Ramyad Hadidi, Lee Jaewon, Jun Chen, Arthur Siqueria, Rahul Rajan, Shaan Dhawan, Pooya Shoghi, Hyesoon Kim

Published in arXiv preprint arXiv:2011.08936, 2020

Presents a novel security protocol for autonomous vehicles that integrates message authentication with visual localization, enabling vehicles to simultaneously verify messages and identify sender locations without additional computational costs or infrastructure requirements.

Neural network weight compression with nnw-bdi

Andrei Bersatti, Nima Shoghi, Hyesoon Kim

Published in Proceedings of the International Symposium on Memory Systems, 2020

Introduces NNW-BDI, a memory compression technique for neural network weights that reduces memory usage by up to 85% without sacrificing accuracy.

Slam performance on embedded robots

Nima Shoghi, Ramyad Hadidi, Hyesoon Kim

Published in Student Research Competition at Embedded System Week (SRC ESWEEK), 2019

Demonstrates that with appropriate optimizations, the ORB-SLAM2 algorithm can run in real-time on a Raspberry Pi 3B+ for embedded robotics applications, achieving a 5x speed increase with minimal impact on mapping accuracy.