Anna M. Hiszpanski

According to our database, Anna M. Hiszpanski authored at least 12 papers between 2020 and 2025.

Collaborative distances:

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

2025

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models.

Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun, Peggy Li, James Diffenderfer, Busra Demirci, Obadiah Smolenski, Tim Hsu, Anna M. Hiszpanski, Kenneth Chiu, Bhavya Kailkhura, Brian Van Essen

CoRR, May, 2025

Active Learning Enables Extrapolation in Molecular Generative Models.

Evan R. Antoniuk, Peggy Li, Nathan Keilbart, Stephen Weitzner, Bhavya Kailkhura, Anna M. Hiszpanski

CoRR, January, 2025

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models.

Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun, Peggy Li, James Diffenderfer, Büsra Sahin, Obadiah Smolenski, Everett Grethel, Tim Hsu, Anna M. Hiszpanski, Kenneth Chiu, Bhavya Kailkhura, Brian Van Essen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

MaTableGPT: GPT-based Table Data Extractor from Materials Science Literature.

Gyeong Hoon Yi, Jiwoo Choi, Hyeongyun Song, Olivia Miano, Jaewoong Choi, Kihoon Bang, Byungju Lee, Seok Su Sohn, David Buttler, Anna M. Hiszpanski, Sang Soo Han, Donghun Kim

CoRR, 2024

2022

Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions.

Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, Anna M. Hiszpanski

J. Chem. Inf. Model., 2022

Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning.

Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong

CoRR, 2022

2021

Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning.

Phan Nguyen, Donald Loveland, Joanne Taery Kim, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han

J. Chem. Inf. Model., 2021

Reliable Graph Neural Network Explanations Through Adversarial Training.

Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han

CoRR, 2021

2020

Automated Identification of Molecular Crystals' Packing Motifs.

Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han

J. Chem. Inf. Model., 2020

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge.

Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David J. Buttler, Thomas Yong-Jin Han

J. Chem. Inf. Model., 2020

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design.

Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han

CoRR, 2020

Actionable Attribution Maps for Scientific Machine Learning.

Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han

CoRR, 2020