Anna M. Hiszpanski

According to our database, Anna M. Hiszpanski authored at least 8 papers between 2020 and 2022.

Collaborative distances:

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

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