Samuel M. Blau

According to our database, Samuel M. Blau authored at least 11 papers between 2020 and 2026.

Collaborative distances:

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

2026

Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials.

Alex Morehead, Miruna T. Cretu, Antonia Panescu, Rishabh Anand, Maurice Weiler, Tynan Perez, Samuel M. Blau, Steven Farrell, Wahid Bhimji, Anubhav Jain, Hrushikesh Sahasrabuddhe, Pietro Lio, Tommi S. Jaakkola, Rafael Gómez-Bombarelli, Rex Ying, N. Benjamin Erichson, Michael W. Mahoney

CoRR, February, 2026

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures.

Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan

CoRR, February, 2026

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs.

Eric Sivonxay, Lucas Attia, Evan Walter Clark Spotte-Smith, Benjamín Sánchez-Lengeling, Xiaojing Xia, Daniel Barter, Emory M. Chan, Samuel M. Blau

Nat. Comput. Sci., January, 2026

2025

Learning Inter-Atomic Potentials without Explicit Equivariance.

Ahmed A. A. Elhag, Arun Raja, Alex Morehead, Samuel M. Blau, Garrett M. Morris, Michael M. Bronstein

CoRR, October, 2025

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.

Rishabh D. Guha, Santiago Vargas, Evan Walter Clark Spotte-Smith, Alexander Rizzolo Epstein, Maxwell C. Venetos, Ryan Kingsbury, Mingjian Wen, Samuel M. Blau, Kristin A. Persson

J. Chem. Inf. Model., 2025

MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform.

Yuan Chiang, Tobias Kreiman, Christine Zhang, Matthew C. Kuner, Elizabeth Weaver, Ishan Amin, Hyunsoo Park, Yunsung Lim, Jihan Kim, Daryl Chrzan, Aron Walsh, Samuel M. Blau, Mark Asta, Aditi S. Krishnapriyan

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

2024

RNMC: kinetic Monte Carlo implementations for complex reaction networks.

Laura Zichi, Daniel Barter, Eric Sivonxay, Evan Walter Clark Spotte-Smith, Rohith Srinivaas Mohanakrishnan, Emory M. Chan, Kristin A. Persson, Samuel M. Blau

J. Open Source Softw., December, 2024

Advancing Molecular Machine (Learned) Representations with Stereoelectronics-Infused Molecular Graphs.

Daniil A. Boiko, Thiago Reschützegger, Benjamín Sánchez-Lengeling, Samuel M. Blau, Gabe Gomes

CoRR, 2024

2023

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set.

Aaron G. Garrison, Javier Heras-Domingo, John R. Kitchin, Gabriel dos Passos Gomes, Zachary W. Ulissi, Samuel M. Blau

J. Chem. Inf. Model., December, 2023

Chemical reaction networks and opportunities for machine learning.

Mingjian Wen, Evan Walter Clark Spotte-Smith, Samuel M. Blau, Matthew J. McDermott, Aditi S. Krishnapriyan, Kristin A. Persson

Nat. Comput. Sci., 2023

2020

Aqueous Diels-Alder reactions for thermochemical storage and heat transfer fluids identified using density functional theory.

Evan Walter Clark Spotte-Smith, Peiyuan Yu, Samuel M. Blau, Ravi S. Prasher, Anubhav Jain

J. Comput. Chem., 2020