Santiago Miret

According to our database, Santiago Miret authored at least 28 papers between 2019 and 2024.

Collaborative distances:

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

2024

Are large language models superhuman chemists?

Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Benedict Emoekabu, Aswanth Krishnan, Mara Wilhelmi, Macjonathan Okereke, Juliane Eberhardt, Amir Mohammad Elahi, Maximilian Greiner, Caroline T. Holick, Tanya Gupta, Mehrdad Asgari, Christina Glaubitz, Lea C. Klepsch, Yannik Köster, Jakob Meyer, Santiago Miret, Tim Hoffmann, Fabian Alexander Kreth, Michael Ringleb, Nicole Roesner, Ulrich S. Schubert, Leanne M. Stafast, Dinga Wonanke, Michael Pieler, Philippe Schwaller, Kevin Maik Jablonka

CoRR, 2024

Are LLMs Ready for Real-World Materials Discovery?

Santiago Miret, N. M. Anoop Krishnan

CoRR, 2024

2023

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems.

Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein

CoRR, 2023

Towards equilibrium molecular conformation generation with GFlowNets.

Alexandra Volokhova, Michal Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca A. Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio

CoRR, 2023

Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance.

Austin Cheng, Alston Lo, Santiago Miret, Brooks Pate, Alán Aspuru-Guzik

CoRR, 2023

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions.

Alvaro Carbonero, Alexandre Duval, Victor Schmidt, Santiago Miret, Alex Hernández-García, Yoshua Bengio, David Rolnick

CoRR, 2023

Searching for High-Value Molecules Using Reinforcement Learning and Transformers.

Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen Berseth

CoRR, 2023

EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations.

Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu, N. M. Anoop Krishnan

CoRR, 2023

MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling.

Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings, Mikhail Galkin, Santiago Miret

CoRR, 2023

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks.

Daniel T. Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh

CoRR, 2023

Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit.

Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings, Mikhail Galkin, Santiago Miret, Nalini Kumar

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts.

Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang

Proceedings of the International Conference on Machine Learning, 2023

Multi-Objective GFlowNets.

Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio

Proceedings of the International Conference on Machine Learning, 2023

FAENet: Frame Averaging Equivariant GNN for Materials Modeling.

Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick

Proceedings of the International Conference on Machine Learning, 2023

HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science.

Yu Song, Santiago Miret, Huan Zhang, Bang Liu

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model.

Parishad BehnamGhader, Santiago Miret, Siva Reddy

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling.

Yu Song, Santiago Miret, Bang Liu

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Group SELFIES: A Robust Fragment-Based Molecular String Representation.

Austin Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alán Aspuru-Guzik

CoRR, 2022

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design.

Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick

CoRR, 2022

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science.

Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings

CoRR, 2022

Learning Intrinsic Symbolic Rewards in Reinforcement Learning.

Hassam Ullah Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar, Mariano Phielipp

Proceedings of the International Joint Conference on Neural Networks, 2022

Neuroevolution-enhanced multi-objective optimization for mixed-precision quantization.

Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phiellip, Nilesh Jain, Somdeb Majumdar

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

2021

Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization.

Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phielipp, Nilesh Jain, Somdeb Majumdar

CoRR, 2021

Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning.

Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Learning Intrinsic Symbolic Rewards in Reinforcement Learning.

Hassam Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar

CoRR, 2020

Safety Aware Reinforcement Learning (SARL).

Santiago Miret, Somdeb Majumdar, Carroll Wainwright

CoRR, 2020

Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination.

Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen McAleer, Kagan Tumer

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Collaborative Evolutionary Reinforcement Learning.

Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer

Proceedings of the 36th International Conference on Machine Learning, 2019