Santiago Miret

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 40

Top Topics

Materials Science

Top Venues

EMNLP (Findings)

Publications

Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen Berseth
Searching for High-Value Molecules Using Reinforcement Learning and Transformers. ICLR (2024)
Santiago Miret, N. M. Anoop Krishnan
Are LLMs Ready for Real-World Materials Discovery? CoRR (2024)
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka
MatText: Do Language Models Need More than Text & Scale for Materials Modeling? CoRR (2024)
Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih, María Victoria Gil, Santiago Miret, Christoph T. Koch, José A. Márquez, Kevin Maik Jablonka
From Text to Insight: Large Language Models for Materials Science Data Extraction. CoRR (2024)
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
Are large language models superhuman chemists? CoRR (2024)
Yu Song, Santiago Miret, Huan Zhang, Bang Liu
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science. CoRR (2023)
Alvaro Carbonero, Alexandre Duval, Victor Schmidt, Santiago Miret, Alex Hernández-García, Yoshua Bengio, David Rolnick
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions. CoRR (2023)
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings, Mikhail Galkin, Santiago Miret
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling. CoRR (2023)
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR (2023)
Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick
FAENet: Frame Averaging Equivariant GNN for Materials Modeling. CoRR (2023)
Alexandra Volokhova, Michal Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca A. Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio
Towards equilibrium molecular conformation generation with GFlowNets. CoRR (2023)
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio
Multi-Objective GFlowNets. ICML (2023)
Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. CoRR (2023)
Yu Song, Santiago Miret, Bang Liu
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling. CoRR (2023)
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings, Mikhail Galkin, Santiago Miret, Nalini Kumar
Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit. SC Workshops (2023)
Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick
FAENet: Frame Averaging Equivariant GNN for Materials Modeling. ICML (2023)
Yu Song, Santiago Miret, Huan Zhang, Bang Liu
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science. EMNLP (Findings) (2023)
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu, N. M. Anoop Krishnan
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations. CoRR (2023)
Daniel Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks. CoRR (2023)
Yu Song, Santiago Miret, Bang Liu
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling. ACL (1) (2023)
Parishad BehnamGhader, Santiago Miret, Siva Reddy
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model. EMNLP (Findings) (2023)
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science. Trans. Mach. Learn. Res. 2023 (2023)
Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen Berseth
Searching for High-Value Molecules Using Reinforcement Learning and Transformers. CoRR (2023)
Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. ICML (2023)
Austin Cheng, Alston Lo, Santiago Miret, Brooks Pate, Alán Aspuru-Guzik
Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance. CoRR (2023)
Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phiellip, Nilesh Jain, Somdeb Majumdar
Neuroevolution-enhanced multi-objective optimization for mixed-precision quantization. GECCO (2022)
Austin Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alán Aspuru-Guzik
Group SELFIES: A Robust Fragment-Based Molecular String Representation. CoRR (2022)
Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design. CoRR (2022)
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science. CoRR (2022)
Hassam Ullah Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar, Mariano Phielipp
Learning Intrinsic Symbolic Rewards in Reinforcement Learning. IJCNN (2022)
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio
Multi-Objective GFlowNets. CoRR (2022)
Parishad BehnamGhader, Santiago Miret, Siva Reddy
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model. CoRR (2022)
Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phielipp, Nilesh Jain, Somdeb Majumdar
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization. CoRR (2021)
Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. ICLR (2021)
Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen McAleer, Kagan Tumer
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. ICML (2020)
Santiago Miret, Somdeb Majumdar, Carroll Wainwright
Safety Aware Reinforcement Learning (SARL). CoRR (2020)
Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Hanlin Tang, Shie Mannor, Tamir Hazan, Somdeb Majumdar
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. CoRR (2020)
Hassam Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar
Learning Intrinsic Symbolic Rewards in Reinforcement Learning. CoRR (2020)
Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer
Collaborative Evolutionary Reinforcement Learning. ICML (2019)
Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer
Collaborative Evolutionary Reinforcement Learning. CoRR (2019)