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ORCIDSantiago Miret
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2020 – today
- 2024
[j2]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. J. Mach. Learn. Res. 25: 106:1-106:26 (2024)- [c13]Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen Berseth:
Searching for High-Value Molecules Using Reinforcement Learning and Transformers. ICLR 2024 - [i27]Santiago Miret, N. M. Anoop Krishnan:
Are LLMs Ready for Real-World Materials Discovery? CoRR abs/2402.05200 (2024) - [i26]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 abs/2404.01475 (2024) - [i25]Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka:
MatText: Do Language Models Need More than Text & Scale for Materials Modeling? CoRR abs/2406.17295 (2024) - [i24]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 abs/2407.16867 (2024) - [i23]Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, Bang Liu:
HoneyComb: A Flexible LLM-Based Agent System for Materials Science. CoRR abs/2409.00135 (2024) - 2023
- [j1]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) - [c12]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: 3621-3639 - [c11]Yu Song, Santiago Miret, Huan Zhang, Bang Liu:
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science. EMNLP (Findings) 2023: 5724-5739 - [c10]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: 15492-15509 - [c9]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: 9013-9033 - [c8]Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio:
Multi-Objective GFlowNets. ICML 2023: 14631-14653 - [c7]Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang:
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. ICML 2023: 38749-38767 - [c6]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: 51-59 - [i22]Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang:
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. CoRR abs/2301.12040 (2023) - [i21]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 abs/2305.05577 (2023) - [i20]Yu Song, Santiago Miret, Bang Liu:
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling. CoRR abs/2305.08264 (2023) - [i19]Daniel T. Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh:
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks. CoRR abs/2309.03139 (2023) - [i18]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 abs/2309.05934 (2023) - [i17]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 abs/2310.02428 (2023) - [i16]Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen Berseth:
Searching for High-Value Molecules Using Reinforcement Learning and Transformers. CoRR abs/2310.02902 (2023) - [i15]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 abs/2310.06682 (2023) - [i14]Yu Song, Santiago Miret, Huan Zhang, Bang Liu:
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science. CoRR abs/2310.08511 (2023) - [i13]Austin H. 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 abs/2310.11609 (2023) - [i12]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 abs/2310.14782 (2023) - [i11]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 abs/2312.07511 (2023) - 2022
- [c5]Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phiellip, Nilesh Jain, Somdeb Majumdar:
Neuroevolution-enhanced multi-objective optimization for mixed-precision quantization. GECCO 2022: 1057-1065 - [c4]Hassam Ullah Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar, Mariano Phielipp:
Learning Intrinsic Symbolic Rewards in Reinforcement Learning. IJCNN 2022: 1-8 - [i10]Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio:
Multi-Objective GFlowNets. CoRR abs/2210.12765 (2022) - [i9]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 abs/2210.17484 (2022) - [i8]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 abs/2211.12020 (2022) - [i7]Austin H. Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alán Aspuru-Guzik:
Group SELFIES: A Robust Fragment-Based Molecular String Representation. CoRR abs/2211.13322 (2022) - [i6]Parishad BehnamGhader, Santiago Miret, Siva Reddy:
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model. CoRR abs/2212.09146 (2022) - 2021
- [c3]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 - [i5]Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phielipp, Nilesh Jain, Somdeb Majumdar:
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization. CoRR abs/2106.07611 (2021) - 2020
- [c2]Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen McAleer, Kagan Tumer:
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. ICML 2020: 6651-6660 - [i4]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 abs/2007.07298 (2020) - [i3]Santiago Miret, Somdeb Majumdar, Carroll Wainwright:
Safety Aware Reinforcement Learning (SARL). CoRR abs/2010.02846 (2020) - [i2]Hassam Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar:
Learning Intrinsic Symbolic Rewards in Reinforcement Learning. CoRR abs/2010.03694 (2020)
2010 – 2019
- 2019
- [c1]Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer:
Collaborative Evolutionary Reinforcement Learning. ICML 2019: 3341-3350 - [i1]Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer:
Collaborative Evolutionary Reinforcement Learning. CoRR abs/1905.00976 (2019) - 2018
- [b1]Santiago Miret:
Computational Design of Ceramic Matrix Composites for Turbine Blade Applications. University of California, Berkeley, USA, 2018
Coauthor Index
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last updated on 2024-10-07 21:13 CEST by the dblp team
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