no code implementations • ICML 2020 • Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer
Training policies solely on the team-based reward is often difficult due to its sparsity.
no code implementations • 1 Apr 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
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained.
no code implementations • 7 Feb 2024 • Santiago Miret, N M Anoop Krishnan
Given those shortcomings, we outline a framework for developing Materials Science LLMs (MatSci-LLMs) that are grounded in materials science knowledge and hypothesis generation followed by hypothesis testing.
1 code implementation • 12 Dec 2023 • Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, Michael Bronstein
In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.
no code implementations • 20 Oct 2023 • Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
1 code implementation • 17 Oct 2023 • Austin Cheng, Alston Lo, Santiago Miret, Brooks Pate, Alán Aspuru-Guzik
KREED's top-1 predictions identify the correct 3D structure with >98% accuracy on the QM9 and GEOM datasets when provided with substitution coordinates of all heavy atoms with natural isotopic abundance.
1 code implementation • 12 Oct 2023 • Yu Song, Santiago Miret, huan zhang, Bang Liu
We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted for materials science (HoneyBee).
no code implementations • 10 Oct 2023 • Alvaro Carbonero, Alexandre Duval, Victor Schmidt, Santiago Miret, Alex Hernandez-Garcia, Yoshua Bengio, David Rolnick
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms.
no code implementations • 4 Oct 2023 • Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen Berseth
Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs.
no code implementations • 3 Oct 2023 • Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, N M Anoop Krishnan
In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks.
Ranked #1 on Formation Energy on GeTe
1 code implementation • 12 Sep 2023 • Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings, Mikhail Galkin, Santiago Miret
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
no code implementations • 6 Sep 2023 • Daniel Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh
We present a natural extension to E(n)-equivariant graph neural networks that uses multiple equivariant vectors per node.
1 code implementation • 14 May 2023 • Yu Song, Santiago Miret, Bang Liu
Our experiments in this low-resource training setting show that language models pretrained on scientific text outperform BERT trained on general text.
1 code implementation • 28 Apr 2023 • Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries.
1 code implementation • 28 Jan 2023 • Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang
On downstream tasks, ProtST enables both supervised learning and zero-shot prediction.
1 code implementation • 18 Dec 2022 • Parishad BehnamGhader, Santiago Miret, Siva Reddy
Our findings indicate that the simple similarity metric employed by retrievers is insufficient for retrieving all the necessary statements for reasoning.
1 code implementation • 23 Nov 2022 • Austin Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alán Aspuru-Guzik
We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees.
2 code implementations • 22 Nov 2022 • Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick
Catalyst materials play a crucial role in the electrochemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis.
1 code implementation • 31 Oct 2022 • Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset.
1 code implementation • 23 Oct 2022 • Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization.
no code implementations • 14 Jun 2021 • Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phielipp, Nilesh Jain, Somdeb Majumdar
In this work, we present a flexible and scalable framework for automated mixed-precision quantization that concurrently optimizes task performance, memory compression, and compute savings through multi-objective evolutionary computing.
no code implementations • 8 Oct 2020 • Hassam Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar
We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks.
no code implementations • 6 Oct 2020 • Santiago Miret, Somdeb Majumdar, Carroll Wainwright
Since the safe agent effectively abstracts a task-independent notion of safety via its action probabilities, it can be ported to modulate multiple policies solving different tasks within the given environment without further training.
no code implementations • ICLR 2021 • Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation.
no code implementations • 18 Jun 2019 • Shauharda Khadka, Somdeb Majumdar, Santiago Miret, Stephen Mcaleer, Kagan Tumer
Training policies solely on the team-based reward is often difficult due to its sparsity.
1 code implementation • 2 May 2019 • Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer
Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks.