Search Results for author: Santiago Miret

Found 35 papers, 19 papers with code

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models

1 code implementation5 Feb 2025 Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh

Generating novel crystalline materials has the potential to lead to advancements in fields such as electronics, energy storage, and catalysis.

valid

Stiefel Flow Matching for Moment-Constrained Structure Elucidation

no code implementations17 Dec 2024 Austin Cheng, Alston Lo, Kin Long Kelvin Lee, Santiago Miret, Alán Aspuru-Guzik

We consider the task of predicting a molecule's all-atom 3D structure given only its molecular formula and moments of inertia, motivated by the ability of rotational spectroscopy to measure these moments.

Foundational Large Language Models for Materials Research

1 code implementation12 Dec 2024 Vaibhav Mishra, Somaditya Singh, Dhruv Ahlawat, Mohd Zaki, Vaibhav Bihani, Hargun Singh Grover, Biswajit Mishra, Santiago Miret, Mausam, N. M. Anoop Krishnan

Here, we present LLaMat, a family of foundational models for materials science developed through continued pretraining of LLaMA models on an extensive corpus of materials literature and crystallographic data.

Domain Adaptation Model Selection

MatExpert: Decomposing Materials Discovery by Mimicking Human Experts

no code implementations26 Oct 2024 Qianggang Ding, Santiago Miret, Bang Liu

Material discovery is a critical research area with profound implications for various industries.

Contrastive Learning Retrieval

Deconstructing equivariant representations in molecular systems

1 code implementation10 Oct 2024 Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret

In this work, we report on a set of experiments using a simple equivariant graph convolution model on the QM9 dataset, focusing on correlating quantitative performance with the resulting molecular graph embeddings.

Property Prediction

HoneyComb: A Flexible LLM-Based Agent System for Materials Science

no code implementations29 Aug 2024 huan zhang, Yu Song, Ziyu Hou, Santiago Miret, Bang Liu

The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks for materials science.

From Text to Insight: Large Language Models for Materials Science Data Extraction

no code implementations23 Jul 2024 Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih, María Victoria Gil, Santiago Miret, Christoph T. Koch, José A. Márquez, Kevin Maik Jablonka

The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design.

MatText: Do Language Models Need More than Text & Scale for Materials Modeling?

1 code implementation25 Jun 2024 Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka

This challenge is further compounded by the absence of a comprehensive benchmark to rigorously evaluate the capabilities and limitations of these text representations in capturing the complexity of material systems.

Benchmarking

Are LLMs Ready for Real-World Materials Discovery?

1 code implementation7 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.

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

1 code implementation12 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.

Protein Structure Prediction Specificity

Towards equilibrium molecular conformation generation with GFlowNets

no code implementations20 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.

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

1 code implementation17 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.

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

1 code implementation12 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).

Language Modeling Language Modelling

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

no code implementations10 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.

Property Prediction

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

no code implementations3 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.

Atomic Forces Benchmarking +2

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

1 code implementation12 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.

Atomic Forces Diversity +1

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

no code implementations6 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.

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

1 code implementation14 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.

named-entity-recognition Named Entity Recognition +2

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

1 code implementation28 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.

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

1 code implementation18 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.

Language Modeling Language Modelling +2

Group SELFIES: A Robust Fragment-Based Molecular String Representation

1 code implementation23 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.

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

2 code implementations22 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.

Computational Efficiency

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

1 code implementation31 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.

Graph Neural Network

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

no code implementations14 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.

Quantization

Learning Intrinsic Symbolic Rewards in Reinforcement Learning

no code implementations8 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.

Deep Reinforcement Learning MuJoCo +2

Safety Aware Reinforcement Learning (SARL)

no code implementations6 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.

reinforcement-learning Reinforcement Learning +1

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