Cover Image

Discovery of Sustainable Refrigerants through Physics-Informed RL Fine-Tuning of Sequence Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Goldszal, Adrien, Calanzone, Diego, Taboga, Vincent, Bacon, Pierre-Luc
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mol-MoE: Training Preference-Guided Routers for Molecule Generation
by: Calanzone, Diego, et al.
Published: (2025)

RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models
by: Liu, Shikun, et al.
Published: (2025)

Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discovery
by: Geitner, Patrick
Published: (2024)

Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery
by: Xu, Guikun, et al.
Published: (2025)

Materials Discovery using Max K-Armed Bandit
by: Kikkawa, Nobuaki, et al.
Published: (2022)

Foundation Models for Discovery and Exploration in Chemical Space
by: Wadell, Alexius, et al.
Published: (2025)

Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism
by: Ni, Tianwei, et al.
Published: (2025)

Unveiling Latent Knowledge in Chemistry Language Models through Sparse Autoencoders
by: Cohen, Jaron, et al.
Published: (2025)

A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
by: Semnani, Parastoo, et al.
Published: (2024)

Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity
by: Muni, Aneri, et al.
Published: (2026)

The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA
by: Youngmin, Lee, et al.
Published: (2024)

Physics-Informed Weakly Supervised Learning for Interatomic Potentials
by: Takamoto, Makoto, et al.
Published: (2024)

Chemically-Informed Machine Learning Approach for Prediction of Reactivity Ratios in Radical Copolymerization
by: Safari, Habibollah, et al.
Published: (2025)

Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials
by: Chen, Li, et al.
Published: (2026)

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model
by: Xu, Shihang, et al.
Published: (2024)

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks
by: Podina, Lena, et al.
Published: (2024)

Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
by: Yang, Soojung, et al.
Published: (2024)

A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning
by: Comesana, Ana E., et al.
Published: (2024)

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
by: Ansari, Narjes, et al.
Published: (2026)

MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
by: Yang, Lake, et al.
Published: (2026)

Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
by: Na, Gyoung S., et al.
Published: (2026)

chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
by: Fuchs, Paul, et al.
Published: (2024)

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
by: Ren, Yuxuan, et al.
Published: (2024)

Evaluating Large Language Models in Scientific Discovery
by: Song, Zhangde, et al.
Published: (2025)

The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
by: Sriram, Anuroop, et al.
Published: (2025)

Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling
by: Wu, Lirong, et al.
Published: (2025)

ChemKANs for Combustion Chemistry Modeling and Acceleration
by: Koenig, Benjamin C., et al.
Published: (2025)

MUBen: Benchmarking the Uncertainty of Molecular Representation Models
by: Li, Yinghao, et al.
Published: (2023)

Meta-Learning Linear Models for Molecular Property Prediction
by: Pimonova, Yulia, et al.
Published: (2025)

Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
by: Eastman, Peter, et al.
Published: (2024)

Diffusion Models in $\textit{De Novo}$ Drug Design
by: Alakhdar, Amira, et al.
Published: (2024)

Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models
by: Chen, Shuan, et al.
Published: (2024)

Deep Learning Foundation Models from Classical Molecular Descriptors
by: Burns, Jackson W., et al.
Published: (2025)

Teaching Language Models Mechanistic Explainability Through MechSMILES
by: Neukomm, Théo A., et al.
Published: (2025)

Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
by: Woo, Jeheon, et al.
Published: (2024)

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
by: Varga-Umbrich, Eszter, et al.
Published: (2026)

How False Data Affects Machine Learning Models in Electrochemistry?
by: Deshsorna, Krittapong, et al.
Published: (2023)

Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis
by: Vinchurkar, Tirtha, et al.
Published: (2024)

DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation
by: Yang, Qingsong, et al.
Published: (2025)

DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis
by: Eivazi, Hamidreza, et al.
Published: (2024)