Antonio del Rio Chanona
@antonioe89.bsky.social
Academic at Imperial College London
Optimisation and Machine Learning in Systems Engineering
PhD Cambridge | UG UNAM 🇲🇽
he/him
https://www.optimlpse.co.uk/people/
Optimisation and Machine Learning in Systems Engineering
PhD Cambridge | UG UNAM 🇲🇽
he/him
https://www.optimlpse.co.uk/people/
𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 ⚛️:
➡️ Machine learning frameworks for chemical retrosynthesis: pubs.rsc.org/en/content/a...
➡️ Machine learning frameworks for chemical retrosynthesis: pubs.rsc.org/en/content/a...
Investigating the reliability and interpretability of machine learning frameworks for chemical retrosynthesis
Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance c...
pubs.rsc.org
January 14, 2025 at 6:49 PM
𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 ⚛️:
➡️ Machine learning frameworks for chemical retrosynthesis: pubs.rsc.org/en/content/a...
➡️ Machine learning frameworks for chemical retrosynthesis: pubs.rsc.org/en/content/a...
𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗟𝗟𝗠𝘀 🧠:
➡️ Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring: arxiv.org/abs/2501.07324
➡️ Improved Scholarly Document Comprehension for Large Language Models: aclanthology.org/2024.sdp-1.28/
➡️ Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring: arxiv.org/abs/2501.07324
➡️ Improved Scholarly Document Comprehension for Large Language Models: aclanthology.org/2024.sdp-1.28/
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it typically needs human...
arxiv.org
January 14, 2025 at 6:49 PM
𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗟𝗟𝗠𝘀 🧠:
➡️ Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring: arxiv.org/abs/2501.07324
➡️ Improved Scholarly Document Comprehension for Large Language Models: aclanthology.org/2024.sdp-1.28/
➡️ Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring: arxiv.org/abs/2501.07324
➡️ Improved Scholarly Document Comprehension for Large Language Models: aclanthology.org/2024.sdp-1.28/
𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 ⛰️:
➡️ Surrogate-Based Optimization: arxiv.org/abs/2412.13948
➡️ Hierarchical planning-scheduling-control: arxiv.org/abs/2310.07870
➡️ Discrete and mixed-variable experimental design with surrogate-based approach: pubs.rsc.org/en/Content/A...
➡️ Surrogate-Based Optimization: arxiv.org/abs/2412.13948
➡️ Hierarchical planning-scheduling-control: arxiv.org/abs/2310.07870
➡️ Discrete and mixed-variable experimental design with surrogate-based approach: pubs.rsc.org/en/Content/A...
January 14, 2025 at 6:49 PM
𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 ⛰️:
➡️ Surrogate-Based Optimization: arxiv.org/abs/2412.13948
➡️ Hierarchical planning-scheduling-control: arxiv.org/abs/2310.07870
➡️ Discrete and mixed-variable experimental design with surrogate-based approach: pubs.rsc.org/en/Content/A...
➡️ Surrogate-Based Optimization: arxiv.org/abs/2412.13948
➡️ Hierarchical planning-scheduling-control: arxiv.org/abs/2310.07870
➡️ Discrete and mixed-variable experimental design with surrogate-based approach: pubs.rsc.org/en/Content/A...
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 🔬:
➡️ The automated discovery of kinetic rate models: pubs.rsc.org/en/content/a...
➡️ Simplest Mechanism Builder Algorithm (SiMBA): arxiv.org/abs/2410.21205
➡️ The automated discovery of kinetic rate models: pubs.rsc.org/en/content/a...
➡️ Simplest Mechanism Builder Algorithm (SiMBA): arxiv.org/abs/2410.21205
The automated discovery of kinetic rate models – methodological frameworks
The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, while data-driven and...
pubs.rsc.org
January 14, 2025 at 6:48 PM
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 🔬:
➡️ The automated discovery of kinetic rate models: pubs.rsc.org/en/content/a...
➡️ Simplest Mechanism Builder Algorithm (SiMBA): arxiv.org/abs/2410.21205
➡️ The automated discovery of kinetic rate models: pubs.rsc.org/en/content/a...
➡️ Simplest Mechanism Builder Algorithm (SiMBA): arxiv.org/abs/2410.21205
#𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 📈
➡️ Bayesian optimization for discovery of reactor designs: www.nature.com/articles/s44...
➡️ Human-algorithm collaborative Bayesian optimization: arxiv.org/abs/2404.10949
➡️ Bayesian optimization for discovery of reactor designs: www.nature.com/articles/s44...
➡️ Human-algorithm collaborative Bayesian optimization: arxiv.org/abs/2404.10949
Machine learning-assisted discovery of flow reactor designs - Nature Chemical Engineering
Identifying the optimal geometry of continuous flow reactors is a major challenge due to the large available parameter design space. Here the authors combine a machine learning-assisted methodology wi...
www.nature.com
January 14, 2025 at 6:48 PM
#𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 📈
➡️ Bayesian optimization for discovery of reactor designs: www.nature.com/articles/s44...
➡️ Human-algorithm collaborative Bayesian optimization: arxiv.org/abs/2404.10949
➡️ Bayesian optimization for discovery of reactor designs: www.nature.com/articles/s44...
➡️ Human-algorithm collaborative Bayesian optimization: arxiv.org/abs/2404.10949
#𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🤖:
➡️ Control-Informed Reinforcement Learning: arxiv.org/abs/2408.13566
➡️ Graph Neural Networks and Multi-Agent Reinforcement Learning: arxiv.org/abs/2410.18631
➡️ PC-Gym: Benchmark Environments for Process Control Problems: arxiv.org/html/2410.22...
➡️ Control-Informed Reinforcement Learning: arxiv.org/abs/2408.13566
➡️ Graph Neural Networks and Multi-Agent Reinforcement Learning: arxiv.org/abs/2410.18631
➡️ PC-Gym: Benchmark Environments for Process Control Problems: arxiv.org/html/2410.22...
Control-Informed Reinforcement Learning for Chemical Processes
This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of deep reinforcement lear...
arxiv.org
January 14, 2025 at 6:47 PM
#𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🤖:
➡️ Control-Informed Reinforcement Learning: arxiv.org/abs/2408.13566
➡️ Graph Neural Networks and Multi-Agent Reinforcement Learning: arxiv.org/abs/2410.18631
➡️ PC-Gym: Benchmark Environments for Process Control Problems: arxiv.org/html/2410.22...
➡️ Control-Informed Reinforcement Learning: arxiv.org/abs/2408.13566
➡️ Graph Neural Networks and Multi-Agent Reinforcement Learning: arxiv.org/abs/2410.18631
➡️ PC-Gym: Benchmark Environments for Process Control Problems: arxiv.org/html/2410.22...