Bayesian Optimization of Catalysis With In-Context Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Ramos, Mayk Caldas, Michtavy, Shane S., Porosoff, Marc D., White, Andrew D. |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks
by: Ramos, Mayk Caldas, et al.
Published: (2023)
by: Ramos, Mayk Caldas, et al.
Published: (2023)
A Review of Large Language Models and Autonomous Agents in Chemistry
by: Ramos, Mayk Caldas, et al.
Published: (2024)
by: Ramos, Mayk Caldas, et al.
Published: (2024)
Active Learning in Symbolic Regression with Physical Constraints
by: Medina, Jorge, et al.
Published: (2023)
by: Medina, Jorge, et al.
Published: (2023)
Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis
by: Vinchurkar, Tirtha, et al.
Published: (2024)
by: Vinchurkar, Tirtha, et al.
Published: (2024)
Multi-Objective Bayesian Optimization with Independent Tanimoto Kernel Gaussian Processes for Diverse Pareto Front Exploration
by: Yong, Anabel
Published: (2025)
by: Yong, Anabel
Published: (2025)
A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
by: Goswami, Rohit
Published: (2026)
by: Goswami, Rohit
Published: (2026)
Gradual Optimization Learning for Conformational Energy Minimization
by: Tsypin, Artem, et al.
Published: (2023)
by: Tsypin, Artem, et al.
Published: (2023)
Censoring chemical data to mitigate dual use risk
by: Campbell, Quintina L., et al.
Published: (2023)
by: Campbell, Quintina L., et al.
Published: (2023)
Clever Hans in Chemistry: Chemist Style Signals Confound Activity Prediction on Public Benchmarks
by: Blevins, Andrew D., et al.
Published: (2025)
by: Blevins, Andrew D., et al.
Published: (2025)
Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach
by: Ray, Pranoy, et al.
Published: (2025)
by: Ray, Pranoy, et al.
Published: (2025)
Diagnosing and fixing common problems in Bayesian optimization for molecule design
by: Tripp, Austin, et al.
Published: (2024)
by: Tripp, Austin, et al.
Published: (2024)
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
by: Eastman, Peter, et al.
Published: (2024)
by: Eastman, Peter, et al.
Published: (2024)
Sub-sampling of NMR Correlation and Exchange Experiments
by: Beckmann, Julian B. B., et al.
Published: (2023)
by: Beckmann, Julian B. B., et al.
Published: (2023)
Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
by: Judge, Edmund, et al.
Published: (2024)
by: Judge, Edmund, et al.
Published: (2024)
Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
by: Chen, Jie, et al.
Published: (2024)
by: Chen, Jie, et al.
Published: (2024)
Machine Learning-Assisted Surrogate Modeling with Multi-Objective Optimization and Decision-Making of a Steam Methane Reforming Reactor
by: Nabavi, Seyed Reza, et al.
Published: (2025)
by: Nabavi, Seyed Reza, et al.
Published: (2025)
SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration
by: Cavanagh, Joseph M., et al.
Published: (2024)
by: Cavanagh, Joseph M., et al.
Published: (2024)
OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems
by: Kang, Beom Seok, et al.
Published: (2025)
by: Kang, Beom Seok, et al.
Published: (2025)
Stable and Accurate Orbital-Free DFT Powered by Machine Learning
by: Remme, Roman, et al.
Published: (2025)
by: Remme, Roman, et al.
Published: (2025)
Molecule Generation and Optimization for Efficient Fragrance Creation
by: Rodrigues, Bruno C. L., et al.
Published: (2024)
by: Rodrigues, Bruno C. L., et al.
Published: (2024)
De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning
by: Hou, Junyu
Published: (2025)
by: Hou, Junyu
Published: (2025)
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
by: Woo, Jeheon, et al.
Published: (2024)
by: Woo, Jeheon, et al.
Published: (2024)
Ensemble Learning of Machine Learning Force Fields
by: Yin, Bangchen, et al.
Published: (2024)
by: Yin, Bangchen, et al.
Published: (2024)
Learning simple heuristic rules for classifying materials based on chemical composition
by: Ma, Andrew, et al.
Published: (2025)
by: Ma, Andrew, et al.
Published: (2025)
First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
by: Meir, Sagi, et al.
Published: (2025)
by: Meir, Sagi, et al.
Published: (2025)
Machine Learning Force Fields
by: Unke, Oliver T., et al.
Published: (2020)
by: Unke, Oliver T., et al.
Published: (2020)
Beyond Learning on Molecules by Weakly Supervising on Molecules
by: Prastalo, Gordan, et al.
Published: (2026)
by: Prastalo, Gordan, et al.
Published: (2026)
Molecular Machine Learning in Chemical Process Design
by: Rittig, Jan G., et al.
Published: (2025)
by: Rittig, Jan G., et al.
Published: (2025)
EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
by: Wang, Yuanqing, et al.
Published: (2023)
by: Wang, Yuanqing, et al.
Published: (2023)
A Bayesian Flow Network Framework for Chemistry Tasks
by: Tao, Nianze, et al.
Published: (2024)
by: Tao, Nianze, et al.
Published: (2024)
Machine-Learning Interatomic Potentials for Long-Range Systems
by: Ji, Yajie, et al.
Published: (2025)
by: Ji, Yajie, et al.
Published: (2025)
Meta-Learning Linear Models for Molecular Property Prediction
by: Pimonova, Yulia, et al.
Published: (2025)
by: Pimonova, Yulia, et al.
Published: (2025)
Generative Deep Learning Framework for Inverse Design of Fuels
by: Yalamanchi, Kiran K., et al.
Published: (2025)
by: Yalamanchi, Kiran K., et al.
Published: (2025)
Federated Learning from Molecules to Processes: A Perspective
by: Rittig, Jan G., et al.
Published: (2025)
by: Rittig, Jan G., et al.
Published: (2025)
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
by: Chen, Siqi, et al.
Published: (2026)
by: Chen, Siqi, et al.
Published: (2026)
How False Data Affects Machine Learning Models in Electrochemistry?
by: Deshsorna, Krittapong, et al.
Published: (2023)
by: Deshsorna, Krittapong, et al.
Published: (2023)
Deep Learning Foundation Models from Classical Molecular Descriptors
by: Burns, Jackson W., et al.
Published: (2025)
by: Burns, Jackson W., et al.
Published: (2025)
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2024)
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2024)
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
by: Varga-Umbrich, Eszter, et al.
Published: (2026)
by: Varga-Umbrich, Eszter, et al.
Published: (2026)
Implicit Delta Learning of High Fidelity Neural Network Potentials
by: Thaler, Stephan, et al.
Published: (2024)
by: Thaler, Stephan, et al.
Published: (2024)
Similar Items
-
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks
by: Ramos, Mayk Caldas, et al.
Published: (2023) -
A Review of Large Language Models and Autonomous Agents in Chemistry
by: Ramos, Mayk Caldas, et al.
Published: (2024) -
Active Learning in Symbolic Regression with Physical Constraints
by: Medina, Jorge, et al.
Published: (2023) -
Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis
by: Vinchurkar, Tirtha, et al.
Published: (2024) -
Multi-Objective Bayesian Optimization with Independent Tanimoto Kernel Gaussian Processes for Diverse Pareto Front Exploration
by: Yong, Anabel
Published: (2025)