:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Nabavi, Seyed Reza, Guo, Zonglin, Wang, Zhiyuan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Chemical Physics Machine Learning
Accesso online:	https://arxiv.org/abs/2507.07641
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Multi-Objective Optimization and Multi-Criteria Decision-Making Approach to Design Multi-Tubular Packed-Bed Membrane Reactor in Oxidative Dehydrogenation of Ethane
di: Nabavi, Seyed Reza, et al.
Pubblicazione: (2024)

Deep Learning Aided Multi-Objective Optimization and Multi-Criteria Decision Making in Thermal Cracking Process for Olefines Production
di: Nabavi, Seyed Reza, et al.
Pubblicazione: (2024)

Sensitivity Assessment of Multi-Criteria Decision-Making Methods in Chemical Engineering Optimization Applications
di: Nabavi, Seyed Reza, et al.
Pubblicazione: (2024)

Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress and Potential
di: Wang, Zhiyuan, et al.
Pubblicazione: (2024)

Selected multi-criteria decision-making methods and their applications to product and system design
di: Wang, Zhiyuan, et al.
Pubblicazione: (2024)

Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
di: Remme, Roman, et al.
Pubblicazione: (2026)

A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction
di: Abdellatif, Alhasan, et al.
Pubblicazione: (2025)

Multi-Objective Bayesian Optimization with Independent Tanimoto Kernel Gaussian Processes for Diverse Pareto Front Exploration
di: Yong, Anabel
Pubblicazione: (2025)

A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions
di: Boledi, Leonardo, et al.
Pubblicazione: (2026)

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

Multi-Fidelity Machine Learning for Excited State Energies of Molecules
di: Vinod, Vivin, et al.
Pubblicazione: (2023)

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

Machine Learning Force Fields
di: Unke, Oliver T., et al.
Pubblicazione: (2020)

Ensemble Learning of Machine Learning Force Fields
di: Yin, Bangchen, et al.
Pubblicazione: (2024)

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
di: Cheng, Lixue, et al.
Pubblicazione: (2022)

Molecular Machine Learning in Chemical Process Design
di: Rittig, Jan G., et al.
Pubblicazione: (2025)

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
di: Liu, Yuzhi, et al.
Pubblicazione: (2026)

Machine-Learning Interatomic Potentials for Long-Range Systems
di: Ji, Yajie, et al.
Pubblicazione: (2025)

Bayesian Optimization of Catalysis With In-Context Learning
di: Ramos, Mayk Caldas, et al.
Pubblicazione: (2023)

Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
di: Vazquez-Salazar, Luis Itza, et al.
Pubblicazione: (2024)

Stable and Accurate Orbital-Free DFT Powered by Machine Learning
di: Remme, Roman, et al.
Pubblicazione: (2025)

Gradual Optimization Learning for Conformational Energy Minimization
di: Tsypin, Artem, et al.
Pubblicazione: (2023)

Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
di: Boiko, Daniil A., et al.
Pubblicazione: (2024)

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
di: Sanocki, Michal, et al.
Pubblicazione: (2025)

MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
di: Osaro, Etinosa, et al.
Pubblicazione: (2026)

Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
di: Baldwin, William J., et al.
Pubblicazione: (2026)

Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model
di: Zhao, Yingqi, et al.
Pubblicazione: (2024)

First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
di: Meir, Sagi, et al.
Pubblicazione: (2025)

AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
di: Farr, Stephen E., et al.
Pubblicazione: (2026)

Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
di: De Fabritiis, Gianni
Pubblicazione: (2024)

Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties
di: Vinod, Vivin, et al.
Pubblicazione: (2024)

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

From 2015 to 2023: How Machine Learning Aids Natural Product Analysis
di: Shi, Suwen, et al.
Pubblicazione: (2024)

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

Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques
di: Gökdemir, Tuğçe, et al.
Pubblicazione: (2024)

Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
di: Hayer, Nicolas, et al.
Pubblicazione: (2024)

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
di: Chen, Weilong, et al.
Pubblicazione: (2025)

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

Self-consistent Validation for Machine Learning Electronic Structure
di: Hu, Gengyuan, et al.
Pubblicazione: (2024)

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