Copertina

AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kouroudis, Ioannis, Poonam, Misciaci, Neel, Mayr, Felix, Müller, Leon, Gu, Zhaosu, Gagliardi, Alessio
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Chemical Physics Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

An Artificial Intelligence (AI) workflow for catalyst design and optimization
di: Lai, Nung Siong, et al.
Pubblicazione: (2024)

Diagnosing and fixing common problems in Bayesian optimization for molecule design
di: Tripp, Austin, et al.
Pubblicazione: (2024)

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
di: Goldshlager, Gil, et al.
Pubblicazione: (2024)

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
di: Szabó, P. Bernát, et al.
Pubblicazione: (2026)

A 3D pocket-aware and evolutionary conserved interaction guided diffusion model for molecular optimization
di: Qiao, Anjie, et al.
Pubblicazione: (2025)

Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers
di: MacKnight, Robert, et al.
Pubblicazione: (2025)

Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
di: Neporozhnii, Ihor, et al.
Pubblicazione: (2026)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
di: Matin, Sakib, et al.
Pubblicazione: (2025)

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning
di: Hu, Frank, et al.
Pubblicazione: (2024)

StringNET: Neural Network based Variational Method for Transition Pathways
di: Han, Jiayue, et al.
Pubblicazione: (2024)

Generating transition states of chemical reactions via distance-geometry-based flow matching
di: Luo, Yufei, et al.
Pubblicazione: (2025)

Using GNN property predictors as molecule generators
di: Therrien, Félix, et al.
Pubblicazione: (2024)

Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
di: Maennel, Hartmut, et al.
Pubblicazione: (2024)

An efficient method based on the evolutionary center algorithm for optimizing chemical-diffusive models for flame acceleration and DDT
di: Xiao, Huahua, et al.
Pubblicazione: (2026)

Transferable Boltzmann Generators
di: Klein, Leon, et al.
Pubblicazione: (2024)

From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
di: Frank, J. Thorben, et al.
Pubblicazione: (2023)

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
di: Trestman, Morris, et al.
Pubblicazione: (2025)

Stochastic hierarchical data-driven optimization: application to plasma-surface kinetics
di: Afonso, José, et al.
Pubblicazione: (2026)

Enhancing molecular dynamics with equivariant machine-learned densities
di: Bogojeski, Mihail, et al.
Pubblicazione: (2026)

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

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

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

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

Operator Forces For Coarse-Grained Molecular Dynamics
di: Klein, Leon, et al.
Pubblicazione: (2025)

Human-level molecular optimization driven by mol-gene evolution
di: Fang, Jiebin, et al.
Pubblicazione: (2024)

Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux
di: Gharbi, Mouna, et al.
Pubblicazione: (2025)

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
di: Liu, Junlan, et al.
Pubblicazione: (2024)

Molecular relaxation by reverse diffusion with time step prediction
di: Kahouli, Khaled, et al.
Pubblicazione: (2024)

Molecular docking via quantum approximate optimization algorithm
di: Ding, Qi-Ming, et al.
Pubblicazione: (2023)

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

ReactionTeam: Teaming Experts for Divergent Thinking Beyond Typical Reaction Patterns
di: Guo, Taicheng, et al.
Pubblicazione: (2023)

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
di: Pavšek, Jan, et al.
Pubblicazione: (2026)

Tensor Hypercontraction Error Correction Using Regression
di: Satyarth, Ishna, et al.
Pubblicazione: (2026)

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

Computing excited states of molecules using normalizing flows
di: Saleh, Yahya, et al.
Pubblicazione: (2023)

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

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
di: Ji, Shengjiao, et al.
Pubblicazione: (2025)

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

Beyond Learning on Molecules by Weakly Supervising on Molecules
di: Prastalo, Gordan, et al.
Pubblicazione: (2026)

Extending machine learning model for implicit solvation to free energy calculations
di: Dey, Rishabh, et al.
Pubblicazione: (2025)