ReviewACS omega2020
Machine Learning in Catalysis, From Proposal to Practicing.
Review in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Photoelectrocatalytic urea synthesis: Surface engineering of the constructions, mechanism insights, and technological challenges.iScience · 2026Review
- Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites.Communications chemistry · 2026Review
- Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques.JACS Au · 2025Article
- Modeling the potential energy surface by force fields for heterogeneous catalysis: classification, applications, and challenges.Chemical science · 2025Review
- Infrared Photovoltaic-Battery Hybrid Systems Enabled by Colloidal Quantum Dots.Chemistry, an Asian journal · 2025Review
- Reaction-conditioned generative model for catalyst design and optimization with CatDRX.Communications chemistry · 2025Article
- Quantum Dot-Based Luminescent Sensors: Review from Analytical Perspective.International journal of molecular sciences · 2025Review
- DFT-metadynamics insights on the origin of the oxygen evolution kinetics at the (100)-WSeiScience · 2025Article
- Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning.Beilstein journal of organic chemistry · 2025Review
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis.Precision chemistry · 2024Review
- Redefining the Symphony of Light Aromatic Synthesis Beyond Fossil Fuels: A Journey Navigating through a Fe-Based/HZSM-5 Tandem Route for Syngas Conversion.ACS catalysis · 2024Review
- Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.Natural product reports · 2024Review
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- A Brief Review of Recent Theoretical Advances in Fe-Based Catalysts for COMolecules (Basel, Switzerland) · 2024Review
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
- Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction.ACS central science · 2023Article
- Towards Machine Learning in Heterogeneous Catalysis-A Case Study of 2,4-Dinitrotoluene Hydrogenation.International journal of molecular sciences · 2023Article
- Photo-Antibacterial Activity of Two-Dimensional (2D)-Based Hybrid Materials: Effective Treatment Strategy for Controlling Bacterial Infection.Antibiotics (Basel, Switzerland) · 2023Review
- Recent Developments and Trends in Asymmetric Organocatalysis.European journal of organic chemistry · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, machine learning (ML) methods have gained popularity and have performed as powerfully predictive tools in various areas of academic and industrious activities. In comparison, their application in catalysis has been underdeveloped. Relying on the rapid development of different algorithms and their implementation, it is the right timing to harvest the potential of ML in catalysis across academy and industry spectra. Herein, we discuss the current applications in the field of homogeneous and heterogeneous catalysis by using various ML approaches. To the best of our knowledge, modern statistical learning techniques will be a strong tool for computational optimization and discovery. This in turn will accurately extract the underlying mechanism in the model that converts readily available data and precatalysts into their promising and useful ones.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.