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Results for algorithm performance testing

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12 papers · ranked by Valyu relevance

01
CchemRxiv20 Jun 2024
Machine Learning for High Energy Density Hydrocarbons for Sustainable Aviation Fuel
Dilip Rijal, Vladislav Vasilyev, Feng Wang
Sustainable aviation fuels (SAFs) are crucial for addressing carbon emissions in the aviation industry. With a focus on SAFs, the research aims to establish a quantitative structure-property relationship for polycyclic hydrocarbons (PCHCs) and their net heat of combustion (NHOC) using the innovative approach of machine…
78relevance
02
CchemRxiv23 Dec 2025
Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
76relevance
03
CchemRxiv12 Aug 2024
Path Toward High-Throughput Synthesis Planning via Performance Benchmarking
Brandon Walker, Nathan Miller, Brett Yang, Dhatri V. L. Penna + 15 more
Rapid generation and evaluation of diverse synthesis pathways play a critical role in exploring a broader chemical space and identifying potent drug candidates. Drug discovery often relies on laborintensive manual processes for retro synthetic route finding, resulting in challenges related to scalability and…
75relevance
04
CchemRxiv24 Feb 2025
Enhancing Monte Carlo Tree Search for Retrosynthesis
Authors not listed
Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to the Monte Carlo tree search (MCTS) algorithm. The enhancements were…
74relevance
05
CchemRxiv20 Nov 2023
Multi-Variable Multi-Metric Optimisation of Self-Assembled Photocatalytic CO2 Reduction Performance using Machine Learning Algorithms
Shannon Bonke, Giovanni Trezza, Luca Bergamasco, Hongwei Song + 4 more
The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimisation approaches are poorly suited to multi-variable multi-metric photocatalytic systems because they aim to optimise one performance metric while sacrificing the…
70relevance
06
CchemRxiv23 Jan 2025
Generalised Fault Diagnostics of Polymer Electrolyte Fuel Cells Using Machine Learning
Authors not listed
The ongoing threat of global warming necessitates a shift towards clean energy sources to meet rising demands while reducing carbon emissions. Polymer electrolyte fuel cells (PEFCs) represent a promising technology for both mobile and stationary applications but their poor operational lifetimes and frequent faults are…
68relevance
07
CchemRxiv17 Dec 2025
Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
65relevance
08
CchemRxiv28 Apr 2021
MAYGEN - an Open-Source Chemical Structure Generator for Constitutional Isomers Based on the Orderly Generation Principle
Mehmet Aziz Yirik, Maria Sorokina, Christoph Steinbeck
The generation of constitutional isomer chemical spaces has been a subject of cheminformatics since the early 1960s, with applications in structure elucidation and elsewhere. In order to perform such a generation efficiently, exhaustively and isomorphism-free, the structure generator needs to ensure the building of…
64relevance
09
CchemRxiv17 Feb 2022
Graph-Based Molecular Pareto Optimisation
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
61relevance
10
CchemRxiv3 May 2021
MAYGEN - an Open-Source Chemical Structure Generator for Constitutional Isomers Based on the Orderly Generation Principle
Mehmet Aziz Yirik, Maria Sorokina, Christoph Steinbeck
The generation of constitutional isomer chemical spaces has been a subject of cheminformatics since the early 1960s, with applications in structure elucidation and elsewhere. In order to perform such a generation efficiently, exhaustively and isomorphism-free, the structure generator needs to ensure the building of…
60relevance
11
CchemRxiv8 Aug 2023
Sample Efficient Reinforcement Learning with Active Learning for Molecular Design
Michael Dodds, Jeff Guo, Thomas Löhr, Alessandro Tibo + 2 more
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual…
58relevance
12
CchemRxiv25 Nov 2025
Predicting MEA Catalyst Layer Performance at 120 °C Using a Pressurized GDE Testing Platform and Machine Learning
Authors not listed
The performance of Pt-based oxygen reduction reaction (ORR) catalysts in proton exchange membrane fuel cells (PEMFCs) arises from complex interactions among Pt nanoparticles, carbon supports, and proton-conductive ionomer. Membrane electrode assembly (MEA) testing is the industrial benchmark for catalyst evaluation but…
55relevance

Results for algorithm performance testing

Machine Learning for High Energy Density Hydrocarbons for Sustainable Aviation Fuel

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

Path Toward High-Throughput Synthesis Planning via Performance Benchmarking

Enhancing Monte Carlo Tree Search for Retrosynthesis

Multi-Variable Multi-Metric Optimisation of Self-Assembled Photocatalytic CO2 Reduction Performance using Machine Learning Algorithms

Generalised Fault Diagnostics of Polymer Electrolyte Fuel Cells Using Machine Learning

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

MAYGEN - an Open-Source Chemical Structure Generator for Constitutional Isomers Based on the Orderly Generation Principle

Graph-Based Molecular Pareto Optimisation

MAYGEN - an Open-Source Chemical Structure Generator for Constitutional Isomers Based on the Orderly Generation Principle

Sample Efficient Reinforcement Learning with Active Learning for Molecular Design

Predicting MEA Catalyst Layer Performance at 120 °C Using a Pressurized GDE Testing Platform and Machine Learning

Machine Learning for High Energy Density Hydrocarbons for Sustainable Aviation Fuel

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

Path Toward High-Throughput Synthesis Planning via Performance Benchmarking

Enhancing Monte Carlo Tree Search for Retrosynthesis

Multi-Variable Multi-Metric Optimisation of Self-Assembled Photocatalytic CO2 Reduction Performance using Machine Learning Algorithms

Generalised Fault Diagnostics of Polymer Electrolyte Fuel Cells Using Machine Learning

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

MAYGEN - an Open-Source Chemical Structure Generator for Constitutional Isomers Based on the Orderly Generation Principle

Graph-Based Molecular Pareto Optimisation

MAYGEN - an Open-Source Chemical Structure Generator for Constitutional Isomers Based on the Orderly Generation Principle

Sample Efficient Reinforcement Learning with Active Learning for Molecular Design

Predicting MEA Catalyst Layer Performance at 120 °C Using a Pressurized GDE Testing Platform and Machine Learning