AIMC Topic: Thermodynamics

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Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn.

Marine pollution bulletin Apr 17, 2024
A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the ...
Thermogravimetry Thermodynamics Support Vector Machine Pyrolysis Machine Learning Biomass Neural Networks, Computer
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Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning.

Journal of chemical information and modeling Apr 16, 2024
Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical m...
Nanostructures Thermodynamics Amino Acids Molecular Dynamics Simulation Neural Networks, Computer Machine Learning Algorithms Adsorption
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ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Journal of chemical information and modeling Apr 8, 2024
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providi...
Algorithms Protein Binding Support Vector Machine Neural Networks, Computer Machine Learning DNA-Binding Proteins Thermodynamics Proteins DNA
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Modeling Zinc Complexes Using Neural Networks.

Journal of chemical information and modeling Apr 8, 2024
Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build...
Thermodynamics Molecular Conformation Coordination Complexes Neural Networks, Computer Zinc Models, Molecular Molecular Dynamics Simulation Quantum Theory
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One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates.

The journal of physical chemistry. B Apr 3, 2024
Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sa...
Molecular Dynamics Simulation Thermodynamics Protein Folding Peptides Discriminant Analysis Machine Learning
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Calculation of solvation force in molecular dynamics simulation by deep-learning method.

Biophysical journal Mar 4, 2024
Electrostatic calculations are generally used in studying the thermodynamics and kinetics of biomolecules in solvent. Generally, this is performed by solving the Poisson-Boltzmann equation on a large grid system, a process known to be time consuming....
Solvents Thermodynamics Molecular Dynamics Simulation Solubility Deep Learning
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Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction.

Journal of chemical theory and computation Feb 23, 2024
Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may ...
Machine Learning Kinetics Humans Molecular Dynamics Simulation Thermodynamics Markov Chains Myeloid Cell Leukemia Sequence 1 Protein
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Poisson-Boltzmann-based machine learning model for electrostatic analysis.

Biophysical journal Feb 15, 2024
Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation ...
Static Electricity Thermodynamics Machine Learning Poisson Distribution Molecular Dynamics Simulation
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Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

Molecules (Basel, Switzerland) Feb 13, 2024
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sa...
Physics Protein Binding Ligands Proteins Cost-Benefit Analysis Machine Learning Binding Sites Retrospective Studies Thermodynamics
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Artificial neural network-assisted thermogravimetric analysis of thermal degradation in combustion reactions: A study across diverse organic samples.

Environmental research Feb 10, 2024
During gasification the kinetic and thermodynamic parameter depend on both the feedstock and the process conditions. As a result, one needs to enhance the understanding of how to model numerically these parameters using thermogravimetric analyzer. Co...
Neural Networks, Computer Models, Chemical Kinetics Thermogravimetry Thermodynamics
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