AIMC Topic: Thermodynamics

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A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches.

Bioresource technology
Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrial-scale thermochemical conversion pl...

Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers.

Proteins
Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely difficult experimentally and computationally. Yet, this knowledge is extremely important as small oligomers are the most toxic species. Experiments and s...

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

Annual review of chemical and biomolecular engineering
Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, r...

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties.

Physical chemistry chemical physics : PCCP
The prediction of the free energy (Δ) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its ce...

Machine learned coarse-grained protein force-fields: Are we there yet?

Current opinion in structural biology
The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning...

Machine Learning to Predict Homolytic Dissociation Energies of C-H Bonds: Calibration of DFT-based Models with Experimental Data.

Molecular informatics
Random Forest (RF) QSPR models were developed with a data set of homolytic bond dissociation energies (BDE) previously calculated by B3LYP/6-311++G(d,p)//DFTB for 2263 sp3C-H covalent bonds. The best set of attributes consisted in 114 descriptors of ...

Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions.

The journal of physical chemistry letters
We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The model is able to predict chem...

Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures.

Journal of chemical information and modeling
The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein str...

On Sampling Minimum Energy Path.

Journal of chemical theory and computation
Sampling the minimum energy path (MEP) between two minima of a system is often hindered by the presence of an energy barrier separating the two metastable states. As a consequence, direct sampling based on molecular dynamics or Markov Chain Monte Car...

gr Predictor: A Deep Learning Model for Predicting the Hydration Structures around Proteins.

Journal of chemical information and modeling
Among the factors affecting biological processes such as protein folding and ligand binding, hydration, which is represented by a three-dimensional water site distribution function around the protein, is crucial. The typical methods for computing the...