AIMC Topic: Thermodynamics

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Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling Nov 5, 2019
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...
Density Functional Theory Models, Molecular Gold Neural Networks, Computer Computer Simulation Crystallization Alloys Thermodynamics Models, Chemical Titanium Surface Properties
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Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

Biomolecules Oct 14, 2019
The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, se...
Supervised Machine Learning Proteins Protein Conformation Databases, Protein Thermodynamics
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Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields.

Journal of chemical information and modeling Sep 27, 2019
We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. ...
Machine Learning Molecular Dynamics Simulation Thermodynamics Chemical Engineering Algorithms Bayes Theorem
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Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

Journal of chemical information and modeling Aug 22, 2019
The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of comp...
Drug Discovery Models, Molecular Humans Protein Kinase Inhibitors Thermodynamics Machine Learning Small Molecule Libraries Drug Design Cyclin-Dependent Kinase 2
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Energy-Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach.

ACS combinatorial science Aug 21, 2019
There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. He...
Molecular Structure Models, Chemical Quantum Theory Computer Simulation Organic Chemicals Small Molecule Libraries Inorganic Chemicals Ions Databases, Chemical Thermodynamics Machine Learning
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Artificial Intelligence Approach To Investigate the Longevity Drug.

The journal of physical chemistry letters Aug 14, 2019
Longevity is a very important and interesting topic, and has been demonstrated to be related to longevity. We combined network pharmacology, machine learning, deep learning, and molecular dynamics (MD) simulation to investigate potent lead drugs. Re...
Receptor, IGF Type 1 Antigens, CD Molecular Docking Simulation Ligands Artificial Intelligence Inhibitory Concentration 50 Drug Discovery Thermodynamics Medicine, Chinese Traditional Algorithms Binding Sites Protein Interaction Maps Receptor, Insulin Protein Binding Klotho Proteins Databases, Factual Signal Transduction Glucuronidase
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Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation.

The journal of physical chemistry letters Aug 14, 2019
We propose a simple, but efficient and accurate, machine learning (ML) model for developing a high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical embedded atom...
Organic Chemicals Thermodynamics Neural Networks, Computer Machine Learning
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Past-future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics.

Nature communications Aug 8, 2019
The ability to rapidly learn from high-dimensional data to make reliable bets about the future is crucial in many contexts. This could be a fly avoiding predators, or the retina processing gigabytes of data to guide human actions. In this work we dra...
Deep Learning Kinetics Molecular Dynamics Simulation Thermodynamics Forecasting
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.

Nature communications Jul 1, 2019
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy ...
Isomerism Hydrocarbons Thermodynamics Neural Networks, Computer Computer Simulation Machine Learning Benchmarking
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PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges.

Journal of chemical theory and computation May 14, 2019
In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio reference data, these methods allow for accurately predicting the properties of chemical systems,...
Protein Conformation Thermodynamics Proteins Static Electricity Molecular Dynamics Simulation Neural Networks, Computer Quantum Theory Algorithms
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