Search
Results: 107
Developing Ethical Future Managers Embracing Sustainability
The paper describes and evaluates how a higher education institution (Budapest Business School - BBS), aspiring for AACSB accreditation, co-created innovative tools, based on the UN's Sustainable Development Goals and the...
Published by: IGI Global Scientific Publishing
A machine learning based intramolecular potential for a flexible organic molecule.
Quantum mechanical predictive modelling in chemistry and biology is often hindered by the long time scales and large system sizes required of the computational model. Here, we employ the kernel regression machine learning...
Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression.
Practical free energy reconstruction algorithms involve three separate tasks: biasing, measuring some observable, and finally reconstructing the free energy surface from those measurements. In more than one dimension, adaptive...
Atomic permutationally invariant polynomials for fitting molecular force fields
Abstract: We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent...
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials.
We introduce a computational framework that is able to describe general many-body coarse-grained (CG) interactions of molecules and use it to model the free energy surface of molecular liquids as a cluster expansion in terms of...
Data-efficient machine learning for molecular crystal structure prediction.
The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by...
Published by: Chemical science
Preconditioners for the geometry optimisation and saddle point search of molecular systems.
A class of preconditioners is introduced to enhance geometry optimisation and transition state search of molecular systems. We start from the Hessian of molecular mechanical terms, decompose it and retain only its positive...
Improvement of molecular-replacement models with Sculptor.
In molecular replacement, the quality of models can be improved by transferring information contained in sequence alignment to the template structure. A family of algorithms has been developed that make use of the...
Data-efficient machine learning for molecular crystal structure prediction.
The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by...
Nested Transition Path Sampling.
We introduce a novel transition path (TPS) sampling scheme employing nested sampling. Analogous to how nested sampling explores the entire configurational phase space for atomistic systems, nested TPS samples the entire...
Nested sampling for materials
Abstract: We review the materials science applications of the nested sampling (NS) method, which was originally conceived for calculating the evidence in Bayesian inference. We describe how NS can be adapted to sample the...
De novo exploration and self-guided learning of potential-energy surfaces
Abstract: Interatomic potential models based on machine learning (ML) are rapidly developing as tools for material simulations. However, because of their flexibility, they require large fitting databases that are normally...
Atomic permutationally invariant polynomials for fitting molecular force fields
Abstract: We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent...
Predicting polarizabilities of silicon clusters using local chemical environments
Abstract: Calculating polarizabilities of large clusters with first-principles techniques is challenging because of the unfavorable scaling of computational cost with cluster size. To address this challenge, we demonstrate that...
Machine learning in chemical reaction space
Abstract: Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately...
Atomic permutationally invariant polynomials for fitting molecular force fields
Abstract: We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent...
|
|< |
< |
1 |