Thomas Bligaard Accelerating High Throughput Simulations Using Machine Learning Methods

(PDF) Using High-throughput Parallel CFD Simulations And Machine Learning To Accelerate Design ...

(PDF) Using High-throughput Parallel CFD Simulations And Machine Learning To Accelerate Design ... Recorded on may 29, 2017.this highlight talk was originally given at the marvel/max/psi k tutorial on “high throughput computations: general methods and appl. Furthermore, i will highlight how such high throughput schemes can be combined with machine learning methods for data mining to extract novel materials designing rules and for identifying new prototypes for further investigation.

Thomas Bligaard

Thomas Bligaard The first talk, on monday afternoon, was given by dr. thomas bligaard (slac national accelerator laboratory / stanford university, usa), with the title “accelerating high throughput simulations using machine learning methods”. Series of four invited highlight talks originally given at the marvel/max/psi k tutorial on "high throughput computations: general methods and applications using aiida" held on may 29 to 31, 2017 at epfl. Materials discovery is increasingly being impelled by machine learning methods that rely on pre existing datasets. where datasets are lacking, unbiased data generation can be achieved with. My research goals are to understand and to design functional materials through the use of atomic scale computer simulations and to establish the tools to do so.

(PDF) Combining Machine Learning And High-Throughput Science

(PDF) Combining Machine Learning And High-Throughput Science Materials discovery is increasingly being impelled by machine learning methods that rely on pre existing datasets. where datasets are lacking, unbiased data generation can be achieved with. My research goals are to understand and to design functional materials through the use of atomic scale computer simulations and to establish the tools to do so. In this work, we show how the explicit modeling of the different character of the bonds in these systems improves the performance of machine learning methods for optimization. In this study, we have developed an innovative paradigm for the design and screening of mmms towards he/ch 4 separation by integrating high throughput computer simulations and machine learning techniques. The methodologies i utilize are primarily machine learning, quantum mechanics, density functional theory, numerical analysis, statistical mechanics, micro kinetic modeling, and rate theory. In the materials virtual lab, we aim to address this “scale challenge” by developing accurate potential models that scale linearly with number of atoms, as well as property prediction models that enable rapid screening across vast chemical spaces using machine learning.

High-throughput Computation And Machine Learning Methods Applied To M…

High-throughput Computation And Machine Learning Methods Applied To M… In this work, we show how the explicit modeling of the different character of the bonds in these systems improves the performance of machine learning methods for optimization. In this study, we have developed an innovative paradigm for the design and screening of mmms towards he/ch 4 separation by integrating high throughput computer simulations and machine learning techniques. The methodologies i utilize are primarily machine learning, quantum mechanics, density functional theory, numerical analysis, statistical mechanics, micro kinetic modeling, and rate theory. In the materials virtual lab, we aim to address this “scale challenge” by developing accurate potential models that scale linearly with number of atoms, as well as property prediction models that enable rapid screening across vast chemical spaces using machine learning.

Thomas Bligaard: Accelerating high-throughput simulations using machine learning methods