Research Output per year

## Fingerprint Fingerprint is based on mining the text of the experts' scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

continuums
Physics & Astronomy

Electronic Structure
Mathematics

Electronic structure
Engineering & Materials Science

Homogenization
Mathematics

Multiscale Methods
Mathematics

Incompressible flow
Engineering & Materials Science

Density functional theory
Engineering & Materials Science

simulation
Physics & Astronomy

##
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.

## Research Output 1900 2019

## Active learning of uniformly accurate interatomic potentials for materials simulation

Zhang, L., Lin, D. Y., Wang, H., Car, R. & E, W., Feb 25 2019, In : Physical Review Materials. 3, 2, 023804.Research output: Contribution to journal › Article

Potential energy surfaces

Molecular modeling

Learning systems

Problem-Based Learning

## Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

Beck, C., E, W. & Jentzen, A., Jan 1 2019, (Accepted/In press) In : Journal of Nonlinear Science.Research output: Contribution to journal › Article

Backward Stochastic Differential Equation

Fully Nonlinear

Approximation algorithms

Nonlinear Partial Differential Equations

Second order differential equation

## On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations

E, W., Hutzenthaler, M., Jentzen, A. & Kruse, T., Jan 1 2019, In : Journal of Scientific Computing.Research output: Contribution to journal › Article

Backward Stochastic Differential Equation

Parabolic Partial Differential Equations

Numerical Approximation

Nonlinear Partial Differential Equations

Nonlinear Differential Equations

## Adaptive coupling of a deep neural network potential to a classical force field

Zhang, L., Wang, H. & E, W., Oct 21 2018, In : Journal of Chemical Physics. 149, 15, 154107.Research output: Contribution to journal › Article

field theory (physics)

Molecular dynamics

molecular dynamics

Dynamic models

dynamic models

8
Citations
(Scopus)

## DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Wang, H., Zhang, L., Han, J. & E, W., Jul 1 2018, In : Computer Physics Communications. 228, p. 178-184 7 p.Research output: Contribution to journal › Article

kits

Potential energy

learning

Molecular dynamics

potential energy