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Found 10 papers, 5 papers with code
Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
To overcome this limitation, we present a benchmark that automatically quantifies the performance of MLPs in MD simulations of a liquid-liquid phase transition in hydrogen under pressure, a challenging benchmark system.
Code Generation for Machine Learning using Model-Driven Engineering and SysML
Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems.
Heat flux for semi-local machine-learning potentials
The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials.
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations.
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning.
Unified Representation of Molecules and Crystals for Machine Learning
Accurate simulations of atomistic systems from first principles are limited by computational cost.
Chemical Physics Materials Science
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Accurate approximations to density functionals have recently been obtained via machine learning (ML).
Understanding Machine-learned Density Functionals
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density.
Orbital-free Bond Breaking via Machine Learning
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.
Learning Invariant Representations of Molecules for Atomization Energy Prediction
The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design.
