Learning formation energy of inorganic compounds using matrix variate deep Gaussian process

12/22/2018
by   Saket Mishra, et al.
0

Future advancement of engineering applications is dependent on design of novel materials with desired properties. Enormous size of known chemical space necessitates use of automated high throughput screening to search the desired material. The high throughput screening uses quantum chemistry calculations to predict material properties, however, computational complexity of these calculations often imposes prohibitively high cost on the search for desired material. This critical bottleneck is resolved by using deep machine learning to emulate the quantum computations. However, the deep learning algorithms require a large training dataset to ensure an acceptable generalization, which is often unavailable a-priory. In this paper, we propose a deep Gaussian process based approach to develop an emulator for quantum calculations. We further propose a novel molecular descriptor that enables implementation of the proposed approach. As demonstrated in this paper, the proposed approach can be implemented using a small dataset. We demonstrate efficacy of our approach for prediction of formation energy of inorganic molecules.

READ FULL TEXT
research
01/28/2022

Experiences with managing data parallel computational workflows for High-throughput Fragment Molecular Orbital (FMO) Calculations

Fragment Molecular Orbital (FMO) calculations provide a framework to spe...
research
02/14/2022

Machine Learning-Aided Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries

Li-Ion Solid-State Electrolytes (Li-SSEs) are a promising solution that ...
research
07/20/2017

Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties

Understanding the relationship between the structure of light-harvesting...
research
08/12/2022

High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach

High-throughput DFT calculations are key to screening existing/novel mat...
research
08/29/2022

Machine Learning guided high-throughput search of non-oxide garnets

Garnets, known since the early stages of human civilization, have found ...
research
06/01/2023

Microstructure quality control of steels using deep learning

In quality control, microstructures are investigated rigorously to ensur...
research
07/07/2023

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

Advanced computational methods are being actively sought for addressing ...

Please sign up or login with your details

Forgot password? Click here to reset