Machine Learning Enabled Inorganic Synthesis Planning and Materials Design
The discovery and design of materials is essential for addressing important societal problems in areas such as energy, biomedicine, and computing technology. Data-driven synthesis planning with machine learning is a key step in the design of novel inorganic compounds with desirable properties. Inorg...
Main Author: | Karpovich, Christopher |
---|---|
Other Authors: | Olivetti, Elsa A. |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2023
|
Online Access: | https://hdl.handle.net/1721.1/151288 https://orcid.org/0000-0001-6691-5578 |
Similar Items
-
Machine learning-guided synthesis of advanced inorganic materials
by: Tang, Bijun, et al.
Published: (2021) -
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
by: Kim, Edward, et al.
Published: (2022) -
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
by: Kim, Edward, et al.
Published: (2021) -
Inorganic materials synthesis and fabrication /
by: Lalena, John N.
Published: (2008) -
Machine learning-driven materials synthesis and design
by: Lu, Yuhao
Published: (2024)