Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Abstract
Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 05, 2019
- Accession Number
- AD1105730
Entities
People
- Aditya Nandy
- Chenru Duan
- Fang Liu
- Heather J. Kulik
- Jon P. Janet
- Sean Lin
- Tzuhsiung Yang
Organizations
- Massachusetts Institute of Technology