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Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

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Journal Article - Open Access

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Massachusetts Institute of Technology Cambridge United States

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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.

Subject Categories:

  • Nuclear Physics and Elementary Particle Physics
  • Inorganic Chemistry
  • Cybernetics

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