Elsa A. Olivetti

  • Atlantic Richfield Associate Professor of Energy Studies
  • BS, Engineering Science, University of Virginia, 2000
  • PhD, Materials Science and Engineering, MIT, 2007

Energy; Manufacturing; Materials Processing; Materials Systems and Analysis

Elsa A. Olivetti

Research

Professor Olivetti’s research focuses on improving the environmental and economic sustainability of materials in the context of rapid-expanding global demand. Her research addresses two major problems where solutions could yield significant environmental benefits:

  1. Improving the sustainability of materials through increased use of recycled and renewable materials, recycling-friendly material design, and intelligent waste disposition
  2. Understanding the implications of substitution, dematerialization and waste mining on materials markets.  Her research spans three levels of materials production: operational-level, industrial network-level and market-level strategies.

Recent News

Game-changing materials

April 9, 2019

Hear Professors Fikile Brushett, Elsa Olivetti, and Yogi Surendranath discuss building the next generation of materials for future energy systems.   more

Elsa Olivetti Uses AI to Identify New Materials Fabrication

June 13, 2018

MIT researchers and their collaborators have demonstrated a novel system using artificial-intelligence techniques to help identify methods of fabricating materials, especially those that look promising in computer simulations. In one test, the system scanned half a million journal articles,…   more

DMSE will join NEET in 2018

April 21, 2018

The beginning of academic year 2017 marked the start of a new program called the New Engineering Education Transformation (NEET) which provides interdepartmental courses for students interested in combining disciplines that may not have formalized crossovers available in the curriculum. 
…   more

More news about Elsa A. Olivetti >>

Publications

2019

D. Schwalbe-Koda, Jensen, Z., Olivetti, E., and Gomez-Bombarelli, R., “Graph similarity drives zeolite diffusionless transformations and intergrowth”, Nature Materials. Springer Science and Business Media LLC, 2019.
D. Raabe, Tasan, C. C., and Olivetti, E., “Strategies for improving the sustainability of structural metals”, Nature, vol. 575, no. 7781. Springer Science and Business Media LLC, pp. 64-74, 2019.

2018

P. Chaunsali et al., “Mineralogical and microstructural characterization of biomass ash binder”, Cement and Concrete Composites, vol. 89. pp. 41-51, 2018.
X. Fu, Polli, A., and Olivetti, E., “High-Resolution Insight into Materials Criticality: Quantifying Risk for By-Product Metals from Primary Production: Quantifying Risk for By-Product Metals”, Journal of Industrial Ecology. 2018.
P. Tecchio, Gregory, J., Olivetti, E., Ghattas, R., and Kirchain, R., “Streamlining the Life Cycle Assessment of Buildings by Structured Under-Specification and Probabilistic Triage: Probabilistic Triage: LCA of Buildings”, Journal of Industrial Ecology. 2018.

2017

E. Kim et al., “Machine-learned and codified synthesis parameters of oxide materials”, Scientific Data, vol. 4. p. 170127, 2017.
E. Kim, Huang, K., Saunders, A., McCallum, A., Ceder, G., and Olivetti, E., “Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning”, Chemistry of Materials, vol. 29. pp. 9436-9444, 2017.
T. F. Malloy et al., “Advancing Alternative Analysis: Integration of Decision Science”, Environmental Health Perspectives, vol. 125. p. 066001, 2017.
E. Helminen, Olivetti, E., Okrasinski, T., and Marcanti, L., “Environmental impact of high density interconnect printed boards as a function of design parameters”, in 2017 IMAPS Nordic Conference on Microelectronics Packaging (NordPac), Gothenburg, Sweden, 2017, pp. 120-124.
X. Fu, Ueland, S. M., and Olivetti, E., “Econometric modeling of recycled copper supply”, Resources, Conservation and Recycling, vol. 122. pp. 219-226, 2017.
E. Kim, Huang, K., Jegelka, S., and Olivetti, E., “Virtual screening of inorganic materials synthesis parameters with deep learning”, npj Computational Materials, vol. 3. 2017.