Engineering topics

Montoya, Joseph

Publications and source records attributed to Montoya, Joseph.

3 records

DOE OSTI2021

Autonomous experimentation systems for materials development: A community perspective

Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2020

Nutrient Addition Effects on Phytoplankton Communities in the Amazon River Plume

The types and abundance of phytoplankton is largely controlled by availability of sunlight and bioavailable nutrients. Phytoplankton require essential nutrients including nitrate, phosphate, and silicate to grow, so understanding the role of these macronutrients in limiting the growth phytoplankton communitiesâ€”and the way this may differ depending on community compositionâ€”is key to understanding the controls on phytoplankton biomass and community structure. We aimed to explore how the availability of these nutrients affects the health and composition of phytoplankton communities by conducting a series of nutrient amendment experiments (NAEs) with samples from the Western Tropical North Atlantic, which is heavily influenced by the nutrient-rich, low salinity waters of the Amazon River Plume. These experiments, conducted at five locations in and around the plume, provide greater resolution and further our understanding about the ways nutrients affect communities in dynamic coastal regions.

Klotz, Vivian↗