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researcher

Michael Greenwood

3 papers hereh-index 3228 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cond-mat.mtrl-sci3

identity via Semantic Scholar / OpenAlex

activity
20222024
most citedA critical examination of robustness and generalizability of machine learning prediction of materials properties

137 citations · 272 across the 3 of their papers we have counts for

collaborators

3 papers

cond-mat.mtrl-sci2024★ 23 cited

Efficient first principles based modeling via machine learning: from simple representations to high entropy materials

Kangming Li, Kamal Choudhary, Brian DeCost +2

High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing densit…

cond-mat.mtrl-sci2023★ 112 cited

On the redundancy in large material datasets: efficient and robust learning with less data

Kangming Li, Daniel Persaud, Kamal Choudhary +3

Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multip…

cond-mat.mtrl-sci2022★ 137 cited

A critical examination of robustness and generalizability of machine learning prediction of materials properties

Kangming Li, Brian DeCost, Kamal Choudhary +2

Recent advances in machine learning (ML) methods have led to substantial improvement in materials property prediction against community benchmarks, but an excellent benchmark score…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.