About

Laura H. Lewis is the Distinguished University and Cabot Professor of Chemical Engineering and Professor of Mechanical and Industrial Engineering at Northeastern University. She joined the Chemical Engineering Department in Spring 2007 and has a background that includes being a research group leader and Associate Department Chair in the Nanoscience Department of Brookhaven National Laboratory. Her research focuses on investigating the materials factors at the atomic level that provide functionality to magnetic and electronic materials, emphasizing strategic and technologically relevant systems that impact sustainability and critical materials supply chains. Her work includes the study of nanomaterials, amorphous materials, and magnets, with potential applications in energy transformations, thermal, electromechanical, and biomedical systems such as motors, generators, magnetocaloric cooling, and biosensors. Lewis serves on various advisory panels, including for the Critical Materials Institute and is involved in developing supply chain and sustainability standards for ISO. She holds a B.A. in Physics with a specialization in Earth Sciences from the University of California, San Diego, an M.S. in Electronic Materials from MIT, and a Ph.D. in Materials Science and Engineering from the University of Texas at Austin. Her contributions to the field have been recognized through numerous honors, including fellowships in the American Physical Society and IEEE, and she has been actively involved in advancing magnetics research and applications.

Research topics

• Computer Science
• Metallurgy
• Materials science
• Physics
• Thermodynamics
• Condensed matter physics
• Chemistry
• Quantum mechanics
• Process engineering
• Waste management
• Nuclear magnetic resonance
• Mathematical optimization
• Geometry
• Engineering
• Chromatography
• Algorithm
• Optics
• Crystallography
• Statistical physics
• Environmental science
• Mathematics

Selected publications

• Improved machine learning algorithm for predicting ground state properties

  Nature Communications · 2024 · 35 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Algorithm

  Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only [Formula: see text] data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require [Formula: see text] data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as [Formula: see text] in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.

  DOI

• Interplay between magnetism and short-range order in medium- and high-entropy alloys: CrCoNi, CrFeCoNi, and CrMnFeCoNi

  Physical Review Materials · 2023 · 35 citations

  • Condensed matter physics
  • Materials science
  • Thermodynamics

  The impact of magnetism on predicted atomic short-range order in three medium- and high-entropy alloys is studied using a first-principles all-electron Landau-type linear-response theory, coupled with lattice-based atomistic modeling. We perform two sets of linear-response calculations: One in which the paramagnetic state is modeled within the disordered local moment picture, and one in which systems are modeled in a magnetically ordered state, which is ferrimagnetic for the alloys considered in this paper. We show that the treatment of magnetism can have a significant impact both on the predicted temperature of atomic ordering and the nature of atomic order itself. In CrCoNi, we find that the nature of atomic order changes from being $\mathrm{L}{1}_{2}$-like when modeled in the paramagnetic state to ${\mathrm{MoPt}}_{2}$-like when modeled assuming the system has magnetically ordered. In CrFeCoNi, atomic correlations between Fe and other elements present are dramatically strengthened when we switch from treating the system as magnetically disordered to magnetically ordered. Our results show it is necessary to consider the magnetic state when modeling multicomponent alloys containing mid- to late-$3d$ elements. Furthermore, we suggest that there may be high-entropy alloy compositions containing $3d$ transition metals that will exhibit specific atomic short-range order when thermally treated in an applied magnetic field. This has the potential to provide a route for tuning physical and mechanical properties in this class of materials.

  DOI

• Biofiltration Media Optimization – Phase I Final Report

  2021 · 3 citations

  Senior authorCorresponding
  • Computer Science
  • Process engineering
  • Computer Science
  Publisher

• Estimating the in-operando stabilities of AlFe2B2-Based compounds for magnetic refrigeration

  Journal of Alloys and Compounds · 2020 · 20 citations

  Senior authorCorresponding
  • Materials science
  • Chemistry
  • Thermodynamics
  PublisherOA PDFDOI

• Creating, probing and confirming tetragonality in bulk FeNi alloys

  Acta Materialia · 2020 · 33 citations

  Senior authorCorresponding
  • Materials science
  • Condensed matter physics
  • Crystallography
  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Towards Rare-Earth-Free Advanced Permanent Magnets - High-Anisotropy L10 Materials

  NSF · $155k · 2011–2014

• PFI:AIR - TT: Sustainable Permanent Magnets For Advanced Applications

  NSF · $252k · 2016–2018

• FRG: Magnetic and Optical Properties of Fe-Doped Titania Nanotubes

  NSF · $640k · 2009–2014

• Materials World Network: The Magnetostructural Response in Heterostructured Systems: A US - UK Collaboration

  NSF · $420k · 2009–2014

• CMMI-EPSRC: Multi-Driver Furnace Processing of Magneto-Functional Materials

  NSF · $415k · 2021–2026

Frequent coauthors

• Atsufumi Hirohata

  Conference Board

  369 shared

• Nicoleta Lupu

  National Institute of Research and Development for Technical Physics

  301 shared

• Ron Goldfarb

  Conference Board

  287 shared

• Petru Andrei

  Conference Board

  286 shared

• John Q. Xiao

  282 shared

• Johannes Paulides

  268 shared

• Darío Arena

  University of South Florida

  264 shared

• Mingzhong Wu

  Conference Board

  257 shared

Labs

• Nanomagnetism GroupPI

Education

• Ph.D., Mechanical Engineering / Materials Science

  University of Texas at Austin

• MS, Materials Science

  Massachusetts Institute of Technology

  1988

• BS, Physics

  University of California San Diego

  1985

Awards & honors

• Fulbright Scholar (2018, 2019)
• Fellow, American Physical Society
• Fellow, Institute of Electrical and Electronics Engineers
• Northeastern University Excellence in Research and Creative…
• Chair, Technical Committee of the IEEE Magnetics Society (20…

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• Lisa Feldman Barretth-169
• Herbert Levineh-119
• Eduardo Sontagh-108
• Mansoor Amijih-97