About

Eva Y. Andrei is a Distinguished Professor and Board of Governors Professor of Physics at Rutgers University, specializing in condensed matter physics. Her research focuses on understanding the collective behavior of many-particle systems, particularly in condensed matter, where the correlated motion of large numbers of particles leads to emergent phenomena. Her experimental work explores systems of reduced dimensionality at low temperatures and high magnetic fields, aiming to uncover new phases of matter and dramatic behavioral changes. She employs techniques such as scanning tunneling microscopy, spectroscopy, and transport methods to probe these properties and develop potential device applications. Professor Andrei has made significant contributions to the field, including discovering that graphene, a two-dimensional sheet of carbon atoms, has a power factor twice as high as previously known thermoelectric coolers. Her group, along with international collaborators, has also discovered ways to control and guide electrons in graphene by removing a single carbon atom from its lattice. Her work has been recognized through numerous awards and honors, including the APS Mildred Dresselhaus Prize, Moore Foundation EPiQS Award, and election to the National Academy of Sciences and the American Academy of Arts and Sciences. She has been featured in various scientific publications and highlighted for her groundbreaking discoveries in condensed matter physics.

Research topics

• Condensed matter physics
• Nanotechnology
• Physics
• Optoelectronics
• Materials science
• Quantum mechanics
• Optics
• Electrical engineering
• Crystallography
• Geometry

Selected publications

• Protonic nickelate device networks for spatiotemporal neuromorphic computing

  Nature Nanotechnology · 2026-03-09 · 2 citations

  articleOpen access

  Abstract Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. Here we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO 3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO 3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanosecond-scale operation with an energy cost of ~0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware.

  PublisherOA PDFDOI

• Fabrication of A Dual Gated Mirror Symmetric Twisted Trilayer Graphene Device to Study Superconductivity

  ArXiv.org · 2025-11-20

  preprintOpen accessSenior author

  Though research on graphene by itself has waned, the interest in moire materials, materials made with stacked layers of graphene with a rotational twist between the layers, has exploded in popularity. These layered devices show a key feature, flat bands. Flat bands localize electrons, which in turn leads to the expression of correlated states such as Mott insulators, superconductivity, and more. A key property of these devices is that their 2D nature allows us to tune them in situ, effectively allowing us to change the device's electronic properties. This powerful ability greatly reduces the time and money required to study superconductivity. The superconductivity in these systems seems to be similar to high-temperature superconductors such as cuprates, giving us a path towards studying high-temperature superconductivity. The fabrication of these devices is nontrivial, and thus we detail one general way to create these layered devices to give maximal tunability.

  PublisherOA PDFDOI

• Machine learning assisted high throughput prediction of moiré materials

  arXiv (Cornell University) · 2025-12-18

  preprintOpen access

  The world of 2D materials is rapidly expanding with new discoveries of stackable and twistable layered systems composed of lattices of different symmetries, orbital character, and structural motifs. Often, however, it is not clear a priori whether a pair of monolayers twisted at a small angle will exhibit correlated or interaction-driven phenomena. The computational cost to make accurate predictions of the single particle states is significant, as small twists require very large unit cells, easily encompassing 10,000 atoms, and therefore implementing a high throughput prediction has been out of reach. Here we show a path to overcome this challenge by introducing a machine learning (ML) based methodology that efficiently estimates the twisted interlayer tunneling at arbitrarily low twist angles through the local-configuration based approach that enables interpolating the local stacking for a range of twist angles using a random forest regression algorithm. We leverage the kernel polynomial method to compute the density of states (DOS) on large real space graphs by reconstructing a lattice model of the twisted bilayer with the ML fitted hoppings. For twisted bilayer graphene (TBG), we show the ability of the method to resolve the magic angle DOS at a substantial improvement in computational time. We use this new technique to scan through the database of stable 2D monolayers (MC2D) and reveal new twistable candidates across the five possible points groups in two-dimensions with a large DOS near the Fermi energy, with potentially exciting interacting physics to be probed in future experiments.

  PublisherDOI

• Machine learning assisted high throughput prediction of moiré materials

  ArXiv.org · 2025-12-18

  articleOpen access

  The world of 2D materials is rapidly expanding with new discoveries of stackable and twistable layered systems composed of lattices of different symmetries, orbital character, and structural motifs. Often, however, it is not clear a priori whether a pair of monolayers twisted at a small angle will exhibit correlated or interaction-driven phenomena. The computational cost to make accurate predictions of the single particle states is significant, as small twists require very large unit cells, easily encompassing 10,000 atoms, and therefore implementing a high throughput prediction has been out of reach. Here we show a path to overcome this challenge by introducing a machine learning (ML) based methodology that efficiently estimates the twisted interlayer tunneling at arbitrarily low twist angles through the local-configuration based approach that enables interpolating the local stacking for a range of twist angles using a random forest regression algorithm. We leverage the kernel polynomial method to compute the density of states (DOS) on large real space graphs by reconstructing a lattice model of the twisted bilayer with the ML fitted hoppings. For twisted bilayer graphene (TBG), we show the ability of the method to resolve the magic angle DOS at a substantial improvement in computational time. We use this new technique to scan through the database of stable 2D monolayers (MC2D) and reveal new twistable candidates across the five possible points groups in two-dimensions with a large DOS near the Fermi energy, with potentially exciting interacting physics to be probed in future experiments.

  PublisherOA PDF

• Flexible internal transporter with gravity-assisted mechanism for vertical transfer of microscope head

  Review of Scientific Instruments · 2025-05-01

  articleSenior author

  We present a flexible mechanism for vertically transferring scanning probe microscope (SPM) heads in ultra-high vacuum. The microscope head is transferred from a room temperature site down to an SPM pluggable connector at low temperatures within the bore of a superconducting magnet located in a top-loading cryostat. Insertion and extraction of the microscope head to and from the connector are accomplished with gravity-assisted tools. Contrasted with typical rigid designs, the low-profile mechanism described here has no external mounting structure, which reduces sensitivity to mechanical disturbances, decreases vacuum volume, and enables installation in standard laboratory spaces.

  PublisherDOI

• Protonic Nickelate Device Networks for Spatiotemporal Neuromorphic Computing

  arXiv (Cornell University) · 2025-12-27

  preprintOpen access

  Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. In this work, we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanoseconds scale operation with an energy cost of 0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken-digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware.

  PublisherDOI

• Quantum criticality and tunable Griffiths phase in superconducting twisted trilayer graphene

  ArXiv.org · 2025-07-14

  preprintOpen accessSenior author

  When dimensionality is reduced, enhanced quantum fluctuations can destroy long-range phase coherence, driving a superconductor insulator transition, SIT, where disorder and electronic correlations give rise to novel many-body states. Here, we report the first observation of a magnetic field tuned SIT in mirrorsymmetric twisted trilayer graphene, TTG. Remarkably, signatures of quantum criticality persist over an exceptionally broad range of magnetic fields and are well described by the formation of a quantum Griffiths phase, a regime in which rare spatially extended regions develop local order within a globally disordered phase. This leads to a quantum phase transition governed by an infinite-randomness fixed point and characterized by ultraslow relaxation dynamics. Near the quantum critical region, transport measurements reveal strongly nonlinear electrical behavior, including a current-driven reentrant transition from insulating to superconducting transport, providing direct evidence of local superconducting order. By tilting the magnetic field, we are able to collapse the broad Griffiths regime into a single quantum critical point, QCP, demonstrating a striking level of control over disorder induced quantum dynamics. Our results further show that TTG strongly violates the Pauli limit and establishes twisted trilayer graphene as a tunable platform for exploring quantum phase fluctuations, Cooper pair localization, and unconventional superconductivity.

  PublisherOA PDFDOI

• Protonic Nickelate Device Networks for Spatiotemporal Neuromorphic Computing

  ArXiv.org · 2025-12-27

  articleOpen access

  Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. In this work, we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanoseconds scale operation with an energy cost of 0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken-digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware.

  PublisherOA PDF

• Moiré periodic and quasiperiodic crystals in heterostructures of twisted bilayer graphene on hexagonal boron nitride

  Nature Materials · 2025-05-06 · 15 citations

  articleSenior author
  PublisherDOI

• Quantum criticality and tunable Griffiths phase in superconducting twisted trilayer graphene

  Research Square · 2025-08-05

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

Recent grants

• Experiments on Time-Resolved Transport and Imaging of Moving Vortex Matter

  NSF · $380k · 2005–2009

• Experimental Studies of Graphene Layers

  NSF · $410k · 2009–2014

• Strain Engineering of Band Structure and Electronic Properties in Two Dimensional Materials.

  NSF · $440k · 2017–2020

• Electronic Properties of Two Dimensional Electron Systems: Exploring the Role of Dimensionality Boundaries and Interfaces.

  NSF · $553k · 2012–2017

• MRI: Development of an Ultra-High Vacuum Cryogen-free Low Temperature Proximal Probe System for the Exploration of Low Dimensional Materials and Nano-devices

  NSF · $703k · 2013–2019

Frequent coauthors

• Guohong Li

  Rutgers, The State University of New Jersey

  85 shared

• Jinhai Mao

  University of Chinese Academy of Sciences

  38 shared

• Yuhang Jiang

  University of Chinese Academy of Sciences

  33 shared

• Kenji Watanabe

  National Institute for Materials Science

  31 shared

• Takashi Taniguchi

  30 shared

• Xinyuan Lai

  25 shared

• Nikhil Tilak

  Rutgers, The State University of New Jersey

  22 shared

• Michael Altvater

  21 shared

Awards & honors

• Rutgers 2012 PhD, is a new 2025 Gordon and Betty Moore Found…
• Rutgers Innovation Awards
• Rutgers Researchers discover "intercrystals"
• Larry Zamick recognized as a 2025 APS Outstanding Referee
• Eva Andrei receives APS's 2023 Mildred Dresselhaus Prize