About

Jan Schroers is a professor affiliated with the Schroers Lab at Yale University, specializing in the field of materials science. He holds a B.S. and M.S. in Physics from the University of Cologne, a Ph.D. in Physics from RWTH Aachen, and completed a postdoctoral fellowship in Materials Science at the California Institute of Technology. His academic background reflects a strong foundation in physics and materials science, which underpins his research activities at Yale. The Schroers Lab focuses on advanced materials research, including metallic glasses and other novel materials, although specific details about Professor Schroers' individual research interests and contributions are not explicitly detailed in the provided text.

Research topics

• Materials science
• Physics
• Composite material
• Chemistry
• Computer Science
• Thermodynamics
• Metallurgy
• Artificial Intelligence
• Chemical physics
• Nanotechnology
• Machine Learning
• Mechanics
• Crystallography
• Computational chemistry
• Optics
• Chemical engineering
• Biological system
• Statistical physics
• Process engineering

Selected publications

• Size dependent phase selection during thermomechanical nanomolding

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-19

  datasetOpen access
  PublisherDOI

• Computational study of density fluctuation-facilitated shear band formation in bulk metallic glasses

  npj Computational Materials · 2026-03-06

  articleOpen access

  Seemingly identical Bulk Metallic Glasses (BMG) often exhibit strikingly different mechanical properties despite having the same composition and fictive temperature. A postulated mechanism underlying these differences is the presence of “defects” and density variations. Motivated by this perspective, we introduce physically realistic and quantitatively controllable density fluctuations in molecular dynamics simulations to systematically examine their role in shear band formation under applied stress. We find that the critical shear strain is strongly dependent on the magnitude and size of the fluctuations, revealing a nonlinear activation behavior associated with localized rejuvenation. This finding also elucidates why, historically, critical shear stresses obtained in simulations have differed so much from those found experimentally, as typical simulations setups might favor unrealistically uniform geometries.

  PublisherOA PDFDOI

• Size dependent phase selection during thermomechanical nanomolding

  Open MIND · 2026-02-19 · 1 citations

  dataset
  DOI

• Glass Formation During Combinatorial Sputtering in Binary Alloys

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Soliquidy: a descriptor for atomic geometrical confusion

  npj Computational Materials · 2025-02-19 · 3 citations

  articleOpen access

  Tailoring material properties often requires understanding the solidification process. Herein, we introduce the geometric descriptor Soliquidy, which numerically captures the Euclidean transport cost between the translationally disordered versus ordered states of a materials. As a testbed, we apply Soliquidy to the classification of glass-forming metal alloys. By extending and combining an experimental library of metallic thin films (glass/no-glass) with the aflow.org computational database (geometrical and energetic information of mixtures) we found that the combination of Soliquity and formation enthalpies generates an effective classifier for glass formation. Such a classifier is then used to tackle a public dataset of metallic glasses showing that the glass-agnostic assumptions of Soliquity can be useful for understanding kinetically-controlled phase transitions.

  PublisherOA PDFDOI

• Soliquidy: a descriptor for atomic geometrical confusion

  ArXiv.org · 2025-01-29

  preprintOpen access

  Tailoring material properties often requires understanding the solidification process. Herein, we introduce the geometric descriptor Soliquidy, which numerically captures the Euclidean transport cost between the translationally disordered versus ordered states of a materials. As a testbed, we apply Soliquidy to the classification of glass-forming metal alloys. By extending and combining an experimental library of metallic thin-films (glass/no-glass) with the aflow.org computational database (geometrical and energetic information of mixtures) we found that the combination of Soliquity and formation enthalpies generates an effective classifier for glass formation. Such classifier is then used to tackle a public dataset of metallic glasses showing that the glass-agnostic assumptions of Soliquity can be useful for understanding kinetically-controlled phase transitions.

  PublisherOA PDFDOI

• Local Deformation Mapping Reveals Diffusion through Microstructures

  Research Square · 2025-09-16 · 1 citations

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Glass formation during combinatorial sputtering in binary alloys

  Acta Materialia · 2025-06-12 · 3 citations

  articleSenior authorCorresponding
  PublisherDOI

• High Resolution Mapping of Diffusion Characteristics in General Microstructures

  Diffusion fundamentals. · 2025-11-03

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Can Machine Learning Predict the Liquidus Temperature of Binary Alloys?

  Acta Materialia · 2025-01-01

  articleOpen accessSenior author

  Accurate prediction of the liquidus temperature ( ) of alloys remains a challenge despite numerous theoretical models. Here, we explore and analyze the degree to which machine learning, ML, strategies can be used to predict . We use established literature data on liquidus temperatures of 85523 binary alloys to train ML models using various feature vectors to represent the alloys. While our results are comparable to previous studies, the persistent ~8% error underscores the limitations of current ML models for practical usage. The suboptimal accuracy leads us to question how well-defined the problem is and to what degree fundamental limitations prevent us from attaining more accurate predictions. We identify two major challenges in predicting the liquidus temperature of binary alloys through supervised ML algorithms. One challenge is representing the relevant characteristics of an alloy that determines liquidus temperature through appropriate features. The other fundamental challenge is the discreteness of atoms properties. The difference between two elements and thereby alloy systems is significant, which makes it difficult to learn from one alloy system to predict properties of another. We argue that these problems can be reduced to some extent, however these challenges are common in complex materials science problems and constitute a fundamental challenge in applying supervised ML strategies in this context.

  PublisherDOI

Recent grants

• GOALI: Miniature Net-Shape Fabrication Method Using Thermoplastic Forming with Bulk Mettalic Glass

  NSF · $367k · 2008–2013

• Nanoimprinting with Amorphous Metals

  NSF · $363k · 2009–2013

• DMREF/GOALI/Collaborative Research: High-Throughput Simulations and Experiments to Develop Metallic Glasses

  NSF · $400k · 2014–2017

• Combinatorial exploration of stability regions of high component single-phase solid solutions with near-equiatomic composition

  NSF · $448k · 2016–2019

• Single Crystal Metal Nanorods by Thermomechanical Nanomolding

  NSF · $488k · 2019–2022

Frequent coauthors

• Corey S. O’Hern

  Yale University

  68 shared

• Ze Liu

  Guangzhou Medical University

  57 shared

• William L. Johnson

  University of New Mexico

  57 shared

• Mark D. Shattuck

  55 shared

• Yanhui Liu

  Peking University

  54 shared

• André D. Taylor

  New York University

  53 shared

• Sungwoo Sohn

  50 shared

• Yanhui Liu

  Institute of Physics

  45 shared

Labs

• Schroers LabPI