Every summer, the Molecular Foundry supports several summer undergraduate students from the College of Chemistry at UC Berkeley for a paid summer internship. The internship is administered through the Berkeley Lab Undergraduate Research (BLUR) program.
The internship program period spans 10 weeks from early June through early August. As an intern, participants will not only conduct research, but will participate in virtual tours of Berkeley Lab facilities, lunchtime talks with researchers from across the lab, and more. The program culminates with a poster session where the interns present their research.
Continuing research opportunities after this time period may be available.
The 2021 internships will be fully virtual. Participants must be based in the United States.
Summer 2021 Deadline: February 15, 2021
- Applicants must be a United States Citizen or Lawful Permanent Resident at the time of applying, and reside in the United States through the duration of the internship.
- Applicants must have an undergraduate cumulative minimum grade point average (GPA) of at least 3.0 on a 4.0 scale for all completed courses taken as a matriculated student at the applicant’s current (or recently graduated) institution and at any undergraduate institutions attended as a matriculated post-secondary student during the five years preceding the start of the current year.
- Upper division coursework preferred, but not required.
- BLUR interns cannot take classes while participating in the program.
BLUR Program Requirements
- Complete the full 10-week program.
- Complete entrance and exit surveys.
- Submit a complete copy of a research report and poster.
- Submit an abstract of your research in the required format.
- Attend all scheduled events, including lectures, tours, and group activities.
- Complete all Berkeley Lab safety and procedural requirements, if applicable.
- Behave in a responsible and professional manner. Interns must adhere to the Ethics and Conduct at Berkeley Lab Requirements and Policies Manual (RPM).
BLUR program FAQ
To apply for the 2021 Foundry/BLUR internship program, applicants had to submit the online application no later than February 15, 2021. If you have questions about the program or application, please contact us.
The application asks for a 1-page statement of interest, college transcripts, and project preferences, as well as demographic data for administrative purposes.
Opportunities for the 2021 Summer Program
Project listings, as available, will be posted below. Interested students will be welcome and encouraged to reach out to the PIs to discuss their interest in a particular project, or to learn more about a project.
Using disorder to control topology in 2D materials
Principal Investigator: Sinéad Griffin
The Griffin Group is a multidisciplinary theory and computation team that solve the most exciting problems ranging from future technologies to high-energy physics, bridging disparate fields to solve grand challenges. We primarily use first-principles calculations and phenomenological models to describe and design materials for quantum systems (including dark matter detection), and next-generation microelectronics. Our work is often combined with and inspires experimental investigations of candidate materials, which is facilitated by proximity to state-of-the-art materials synthesis and characterization capabilities at LBNL and UC Berkeley.
Can disorder be used to turn on/off topological protection in 2D materials?
Topological insulators are curious materials: they act like insulators in the center of the material but are metallic on their edges. This unusual property makes them particularly interesting for applications like next-generation electron devices and quantum computers. Recently, my group showed that disorder in the atomic positions can be used to cause a normal insulator to become a topological insulator. This project will explore if this same route can be used to control topology in 2D materials using quantum mechanical calculations.
- Carry out computational and theoretical studies on the material
- Compare calculations to existing literature
- Deliver oral, written and poster presentations
- Collaborate and interact with scientists in the group and at LBL
- Write paper with results and conclusions
What can the intern expect to learn?
- How to perform first-principles calculations (Density Functional Theory)
- How to interpret results and understand advantages and drawbacks of methods
- How to plan research and ask good questions
- Scientific communication and presentation skills
- How to learn something new
An evolutionary approach to materials self-assembly
Principal Investigator: Steve Whitelam
An outstanding problem of materials science is to develop predictive, microscopic rules for self-assembly: given a nanoscale building block, such as a protein or small molecule, how will it self-assemble? As time evolves, what phases and structures will it form, and what will be the yield of the ‘target’ structure when (and if) it assembles? My group uses the tools and techniques of statistical mechanics to address these questions.
How do we make a material with specified properties? In pursuit of “synthesis by design” the materials science community has developed algorithms of inverse design and machine learning. These approaches can identify interparticle potentials able to stabilize target structures or promote their self-assembly from solution, and can identify protocols or reaction conditions that optimize the self-assembly of specified particles.
In this project we will explore approaches based on evolutionary learning that design particles and protocols in order to self-assemble materials to order. We will express the interparticle potential and time-dependent assembly protocol as arbitrary functions, encoded by neural networks. This encoding is the instruction code or “genome” for self-assembling a material. Molecular simulations carried out using the particle and protocols specified by the genome results in the “phenome”, a material whose properties can be measured and compared to a design goal. We will use evolutionary learning to produce phenomes whose properties satisfy user-defined goals, both directed or exploratory in nature.
Intern’s role and expected learning goal:
Statistical mechanics & computer simulations (molecular simulations of self-assembly, evolutionary learning, neural networks).
3D models capable of bio-inspired hierarchical self-assembly
Principal Investigator: Ron Zuckermann
We have developed a new class of bio-inspired polymers, called peptoids, that can mimic many properties of peptides and proteins, yet they are more stable, and are easier and cheaper to make. We make peptoids using automated robotic synthesizers, and are able to look at their self-assembly behavior by molecular modeling, optical microscopy, electron microscopy and a variety of spectroscopic methods. We seek to discover new biomimetic structures made from these non-natural polymers by a detailed understanding of their 3D structure.
The intern will assist in the design of 3D-printed molecular models that we will use to understand lattice packing interactions in peptoid crystals, and in the folding of proteins. Create molecular models of biopolymers that exhibit realistic degrees of freedom, and are able to fold and self-assemble into organized structures in ‘at-home’ experiments. By combining molecular modeling, CAD design and 3D-printing, we are able to reproduce some of the real assembly behaviors observed in nature using macroscopic molecular models that assemble in a circulating water tank (see our recent open access Skeletides paper).
We aim to understand the interplay of entropy and enthalpy in oligomer self-assembly. Since these systems involve a huge number of simultaneous weak interactions, they can be very difficult to analyze computationally. Instead, we will build 3D-printed molecular shapes with embedded magnets and flexible joints to directly observe their ensemble assembly behavior. Can we create models that will spontaneously fold into hierarchical assemblies?
The intern will be tasked with designing 3D models that exhibit some of the fundamental properties observed in biological folding, such as cooperativity in the folding of an alpha helix, or hierarchical assembly as seen in beta sheets.
What can the intern expect to learn?
Use techniques involving molecular modeling, computer-assisted design (CAD), 3D printing, model building, and macroscopic self-assembly experiments. There is also the opportunity to learn about electronics and programming microcontrollers to create next-generation ‘smart’ models.
Anti-COVID-19 screen self-cleaning technology
Principal Investigator: Jeff Urban
In our lab, we investigate energy and mass transport at the nanoscale with a special focus on hybrid organic-inorganic materials and interfaces. This marriage of “hard” and “soft” materials presents an interesting contrast of transport modalities, bond strengths, mechanical properties, and the like. If we can apply understanding and design to these materials, we can harness advantages of both polymers and nanocrystals in one material, which presents many exciting opportunities to 1) study fundamental interfacial transport and 2) design novel tailored materials for broad applications.
We will investigate the ability to use our advanced know-how and ideas around UV light-sources and sensor systems to create a touch-display system. This design, which would automatically disinfect its surfaces, will utilize intelligence and sensors from outside the display to clean only the parts that were touched, using a UV laser and mems system (smart targeting). Eventually, these areas would be targeted from the inside of the screen as well (designed as part of the LCD display pixel structure using state-of-the-art UVC LEDs and microled designs).
We would start with investigations to do the calculation of power capabilities and requirements and the timing & speed of UV scanning to clean a surface for specific applications and display sizes and ranges (and delivering a specific dosage over a specified time).
What can the intern expect to learn?
Energy and engineering considerations for the design of anti-COVID-19 materials.
Image Recognition and Advanced Metrology for Directed Self-Assembly of Block Copolymers and Nanoparticles
Principal Investigator: Ricardo Ruiz
Our group uses e-beam directed self-assembly of block copolymers, nanoparticles and biomolecules to solve fabrication challenges in the single nm length scale.
Accurate metrology from electron microscopy and atomic force microscopy images is essential to understand and evaluate the mechanisms of directed self-assembly, degree of perfection, line roughness, phase transitions, defect densities etc. While several off-the-shelf solutions exists, they usually don’t provide the flexibility or accuracy needed for the specific patterns formed in our lab. In this project we will build on prior expertise to develop software routines to perform image recognition and metrology relevant for large-area directed self-assembly of block copolymers and nanoparticles. The measurements will then be fed to soft-matter physics models to fit and understand assembly mechanisms, phase transitions and line roughness.
Skills needed: Python (additional Mathematica is a plus), basic image recognition, Fourier transforms and Fourier Analysis
The successful candidate will write image recognition software specifically tailored to the type of DSA patterns made in our lab (preferably python). The programs will extract key metrics in real and reciprocal space. The data should then be fed and fitted to soft matter physics models. The intern will work closely with experimentalist team members who will provide microscopy images and guidance.
What can the intern expect to learn?
The intern will have an opportunity to learn and develop computer programming skills for image recognition as well as fundamentals of pattern formation, thermodynamics and kinetics in self-assembly.