Sean P. Florez

OpenSDL

Mon, 28 Sep 2026 00:00:00 +0000

How fast a lab learns depends on how fast it gets from one result to the next experiment. Iteration rate beats brilliance. The person who tests ten hypotheses learns more than the person who tests one.

OpenSDL is open-source software for self-driving labs. You declare what a laboratory can do, and OpenSDL turns those declarations into reproducible workflows. It keeps the evidence from every run and uses each result to choose the next experiment. For now it is an alpha, and it does all of this only in simulation.

MXene Interlayer Shear

Mon, 28 Sep 2026 00:00:00 +0000

MXenes are conductive two-dimensional materials, and they’re promising for electromagnetic shielding in composites. Each layer’s surface carries chemical groups such as OH and F, its termination. To use them you need to understand how the layers hold together and slide apart.

At the Air Force Research Laboratory in summer 2025 I built a LAMMPS workflow that shears OH-terminated Ti₃C₂ bilayers under controlled hydration, and I ran the stress sweeps on DoD HPC. Fitting slip probability against applied shear stress showed that hydration is non-monotonic. A little water lubricates the interface, and full coverage rebuilds strength as water forms bridging hydrogen bonds. Water is a knob that moves shear strength by an order of magnitude, which gives stress windows for EM-shielding composite design.

Hydrogen Release on Pt Nanocrystals

Mon, 28 Sep 2026 00:00:00 +0000

Liquid organic hydrogen carriers are organic compounds that store hydrogen chemically, and N-ethylcarbazole is one of them. Getting the hydrogen back out takes heat and a catalyst, and it’s generally seen as the main drawback of the approach. Platinum nanocrystals can do the job. The question is how the release happens on their surface, and which sites do the work.

I designed and ran the molecular dynamics campaign behind the first paper. It simulates slabs, flat platinum surfaces that isolate one facet at a time, and cuboctahedral nanoparticles, which have flat faces, edges and vertices. Dehydrogenation-reactive configurations concentrate at low-coordination sites, the surface atoms with the fewest neighbors, so local coordination rather than facet identity sets where release happens.

Interface Force Field

Sat, 03 Oct 2026 00:00:00 +0000

I use this presentation to explain how atomic structures, chemical assignments, model parameters and experimental references connect to testable material predictions. I follow source-grounded rhodium structures, published lattice and surface calibration, and mechanical predictions that were not fitted to those targets. I also explain the IFF Agent workflow and the complementary roles of classical models, DFT and learned interatomic potentials.

Explore the presentation, download the editable PowerPoint or static PDF, and view the structure, energy/force and evidence animations. The motion is illustrative, and I have not rerun the published results for this presentation.

A District Before It's Finished

Wed, 30 Sep 2026 00:00:00 +0000

A coastal district should be worth spending time in while parts of it are still being built. The finished waterfront has to make sense alongside the work that brings the rest of the place into use.

A short concept film explores this through an imagined district. It opens on a busy street, close to the people walking between buildings. The camera moves toward the water and through a greener urban area, then pulls back across working yards and unfinished land. By the time the whole district comes into view, several stages of construction are visible together.

Where Are We

Thu, 03 Sep 2026 00:00:00 +0000

Talk given in a foundation models and alignment course at CU Boulder, September 2026. The brief was to present the state of practice in your own field, name the methods people actually use, and say where they break.

Read the slides in your browser (18 slides, arrow keys to move) · PDF · PowerPoint

What is in it. Machine-learned interatomic potentials as foundation models: MACE, GNoME, MatterSim, UMA. How density functional theory, hand-written force fields and learned potentials each answer the same question, which is what a given arrangement of atoms costs in energy. What scaling the training data bought. How that data gets manufactured in the first place.

Build Evidence Before You Build Commitment

Fri, 10 Jul 2026 00:00:00 +0000

Most technical projects don’t fail because the team couldn’t write enough code. They fail because an early assumption was treated as a fact.

Someone assumed a provider’s interface would do what the docs implied. Someone assumed the model would hold up on the real task, not the demo one. Someone assumed a material would keep its properties at scale, or that two subsystems would talk to each other cleanly, or that a security boundary would actually hold. The team turned that assumption into a requirement, built a competent implementation around it, and only discovered the problem after the architecture was expensive to change.

A Capability, Not a Megaproject