Materials Scientist · Pacific Northwest National Laboratory

Sarah Allec

I combine physics-based simulation with AI, from Bayesian models to autonomous agents, to design new materials and close the loop between computation and experiment.

01About

Computational materials scientist at PNNL.

I am a computational materials scientist on the Materials Discovery Team at Pacific Northwest National Laboratory, where I specialize in ab initio modeling and active learning to help bridge the gap between simulations and experiments. I previously worked as a research scientist at Citrine Informatics, specializing in materials informatics, and before that as a postdoc in PNNL's Basic & Applied Molecular Foundations group, studying CO2 capture and heterogeneous catalysis.

I earned my B.S. in Applied Mathematics with an emphasis in Physics from UC Riverside in 2015, then stayed for a Ph.D. in Materials Science & Engineering, first in Bryan Wong's Nanoscale & Mesoscale Energy Materials group and then in Alex Greaney's Computational Materials Group. My dissertation focused on atomistic modeling of amorphous materials, particularly amorphous metal oxides and water-in-salt electrolytes.

In my research I combine atomistic modeling at both the electronic (quantum) and atomic (classical) levels with machine learning, and increasingly with LLM-based agents: I am a core contributor to SciLink, an open-source platform of agents for automating scientific research, where I build the simulation agents. I am passionate about finding new ways to solve problems in materials science and chemistry in silico in order to accelerate scientific discovery.

02Research

Where simulation, data, and experiment meet.

Atomistic simulation

DFT, ab initio molecular dynamics, and machine-learned interatomic potentials for amorphous materials, surfaces, and interfaces.

Probabilistic machine learning

Bayesian and uncertainty-aware models, from surrogate models over curated datasets to per-atom uncertainty in learned potentials, so predictions come with a measure of trust.

Active learning & autonomous experiments

Sequential design and Bayesian optimization that choose the next simulation or experiment, and closed loops that run them on real instruments.

AI agents for science

LLM-powered agents that plan, analyze, and simulate, so that physics-based modeling becomes a tool an autonomous workflow can pick up and use.

See SciLink →

Open-source software

SciLinkCore contributor An open-source platform of LLM-powered agents that plan experiments, analyze multimodal data, and run simulations. I build the simulation agents, which set up and run DFT and molecular dynamics calculations and recommend structures. SciLink TutorialsAuthor Hands-on notebooks for SciLink from the MRS Spring 2026 tutorial and the MEM-C Seed AI Fellowship, covering knowledge-grounded planning, Bayesian optimization, and active-learning DFT screening. RDsynthesisMLAuthor Scripts and notebooks reproducing "Toward autonomous materials synthesis via reaction-diffusion coupling", from image scalarization through Bayesian optimization of the next experiment.

Methods

  • Active learning
  • Bayesian optimization
  • Uncertainty quantification
  • LLM agents
  • Density functional theory
  • Ab initio molecular dynamics
  • Machine-learned potentials
  • Multiscale modeling
  • Data curation

Systems

  • Nuclear waste glasses
  • CO2 capture solvents
  • Heterogeneous catalysts
  • Amorphous oxides
  • Water-in-salt electrolytes
  • Solid-liquid interfaces
  • Critical materials recovery
  • Materials synthesis

03Experience

National lab, industry, and back again.

  1. 2024 – present

    Materials Scientist

    Pacific Northwest National Laboratory · Materials Discovery Team · Richland, WA

    Research on active learning, uncertainty-aware machine learning, and atomistic modeling for materials discovery, autonomous synthesis, and nuclear waste form design.

  2. 2022 – 2024

    Research Scientist II

    Citrine Informatics · External Research Department · Remote

    Materials informatics research on funded programs, including physics-informed machine learning for glass design and multi-institutional data management for combinatorial materials science.

  3. 2020 – 2022

    Postdoctoral Research Associate

    Pacific Northwest National Laboratory · Basic & Applied Molecular Foundations Group · Richland, WA

    Computational design of carbon capture solvents and catalysts using ab initio molecular dynamics and density functional theory.

Education

  1. 2015 – 2020

    Ph.D. in Materials Science & Engineering

    University of California, Riverside

    • Thesis: Atomistic Modeling of Amorphous Materials
    • Advisors: P. Alex Greaney (Ph.D.) and Bryan M. Wong (M.S., 2018)
    • NSF Graduate Research Fellowship, NASA MIRO FIELDS Graduate Student Fellowship
  2. 2011 – 2015

    B.S. in Applied Mathematics (Physics emphasis)

    University of California, Riverside

    • Graduated summa cum laude
    • Regents Scholarship

04Publications

29 peer-reviewed articles and book chapters.

Google Scholar · ORCID

Selected

Digital Discovery · 2025 Active and transfer learning with partially Bayesian neural networks for materials and chemicals S. I. Allec, M. Ziatdinov Nature Chemistry · 2024 Tetrameric self-assembling of water-lean solvents enables carbamate anhydride-based CO2 capture chemistry J. Leclaire, D. J. Heldebrant, K. Grubel, J. Septavaux, M. Hennebelle, E. D. Walter, Y. Chen, J. L. Bañuelos, D. Zhang, M. T. Nguyen, D. Ray, S. I. Allec, D. Malhotra, W. Joo, J. King Journal of the American Ceramic Society · 2024 Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability S. I. Allec, X. Lu, D. R. Cassar, X. T. Nguyen, V. I. Hegde, T. Mahadevan, M. Peterson, J. Du, B. J. Riley, J. D. Vienna, J. E. Saal The Journal of Chemical Physics · 2021 The role of sub-surface hydrogen on CO2 reduction and dynamics on Ni(110): An ab initio molecular dynamics study S. I. Allec, M. T. Nguyen, R. Rousseau, V. A. Glezakou

All publications

2026

  1. Comparison of DeePMD, MTP, GAP, ACE and MACE Machine-Learned Potentials for Radiation-Damage Simulations: A User Perspective A. Roy, R. Devanathan, S. Allec, G. Nandipati, A. M. Casella, D. J. Senor, D. D. Johnson, G. Balasubramanian, A. Soulami Advanced Intelligent Discovery, e202500196 (2026)
  2. Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation G. Ricchiuti, S. I. Allec, M. Ziatdinov, V. Prabhakaran Advanced Intelligent Discovery 2, e202500237 (2026)
  3. Boron Coordination in Multicomponent Glasses: Analytical Models and Machine Learning With Uncertainty C. E. Curry, M. Diaz-Acevedo, D. Wang, S. I. Allec, J. J. Neeway, J. D. Vienna, X. Lu International Journal of Applied Glass Science 17, e70043 (2026)
  4. Predicting Nepheline Formation in Nuclear Waste Glasses Using Machine Learning With Uncertainty Quantification M. Diaz-Acevedo, C. E. Curry, X. Lu, S. Chong, S. I. Allec, J. D. Vienna International Journal of Applied Glass Science 17, e70062 (2026)
  5. Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation A. Ritchhart, S. I. Allec, P. Butreddy, K. Kulesa, Q. Wang, D. T. Nguyen, M. Ziatdinov, E. Nakouzi Materials Horizons 13, 7008-7014 (2026)
  6. Assessing universal MLIP robustness with per-atom uncertainty for simulations of solid-liquid interfaces J. A. Bilbrey, J. S. Firoz, S. I. Allec, H. W. Sprueill, A. D. von Rueden, B. A. Jackson, S. Raugei, M. S. Lee, S. Choudhury npj Computational Materials 12, 291 (2026)

2025

  1. Active and transfer learning with partially Bayesian neural networks for materials and chemicals S. I. Allec, M. Ziatdinov Digital Discovery 4, 1284-1297 (2025)
  2. Computational Investigation of a CO2 Conversion Strategy via Diels–Alder Reaction in a Carbon Capture Solvent D. Zhang, M. T. Manetsch, S. I. Allec, L. Kollias, R. Rousseau, D. J. Heldebrant, V. A. Glezakou ACS Omega 10, 23663-23672 (2025)
  3. Toward autonomous materials synthesis via reaction–diffusion coupling A. Ritchhart, S. I. Allec, H. M. Job, P. Butreddy, M. Ziatdinov, E. Nakouzi APL Machine Learning 3, 036109 (2025)

2024

  1. A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community S. I. Allec, E. S. Muckley, N. S. Johnson, C. K. H. Borg, D. J. Kirsch, J. Martin, R. Pant, I. Takeuchi, A. S. Lee, J. E. Saal, L. Ward, A. Mehta Integrating Materials and Manufacturing Innovation 13, 406-419 (2024)
  2. Tetrameric self-assembling of water-lean solvents enables carbamate anhydride-based CO2 capture chemistry J. Leclaire, D. J. Heldebrant, K. Grubel, J. Septavaux, M. Hennebelle, E. D. Walter, Y. Chen, J. L. Bañuelos, D. Zhang, M. T. Nguyen, D. Ray, S. I. Allec, D. Malhotra, W. Joo, J. King Nature Chemistry 16, 1160-1168 (2024)
  3. Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability S. I. Allec, X. Lu, D. R. Cassar, X. T. Nguyen, V. I. Hegde, T. Mahadevan, M. Peterson, J. Du, B. J. Riley, J. D. Vienna, J. E. Saal Journal of the American Ceramic Society 107, 7784-7799 (2024)
  4. Molecular Understanding of Nitrogen Oxide Fixation of Water-Lean Carbon Capture Solvents by Atomistic Modeling L. Kollias, M. T. Nguyen, S. I. Allec, D. Malhotra, D. Zhang, R. Rousseau, V. A. Glezakou, P. K. Koech, D. J. Heldebrant Industrial & Engineering Chemistry Research 63, 12316-12324 (2024)
  5. Towards informatics-driven design of nuclear waste forms V. I. Hegde, M. Peterson, S. I. Allec, X. Lu, T. Mahadevan, T. Nguyen, J. Kalahe, J. Oshiro, R. J. Seffens, E. K. Nickerson, J. Du, B. J. Riley, J. D. Vienna, J. E. Saal Digital Discovery 3, 1450-1466 (2024)

2023

  1. The Role of Surface Hydroxyls in the Mobility of Carboxylates on Surfaces: Dynamics of Acetate on Anatase TiO2(101) R. Ma, C. R. O’Connor, G. Collinge, S. I. Allec, M. S. Lee, Z. Dohnálek The Journal of Physical Chemistry Letters 14, 2542-2550 (2023)
  2. Dynamic Evolution of Palladium Single Atoms on Anatase Titania Support Determines the Reverse Water–Gas Shift Activity L. Chen, S. I. Allec, M. T. Nguyen, L. Kovarik, A. S. Hoffman, J. Hong, D. Meira, H. Shi, S. R. Bare, V. A. Glezakou, R. Rousseau, J. Szanyi Journal of the American Chemical Society 145, 10847-10860 (2023)
  3. Enhancing CO2 Transport Across a PEEK-Ionene Membrane and Water-Lean Solvent Interface E. D. Walter, D. Zhang, Y. Chen, K. S. Han, J. D. Bazak, S. Burton, K. O'Harra, D. W. Hoyt, J. E. Bara, D. Malhotra, S. I. Allec, V. A. Glezakou, D. J. Heldebrant, R. Rousseau ChemSusChem 16, e202300157 (2023)

2022

  1. Assessing entropy for catalytic processes at complex reactive interfaces L. Kollias, G. Collinge, D. Zhang, S. I. Allec, P. K. Gurunathan, G. Piccini, S. F. Yuk, M. T. Nguyen, M. S. Lee, V. A. Glezakou, R. Rousseau Annual Reports in Computational Chemistry, 3-51 (2022) · book chapter
  2. Advanced Theory and Simulation to Guide the Development of CO2 Capture Solvents L. Kollias, D. Zhang, S. I. Allec, M. T. Nguyen, M. S. Lee, D. C. Cantu, R. Rousseau, V. A. Glezakou ACS Omega 7, 12453-12466 (2022)

2021

  1. The electrolyte comprising more robust water and superhalides transforms Zn-metal anode reversibly and dendrite-free C. Zhang, W. Shin, L. Zhu, C. Chen, J. C. Neuefeind, Y. Xu, S. I. Allec, C. Liu, Z. Wei, A. Daniyar, J. Jiang, C. Fang, P. A. Greaney, X. Ji Carbon Energy 3, 339-348 (2021)
  2. The role of sub-surface hydrogen on CO2 reduction and dynamics on Ni(110): An ab initio molecular dynamics study S. I. Allec, M. T. Nguyen, R. Rousseau, V. A. Glezakou The Journal of Chemical Physics 155, 044702 (2021)
  3. Amphilic Water-Lean Carbon Capture Solvent Wetting Behavior through Decomposition by Stainless-Steel Interfaces M. T. Nguyen, K. Grubel, D. Zhang, P. K. Koech, D. Malhotra, S. Allec, R. Rousseau, V. A. Glezakou, D. J. Heldebrant ChemSusChem 14, 5283-5292 (2021)

2019

  1. Linear-Response and Real-Time, Time-Dependent Density Functional Theory for Predicting Optoelectronic Properties of Dye-Sensitized Solar Cells S. I. Allec, A. Kumar, B. M. Wong Dye-Sensitized Solar Cells, 171-201 (2019) · book chapter
  2. Heterogeneous CPU+GPU-Enabled Simulations for DFTB Molecular Dynamics of Large Chemical and Biological Systems S. I. Allec, Y. Sun, J. Sun, C. A. Chang, B. M. Wong Journal of Chemical Theory and Computation 15, 2807-2815 (2019)
  3. Chirality Induced Spin Selectivity of Photoexcited Electrons in Carbon-Sulfur [n]Helicenes J. M. Matxain, J. M. Ugalde, V. Mujica, S. I. Allec, B. M. Wong, D. Casanova ChemPhotoChem 3, 770-777 (2019)

2018

  1. A Highly Stretchy, Transparent Elastomer with the Capability to Automatically Self-Heal Underwater Y. Cao, H. Wu, S. I. Allec, B. M. Wong, D. S. Nguyen, C. Wang Advanced Materials 30, 1804602 (2018)

2017

  1. A Transparent, Self-Healing, Highly Stretchable Ionic Conductor Y. Cao, T. G. Morrissey, E. Acome, S. I. Allec, B. M. Wong, C. Keplinger, C. Wang Advanced Materials 29, 1605099 (2017)

2016

  1. Unusual Bandgap Oscillations in Template-Directed π-Conjugated Porphyrin Nanotubes S. I. Allec, N. V. Ilawe, B. M. Wong The Journal of Physical Chemistry Letters 7, 2362-2367 (2016)
  2. Inconsistencies in the Electronic Properties of Phosphorene Nanotubes: New Insights from Large-Scale DFT Calculations S. I. Allec, B. M. Wong The Journal of Physical Chemistry Letters 7, 4340-4345 (2016)

05Talks, tutorials, awards & service

Beyond the papers.

Invited talks

  • Advancing Autonomous Materials Research with Uncertainty-Aware AI and Human-AI CollaborationMaterials Research Society (MRS) Spring Meeting, Honolulu, HI, 2026
  • Novel approaches to informatics-driven nuclear waste form design: dataset curation, surrogate modeling, and sequential learningACerS Glass & Optical Materials Division (GOMD) Meeting, 2024

Tutorials

  • Deploying Agentic AI in Materials Characterization Workflows — The Agent Toolkit: Hybrid Models, Active Learning, and Theory-in-the-LoopMaterials Research Society (MRS) Spring Meeting, Honolulu, HI, 2026

Awards & fellowships

  • NERSC Director's Reserve AwardNational Energy Research Scientific Computing Center, 2024 · Active machine learning for nuclear waste form design
  • DOE Mission Science AwardNational Energy Research Scientific Computing Center, 2022 – 2023 · Computational screening of redox couples for electrochemical direct air capture of CO2
  • NSF Graduate Research FellowshipNational Science Foundation, 2017 – 2020
  • NASA MIRO FIELDS Graduate Student FellowshipNASA, 2016 – 2017
  • Regents ScholarshipUniversity of California, Riverside, 2011 – 2015

Mentoring & service

  • Peer reviewerRSC Digital Discovery; DOE Office of Science SBIR/STTR proposals, 2023 – present
  • Undergraduate research mentorCal Poly San Luis Obispo Materials Engineering Summer Research Program, 2023
  • Workshop leaderNorth American Materials Education Symposium, 2023
  • Community volunteerInterfaith Sanctuary, Boise, ID, 2023 – present
  • Graduate intern mentorDOE Science Undergraduate Laboratory Internships (SULI) program, PNNL, 2021
  • Co-President and TreasurerAssociation for Women in Science, UC Riverside chapter, 2018 – 2020
  • Mentor and tutorSchool on Wheels, Riverside, CA, 2018 – 2021

06Contact

Say hello.

Send me an email if you would like to get in touch, have questions about my work, or just want to say hello.