The Foundation Models and Applications Lab (FMAL) builds bridges across research domains, developing specialized AI models that can generate trustworthy research outcomes. We make sure experts—from chemists to social scientists—can use, shape, and leverage personalized models to solve real-world problems.
Research projects within FMAL focus on building data, benchmarks, and models for both users and experts. More information about past, current and on-going projects are listed below:
Foundation models for chemists | Zhang
Collaborators: Weist (Chemistry), Wang (UCLA), Coley (MIT)
Funding: NSF C-CAS
Although existing foundation models have shown promise in answering general chemistry
queries, our studies reveal several persistent limitations. These include difficulty interpreting
SMILES representations, hindering their ability to complete real chemistry tasks, and a lack of
reasoning capabilities in molecular structure elucidation. As a result, adapting LLMs to
chemistry remains a significant challenge, particularly due to the severe data limitations inherent
to these complex, domain-specific problems. We address these challenges by building chemistry-
specialized foundation models designed to assist chemists in solving real-world tasks, such as
reaction prediction, structure interpretation, and property inference, with enhanced reasoning and
domain-aligned representations.
Interactive agent foundation models for social study scientists | Zhang
Collaborators: Georgina Curto Rex (Senior AI researcher and team lead, United Nations University Institute in Macau)
By integrating foundation models into agent-based simulations, we create virtual environments
where autonomous agents, representing diverse individuals and communities, interact in ways
that reflect the real world. Empowered by the abilities of foundation models in contextual
understanding, reasoning, opinion formation and expression through text generation, these agents
will be designed to mirror human-like behaviors, including decision-making, communication,
and adaptation to socio-economic changes.
Benchmarking foundation models for autonomous lab safety | Zhang
Collaborators: Chen (IBM), Gao (IBM)
Funders: ND-IBM Tech Ethics Lab
Foundation models increasingly assist in tasks ranging from procedural guidance to autonomous
experiment orchestration. However, their advanced capabilities also introduce significant risks
when hallucinations, delayed hazard detection, or protocol misinterpretations intersect with
physical lab operations. Failures in adhering to safety protocols or prioritizing research
objectives can escalate into critical incidents. Our study focuses on understanding and enhancing
the reliability of foundation models in safety-critical tasks.
Polymer foundation models for materials informatics | Jiang
Collaborators: Luo (ND AME), Guo (ND ChemE)
Funded by NSF CBET
This project aims to combine molecular simulations, machine learning, and experiments to develop amorphous polymers with high TC (>1.5 W/mK) and understand the underlying molecular features dictating thermal transport.
More info: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332270
Scientific multimodal foundation models for hypothesis generation and validation | Jiang
Collaborators: Errington, Shakhlo (COS), Wu (ODU), Rajtmajer (PSU)
Funders: Center for Open Science
This project aims to develop a multimodal foundation model agent that automatically generates and validates scientific hypotheses. The agent is expected to be a helpful assistant for researchers.
Fluid dynamics foundation models for advanced mathematics and physics | Jiang
Collaborator: Wu (ND Math)
This project aims to develop an efficient system for evaluation of AI capabilities with respect to professional mathematics such as partial differential equations (Navier Stokes), number theory (Riemann Hypothesis), and computational complexity. Evaluation is based on correctly positing the lemma, formalizing, proving, and stating the proof in natural language.
Geoscience foundation models for planetary scientists | Jiang
Collaborators: Wang, Huang (UTK EEPS)
This project aims to create general-purpose AI systems that can accelerate scientific discovery by learning from large, diverse datasets across planetary science. Geoscience foundation models aim to scale up the reasoning, discovery, and exploration capabilities of planetary scientists by acting as versatile, data-driven scientific assistants.
Mental health foundation models to address anxiety, depression, and disorders | Jiang
Collaborators: Zhang, Haeffel (ND Psychology), Sullivan (IBM-ND Tech Ethics)
This project aims to develop new model capabilities that would lead to more competent models when applied to mental health. It also aims to develop new methods and datasets to evaluate and benchmark model performance when applied to mental health measurement.