ColabFit: Informatics for Advanced Materials and Chemistry


The objectives of ColabFit are (1) to facilitate the connection between empirical and data-driven interatomic potential (DDIP) fitting codes and major online repositories of first principles (FP) data through the application programming interfaces (APIs) that these repositories have developed; and (2) to make it easy for the materials research community to use and collaborate on DDIP development. To facilitate these goals, the PIs have assembled a consortium of leaders in DDIP development and FP cyberinfrastructures (CIs) who can provide input to help guide the design of ColabFit and technical support to enable the ColabFit research team to interface with their platforms. ColabFit will engage with this consortium through an online kickoff meeting and ongoing consultation to design the ColabFit interface standard and archive formats. ColabFit will work to grow the pool of participants as the project moves forward.

DDIP/IP Projects

  • ALC – Interatomic Potentials “à la carte”, Lawrence Livermore National Laboratory, Livermore, CA, USA

  • Atomicrex – A tool for the construction of interaction models, Chalmers University of Technology, Sweden and Darmstadt University of Technology, Germany

  • DOEIPM – Database optimization for empirical interatomic potential models, University of Illinois at Urbana-Champaign, USA

  • FitSNAP – Software for generating SNAP machine-learning interatomic potentials, Sandia National Laboratories, Albuquerque, NM, USA

  • GAP – Gaussian Approximation Potential, University of Cambridge, Cambridge, UK

  • GP/MFF – Gaussian process-based active learning, Harvard University, Cambridge, MA, USA

  • INNP – Implanted Neural Network Potentials, Harvard University, Cambridge, MA, USA

  • MAML – MAterials Machine Learning, University of California, San Diego, CA, USA

  • MLIP – Machine Learning Interatomic Potentials, Skolovo Institute of Science and Technology, Moscow, Russia

  • PINNfit – Physically informed artificial neural networks for atomistic modeling of materials, George Mason University, Fairfax, VA, USA

  • POET – Potential Optimization by Evolutionary Techniques, Johns Hopkins University, Baltimore, MD, USA

  • Potfit – Effective potentials from ab-initio data, University of Warwick, Warwick, UK

  • ReaxFF – Reactive Force Field, Pennsylvania State University, University Park, PA, USA

  • RuNNer – Development of Neural Network potential-energy surfaces, University of Göttingen, Göttingen, Germany

  • SchNetPack – Deep Neural Networks for Atomistic Systems, University of Luxembourg, Luxembourg

FP Data Repositories

  • AFLOW – Automatic framework for high-throughput materials discovery, Duke University, Alexandria, VA, USA

  • CMR – Computational Materials Repository, Technical University of Denmark, Lyngby, Denmark

  • Materials Project, LBNL, Berkeley, CA, USA

  • NOMAD – Novel Materials Discovery, University of Warwick, Warwick, UK

  • OQMD – Open Quantum Materials Database, Northwestern University, Evanston, IL, USA

Standards Organizations

  • OPTIMADE – Open Databases Integration for Materials Design

  • MolSSI – Molecular Sciences Software Institute


If you would like to join the ColabFit Consortium to advance ColabFit goals, please fill in the following information and a ColabFit team member will contact you:

University, company, lab, etc.