About

Professor Tom Crawford is associated with the Center for Geospatial Information Technology (CGIT) at Virginia Tech, which collaborates across research, education, and outreach with a transdisciplinary approach, addressing complex problems with geospatial science. His work involves applying geospatial science to improve quality of life, environment, and community through smart decision making. The center utilizes extensive knowledge in Geographic Information Systems to develop powerful geospatial tools with user-friendly interfaces, transforming spatial data into secure, intuitive decision-making tools that empower agencies, researchers, and communities across the Commonwealth. His research focuses on creating decision-making tools that fuse geospatial science, software engineering, and user experience design to develop applications that translate complex datasets into practical insights. These tools support decision-makers in mapping risk, tracking infrastructure, forecasting change, and enhancing safety, efficiency, and strategic planning. Key projects include redesigning the DMV Geocoding Tool, developing the Virginia State Police Crash Analysis Dashboard, and creating statewide broadband and environmental initiatives. His contributions help turn data into decisions that drive Virginia forward, supporting smarter policy, community development, and safety initiatives.

Research topics

• Computer Science
• Chemistry
• Computational chemistry
• Mathematics
• Engineering
• Computational science
• Programming language
• Database
• Materials science
• Quantum mechanics
• Operating system
• Theoretical computer science
• Parallel computing
• Nanotechnology
• Physics
• Engineering physics

Selected publications

• 50 and 100 Years Ago in <i>The Journal of Physical Chemistry</i> – 2025 Edition

  The Journal of Physical Chemistry C · 2025-05-08

  article
  PublisherDOI

• A low-cost four-component relativistic coupled cluster linear response theory based on perturbation sensitive natural spinors

  The Journal of Chemical Physics · 2025-07-24 · 4 citations

  articleOpen access

  We present an efficient implementation of four-component linear response coupled cluster singles and doubles (4c-LRCCSD) theory that enables accurate and computationally efficient calculation of polarizabilities for systems containing heavy elements. We have observed that the frozen natural spinor (FNS)-based truncation scheme is not suitable for linear response properties, as it leads to larger errors in static and dynamic polarizability values. In this work, we have introduced a "perturbation-sensitive" density to construct the natural spinor basis, termed FNS++. Using FNS++, we achieve excellent accuracy when compared to experimental data and other theoretical results, even after truncating nearly 70% of the total virtual spinors. We also present pilot applications of the 4c-LRCCSD method to calculate the polarizability spectra of 3d transition metals. By employing the FNS++-based 4c-LRCCSD, we have been able to compute polarizabilities for systems with over 1200 virtual spinors, maintaining low computational cost and excellent accuracy.

  PublisherOA PDFDOI

• A Review of 2024 at <i>The Journal of Physical Chemistry</i>

  The Journal of Physical Chemistry C · 2025-01-09

  review
  PublisherDOI

• 50 and 100 Years Ago in <i>The Journal of Physical Chemistry</i> – 2025 Edition

  The Journal of Physical Chemistry A · 2025-05-08

  editorial
  PublisherDOI

• Analytic Computation of Vibrational Circular Dichroism Spectra Using Configuration Interaction Methods

  ArXiv.org · 2025-10-30

  preprintOpen accessSenior author

  In this work, we present the first derivation and implementation of analytic gradient methods for the computation of the atomic axial tensors (AATs) required for simulations of vibrational circular dichroism (VCD) spectra using configuration interaction methods including double (CID) and single and double (CISD) excitations. Our new implementation includes the use of non-canonical perturbed orbitals to improve the numerical stability of the gradients in the presence of orbital near-degeneracies, as well as frozen-core capabilities. We validated our analytic CID and CISD formulations against two new finite-difference approaches. Using this new implementation, we investigated the significance of singly excited determinants and the role of CI-coefficient optimization in VCD simulations by comparisons among Hartree-Fock (HF) theory, second-order Møller-Plesset perturbation (MP2) theory, CID, and CISD theories. For our molecular test set including (P )-hydrogen peroxide, (S )-methyloxirane, (R)-3-chloro-1-butene, (R)-4-methyl-2-oxetanone, and (M )-1,3-dimethylallene we noted sign discrepancies between the HF and MP2 methods compared to that of the new CID and CISD methods for four of the five molecules as well as similar discrepancies between the CID and CISD methods for (M )-1,3-dimethylallene.

  PublisherOA PDFDOI

• The need to implement FAIR principles in biomolecular simulations

  Nature Methods · 2025-04-01 · 53 citations

  articleOpen access
  PublisherOA PDFDOI

• A Review of 2024 at <i>The Journal of Physical Chemistry</i>

  The Journal of Physical Chemistry A · 2025-01-09

  review
  PublisherDOI

• Unlocking the future of materials science: key insights from the DCTMD workshop

  Journal of Materials Informatics · 2025-11-04

  articleOpen access

  The International Workshop on Data-Driven Computational and Theoretical Materials Design was held between October 9-13, 2024, in Shanghai, gathering leading scientists and researchers from around the world, representing various aspects of data-driven AI methodologies and applications in materials design. The topics covered over 46 talks and 29 posters spanned a wide range of the latest advancements, including Machine Learning for Materials Design, Method Development, Machine Learning Interatomic Potentials, Advanced Computing, Infrastructure and Standards, Large Language Models, and Autonomous Labs. As part of the workshop, a panel discussion titled “Unlocking the AI Future of Materials Science” was held to disseminate the state-of-the-art of AI/ML in materials science and consider directions for the future. This report is a synthesis, for this Special Issue, of the panel discussion - drawing on insights gained from the workshop as a whole and surrounding conversations, in particular, the question of what constitutes success.

  PublisherOA PDFDOI

• Tunneling through 100 Years of Quantum Mechanics: An ACS Collection to Celebrate the Centennial

  ACS Applied Materials & Interfaces · 2025-11-07

  editorial1st author
  PublisherDOI

• SEAMM: A Simulation Environment for Atomistic and Molecular Modeling

  The Journal of Physical Chemistry A · 2025-07-18 · 3 citations

  articleOpen accessSenior author

  The simulation environment for atomistic and molecular modeling (SEAMM) is an open-source software package written in Python that provides a graphical interface for setting up, executing, and analyzing molecular and materials simulations. The graphical interface reduces the entry barrier for the use of new simulation tools, facilitating the interoperability of a wide range of simulation tools available to solve complex scientific and engineering problems in computational molecular science. Workflows are represented graphically by user-friendly flowcharts, which are shareable and reproducible. When a flowchart is executed within the SEAMM environment, all results, as well as metadata describing the workflow and codes used, are saved in a datastore that can be viewed using a browser-based dashboard, which allows collaborators to view the results and use the flowcharts to extend the results. SEAMM is a powerful productivity and collaboration tool that enables interoperability between simulation codes and ensures reproducibility and transparency in scientific research. We illustrate the flexibility and productivity of SEAMM with three examples: a simple molecular dynamics calculation to provide an overview; exploring the rearrangement of methylisocyanide to acetonitrile using a wide range of quantum codes and force fields; and using SEAMM for industrial research on battery materials with simulations of the diffusivity and ionic conductivity of electrolytes and the density, thermal expansion, and thermal conductivity of cathode materials as a function of lithiation.

  PublisherDOI

Recent grants

• Collaborative Research: SI2-SSI: Removing Bottlenecks in High Performance Computational Science

  NSF · $600k · 2015–2019

• Collaborative Research: SI2-SSI: Sustainable Development of Next-Generation Software in Quantum Chemistry

  NSF · $325k · 2012–2015

• S2I2: Impl: The Molecular Sciences Software Institute

  NSF · $15.0M · 2021–2027

• CAREER: Accurate Quantum Chemical Methods for the Chiroptical Properties of Large Molecules

  NSF · $435k · 2002–2008

• Advanced Quantum Mechanical Methods for the Chiroptical Properties of Molecules in Solution

  NSF · $450k · 2015–2019

Frequent coauthors

• Micah L. Abrams

  35 shared

• Ryan C. Fortenberry

  33 shared

• Henry F. Schaefer

  University of Georgia

  32 shared

• Roberto Di Remigio

  UiT The Arctic University of Norway

  32 shared

• Patrick H. Vaccaro

  Yale University

  28 shared

• Kenneth B. Wiberg

  28 shared

• Gregory V. Hartland

  University of Notre Dame

  26 shared

• Joan‐Emma Shea

  University of California, Santa Barbara

  26 shared

Labs

• Center for Geospatial Information TechnologyPI

  N/A

Education

• Ph.D., Department of Chemistry

  University of Georgia

  1996

• B.S., Chemistry

  Duke University

  1992

Awards & honors

• Geography and Spatial Sciences Program (GSS) Coastal erosion…
• COCA – Supporting Resilient Coastal Communities and Ecosyste…