Chemical Informatics Group

The Group establishes the accuracy of computational methods by compiling comparisons to measured values. It develops new algorithms for chemical informatics, data analysis, and molecular simulation, as well as new standards for the management, validation, and dissemination of computational and experimental data. The Group's efforts are performed in support of measurement science in areas such as chemical manufacturing, environment and climate, energy, biomedical and health, and advanced materials.

News and Updates

Illustrated cube has crystal framework in gray with red balls interspersed in the open spaces.

To Remove CO₂ From the Atmosphere, Imagine the Possibilities

August 24, 2022

Computer simulation methods from NIST help speed up the search for carbon capture materials.

Projects and Programs

A Fundamental Study of the Conformational Phase Behavior of Chain Molecules at Interfaces

Ongoing

Chain molecules tethered or adsorbed to solid surfaces are at the heart of a wide range of technologies. Examples include colloidal particle stabilization

Classification Methods for the NIST Biorepository

Ongoing

We aim to identify chemometric fingerprints using artificial intelligence methods that reflect the temporal and geographic history of seabirds, and by extension

Connecting Thermodynamic and Dynamic Properties of Bulk and Confined Fluids

Ongoing

It is well known that the physical properties of a fluid can change profoundly, and often non-trivially, when placed in a confined environment. Only for the

Consistency Analysis and Uncertainty in ‘omic Data

Ongoing

As ‘omics moves into industrial and commercial applications, there is a need for quality and process control to ensure safety and efficacy of products and

Publications

MOFX-DB: An online database of computational adsorption data for nanoporous materials

January 4, 2023

Author(s)

N. S. Bobbitt, Kaihang Shi, Benjamin J. Bucior, Haoyuan Chen, Nathaniel Tracy-Amoroso, Zhao Li, Yangzesheng Sun, Julia Merlin, Joern Ilja Siepmann, Daniel Siderius, Randall Q. Snurr

Machine learning and data mining coupled with molecular modeling have become powerful tools for materials discovery. Metal-organic frameworks (MOFs) are a rich

Temperature Extrapolation of Henry's Law Constants and the Isosteric Heat of Adsorption

September 28, 2022

Author(s)

Daniel Siderius, Harold Hatch, Vincent K. Shen

Computational screening of adsorbent materials often uses the Henry's law constant ($\KH$) (at a particular temperature) as a first discriminator metric due to

Systematic Control of Collective Variables Learned from Variational Autoencoders

September 7, 2022

Author(s)

Jacob Monroe, Vincent K. Shen

Variational autoencoders (VAEs) are rapidly gaining popularity within molecular simulation for discovering low- dimensional, or latent, representations, which

Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders

May 25, 2022

Author(s)

Jacob Monroe, Vincent K. Shen

Discovering meaningful collective variables for enhancing sampling, via applied biasing potentials or tailored MC move sets, remains a major challenge within

Awards

2020 - Bronze Medal Award---Barry Schneider, Robert Hanisch, Carl Spangler, Walid Keyrouz, Andrew Reid, Timothy Blattner, Derek Juba, Robert Densock, Abdella Battou, Thomas Allison

For outstanding initiative in developing and deploying a new computational capability in support of NIST initiatives in big data and AI.

Chemical Informatics Group

News and Updates

To Remove CO₂ From the Atmosphere, Imagine the Possibilities

Projects and Programs

A Fundamental Study of the Conformational Phase Behavior of Chain Molecules at Interfaces

Classification Methods for the NIST Biorepository

Connecting Thermodynamic and Dynamic Properties of Bulk and Confined Fluids

Consistency Analysis and Uncertainty in ‘omic Data

Publications

MOFX-DB: An online database of computational adsorption data for nanoporous materials

Temperature Extrapolation of Henry's Law Constants and the Isosteric Heat of Adsorption

Systematic Control of Collective Variables Learned from Variational Autoencoders

Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders

Awards

2020 - Bronze Medal Award---Barry Schneider, Robert Hanisch, Carl Spangler, Walid Keyrouz, Andrew Reid, Timothy Blattner, Derek Juba, Robert Densock, Abdella Battou, Thomas Allison

Contacts

Group Leader

Office Manager