Projects

Open source python packages for automating molecular workflows

In macromolecules, there are numerous examples where structure dictates function including electronic conductivity of conjugated polymer materials. To elucidate the structure of doped and excited conjugated polymers we developed a multiscale model that captures electronic structure rearrangement and stochastically generates polymer chain conformations.

Machine learning models for predicting the bulk modulus of materials

Recent Publications

Conjugated molecules and polymers have the ability to be transformative semiconducting materials; however, to reach their full …

Control of equilibrium and non‐equilibrium thermomechanical behavior of poly(diketoenamine) vitrimers is shown by incorporating linear …

Nonaqueous polyelectrolyte solutions have been recently proposed as high Li+ transference number electrolytes for lithium ion …

Fundamental molecular-level understanding of functional properties of liquid solutions provides an important basis for designing …

In this article, we present evidence that the dielectric constant of an electrolyte solution can be effectively used to infer the …

We introduce atomate, an open-source Python framework for computational materials science simula- tion, analysis, and design with an …

Recent Posts

TL;DR: Running HPO at scale is important and Ray Tune makes that easy. When considering what HPO strategies to use for your project, …

Active Learning on Molecular Systems with GraphDot, xTB, ASE, and scikit-learn Original Post: 3/11/2020 Updated: 4/10/2020 Introduction …

Contact

  • bwood@lbl.gov
  • Building 59 Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720 USA
  • Email me to schedule a meeting