All Publications


  • A framework for automated structure elucidation from routine NMR spectra CHEMICAL SCIENCE Huang, Z., Chen, M. S., Woroch, C. P., Markland, T. E., Kanan, M. W. 2021

    View details for DOI 10.1039/d1sc04105c

    View details for Web of Science ID 000718969800001

  • AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials JOURNAL OF CHEMICAL PHYSICS Chen, M. S., Morawietz, T., Mori, H., Markland, T. E., Artrith, N. 2021; 155 (7): 074801

    Abstract

    Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

    View details for DOI 10.1063/5.0063880

    View details for Web of Science ID 000685163500002

    View details for PubMedID 34418919

  • Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments. The journal of physical chemistry letters Chen, M. S., Zuehlsdorff, T. J., Morawietz, T., Isborn, C. M., Markland, T. E. 2020: 7559–68

    Abstract

    The excited-state dynamics of chromophores in complex environments determine a range of vital biological and energy capture processes. Time-resolved, multidimensional optical spectroscopies provide a key tool to investigate these processes. Although theory has the potential to decode these spectra in terms of the electronic and atomistic dynamics, the need for large numbers of excited-state electronic structure calculations severely limits first-principles predictions of multidimensional optical spectra for chromophores in the condensed phase. Here, we leverage the locality of chromophore excitations to develop machine learning models to predict the excited-state energy gap of chromophores in complex environments for efficiently constructing linear and multidimensional optical spectra. By analyzing the performance of these models, which span a hierarchy of physical approximations, across a range of chromophore-environment interaction strengths, we provide strategies for the construction of machine learning models that greatly accelerate the calculation of multidimensional optical spectra from first principles.

    View details for DOI 10.1021/acs.jpclett.0c02168

    View details for PubMedID 32808797