Journal of physics. Condensed matter : an Institute of Physics journal
Jul 22, 2022
When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. We benchmark two other modalities of evolv...
Journal of physics. Condensed matter : an Institute of Physics journal
Dec 15, 2021
Despite the significant advancement of the data-driven studies for physical science, the textual data that are numerous in the literature are not fully embraced by the physics and materials community. In this manuscript, we successfully employ the na...
Journal of physics. Condensed matter : an Institute of Physics journal
Jul 2, 2018
In this work, we present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These s...
Journal of physics. Condensed matter : an Institute of Physics journal
Jan 31, 2018
In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches...