Convergence Acceleration in Machine Learning Potentials for Atomistic Simulations

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy...

A sparse modeling for small data: Case studies in controlled syntheses of 2D materials

Data-scientific approaches have permeated in chemistry and materials science. In general, these approaches are not easily applied to small data, such as experimental data in laboratories. Our group has focused...

Machine Learning Enhanced Spectroscopic Analysis: Towards Autonomous Chemical Mixture Characterization for Rapid Process Optimization

Autonomous chemical process development and optimization methods use algorithms to explore the operating parameter space based on feedback from experimentally determined exit stream compositions. Measuring the compositions of multicomponent streams...

Performance of chemical structure string representations for chemical image recognition using transformers

The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful...

ChemSpaX: Exploration of chemical space by automated functionalization of molecular scaffold

Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents...

Predicting 3D shapes, masks, and properties of materials inside transparent containers, using the TransProteus CGI dataset

We present TransProteus, a dataset, and methods for predicting the 3D structure and properties of materials inside transparent vessels from a single image. Manipulating materials in containers is essential in...