Accelerated automated screening of viscous graphene suspensions with various surfactants for optimal electrical conductivity

Abstract

Functional composite thin films have a wide variety of applications in flexible and/or electronic devices, telecommunications and multifunctional emerging coatings. Rapid screening of their properties is a challenging task, especially...

Method for Interfacing a Plate Reader Spectrometer Directly with an OT-2 Liquid Handling Robot

“…Fortunately, the OT-2 is open-source with publicly available code and hardware designs, 12 thus allowing researchers to adapt it for their unique research purposes. [13][14][15] For example, Ouyang et al developed a microscopy system that interfaces with the OT-2 to allow direct characterization of samples prepared by the liquid-handling system. 16 Herein, we present another adaption for the OT-2, instead allowing integration of spectroscopic characterization of samples.…”

NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces

“…If we could develop new machine learning approaches to chemical reactivity, we would be able to better tackle many fascinating but quite difficult chemical systems ranging from metal-organic frameworks for binding CO 2 from air or H 2 for hydrogen storage, mechanistic studies of enzymes that accelerate biological reactions, the reactive chemistry at the solidliquid interface in electrocatalysis, and developing new catalysts that are highly selective and which exhibit stereo-, regio-, and chemo-selectivity. [1][2][3][4] The modernization of machine learning as applied to the chemical sciences can be traced to the articial neural network (ANN) representation by Behler and Parrinello 5 to describe the high dimensional potential energy surfaces (PES) important to chemical reactivity. Their rst realization is that the intrinsic description of energies or forces that depend on Cartesian variables needs to be replaced by the use of localized Gaussian symmetry functions that invoke permutation, rotational, and translational invariance to data representations for learning potential energy surfaces.…”

“…If we could develop new machine learning approaches to chemical reactivity, we would be able to better tackle many fascinating but quite difficult chemical systems ranging from metal–organic frameworks for binding CO 2 from air or H 2 for hydrogen storage, mechanistic studies of enzymes that accelerate biological reactions, the reactive chemistry at the solid–liquid interface in electrocatalysis, and developing new catalysts that are highly selective and which exhibit stereo-, regio-, and chemo-selectivity. 1–4…”

Mapping Pareto Fronts for Efficient Multi-Objective Materials Discovery

“…(Zhang and Block, 2009;Mennen et al, 2019) There have been many successful applications of HTE, particularly in the single objective problem space alongside machine learning-assisted optimisation strategies. (Sun et al, , 2019Burger et al, 2020;Dave et al, 2020;Gongora et al, 2020;Langner et al, 2020;Shimizu et al, 2020;Bash et al, 2022; However, many real-world problems are more complex, specifically with multiple conflicting properties to be optimized, for example: strength vs ductility in metal alloys, (Li et al, 2016) device thickness vs fill factor in photovoltaics, (Ramirez et al, 2018) or selectivity vs current density in catalysts. In addition, such problems may include constraints that restrict the space of feasible solution.…”