Impact of Physical/Chemical Properties of Volcanic Ash-Derived Soils on Mechanisms Involved during Sorption of Ionisable and Non-Ionisable Herbicides

Volcanic ash-derived soils (VADSs) are of great importance in the agricultural economy of several emerging and developing countries. The surface-charge amphoteric characteristics will confer physical/chemical properties absolutely different to constant-charge soils. This surface reactivity will confer to them a particular behaviour in relation to the herbicide sorption, representing an environmental substrate that may become polluted over time due to intensive agronomic uses. Sorption is a key parameter to evaluate the fate and behaviour of herbicides in volcanic soils. Sorption type and kinetic sorption models are also necessary in order to develop and validate QSAR models to predict pesticide sorption on volcanic soils to prevent potential contamination of water resources. The use of solute sorption mechanism models and QSAR models for pesticide sorption in soils has contributed to a better understanding of the behaviour of pesticides on volcanic soils. This chapter is divided into five sections: Physical/chemical properties of volcanic ash-derived soils; Ionisable and non-ionisable herbicides’ fate and behaviour in soil; Kinetic sorption: mechanisms involved during sorption of ionisable and non-ionisable herbicides on VADS; Sorption of ionisable and non-ionisable herbicides on VADS; and Physical/chemical properties in QSAR models: a mechanistic interpretation

"Particle Informatics": Advancing Our Understanding of Particle Properties through Digital Design

We introduce a combination of existing and novel approaches to the assessment and prediction of particle properties intrinsic to the formulation and manufacture of pharmaceuticals. Naturally following on from established solid form informatics methods, we return to the drug lamotrigine, re-evaluating its context in the Cambridge Structural Database (CSD). We then apply predictive digital design tools built around the CSD-System suite of software, including Synthonic Engineering methods that focus on intermolecular interaction energies, to analyze and understand important particle properties and their effects on several key stages of pharmaceutical manufacturing. We present a new, robust workflow that brings these approaches together to build on the knowledge gained from each step and explain how this knowledge can be combined to provide resolutions at decision points encountered during formulation design and manufacturing processes

Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century