Nanoinformatics, and the big challenges for the science of small things

AS Barnard, B Motevalli, AJ Parker, JM Fischer… - Nanoscale, 2019 - pubs.rsc.org
The combination of computational chemistry and computational materials science with
machine learning and artificial intelligence provides a powerful way of relating structural …

Using molecular embeddings in QSAR modeling: does it make a difference?

MV Sabando, I Ponzoni, EE Milios… - Briefings in …, 2022 - academic.oup.com
With the consolidation of deep learning in drug discovery, several novel algorithms for
learning molecular representations have been proposed. Despite the interest of the …

[HTML][HTML] A machine learning approach for accelerated design of magnesium alloys. Part A: Alloy data and property space

M Ghorbani, M Boley, PNH Nakashima… - Journal of Magnesium and …, 2023 - Elsevier
Typically, magnesium alloys have been designed using a so-called hill-climbing approach,
with rather incremental advances over the past century. Iterative and incremental alloy …

“Inverting” X-ray absorption spectra of catalysts by machine learning in search for activity descriptors

J Timoshenko, AI Frenkel - Acs Catalysis, 2019 - ACS Publications
The rapid growth of methods emerging in the past decade for synthesis of “designer”
catalysts—ranging from the size and shape-selected nanoparticles to mass-selected …

Crystalgan: learning to discover crystallographic structures with generative adversarial networks

A Nouira, N Sokolovska, JC Crivello - arXiv preprint arXiv:1810.11203, 2018 - arxiv.org
Our main motivation is to propose an efficient approach to generate novel multi-element
stable chemical compounds that can be used in real world applications. This task can be …

From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …

Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning

A Tiihonen, SJ Cox-Vazquez, Q Liang… - Journal of the …, 2021 - ACS Publications
New antibiotics are needed to battle growing antibiotic resistance, but the development
process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in …

Convolutional neural networks for crystal material property prediction using hybrid orbital-field matrix and magpie descriptors

Z Cao, Y Dan, Z Xiong, C Niu, X Li, S Qian, J Hu - Crystals, 2019 - mdpi.com
Computational prediction of crystal materials properties can help to do large-scale in-silicon
screening. Recent studies of material informatics have focused on expert design of multi …

Band nn: A deep learning framework for energy prediction and geometry optimization of organic small molecules

S Laghuvarapu, Y Pathak… - Journal of computational …, 2020 - Wiley Online Library
Recent advances in artificial intelligence along with the development of large data sets of
energies calculated using quantum mechanical (QM)/density functional theory (DFT) …

Progress and challenges in structural, in situ and operando characterization of single-atom catalysts by X-ray based synchrotron radiation techniques

Y Liu, X Su, J Ding, J Zhou, Z Liu, X Wei… - Chemical Society …, 2024 - pubs.rsc.org
Single-atom catalysts (SACs) represent the ultimate size limit of nanoscale catalysts,
combining the advantages of homogeneous and heterogeneous catalysts. SACs have …