Nanoinformatics, and the big challenges for the science of small things
The combination of computational chemistry and computational materials science with
machine learning and artificial intelligence provides a powerful way of relating structural …
machine learning and artificial intelligence provides a powerful way of relating structural …
Using molecular embeddings in QSAR modeling: does it make a difference?
With the consolidation of deep learning in drug discovery, several novel algorithms for
learning molecular representations have been proposed. Despite the interest of the …
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
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 …
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 …
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 …
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 …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning
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 …
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
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 …
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) …
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
Single-atom catalysts (SACs) represent the ultimate size limit of nanoscale catalysts,
combining the advantages of homogeneous and heterogeneous catalysts. SACs have …
combining the advantages of homogeneous and heterogeneous catalysts. SACs have …