We are pleased to provide access to all of the following peer-reviewed publications, which are authored or co-authored by Intellegens team members. Click below to access a pre-print or, for open access publications, to view or or download a copy of the published paper. Papers are listed in chronological order. If you have trouble locating the paper of interest, you may want to return to the main contents page for publications and check its publication date.
Apr 2024, Digital Discovery – Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Feb 2024, Computer-Aided Design – A compact yet flexible design space for large-scale nonperiodic 3D woven composites based on a weighted game for generating candidate tow architectures
Dec 2023, J Chem Inf and Modeling – Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling
Sep 2023, Materials Genome Engineering Advances – Toward learning steelmaking – A review on machine learning for basic oxygen furnace process
June 2023, Computational Materials Science – Machine learning superalloy microchemistry and creep strength from physical descriptors
Apr 2023, Data-Centric Engineering – Probabilistic selection of and design of concrete using machine learning
Oct 2022, Data-Centric Engineering – Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification
Sep 2022, Applied Intelligence – Unveil the unseen: Exploit information hidden in noise
Jul 2022, NASA Technical Memorandum – Design of Materials with Alchemite™
Apr 2022, Molecular Pharmaceutics – Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure
Dec 2021, Cell Reports Physical Science – Formulation and manufacturing optimisation of lithium-ion graphite-based electrodes via machine learning
Nov 2021, J Comp-Aided Mol Des – Imputation of sensory properties using deep learning
Nov 2021, J Med Chem – An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials
Aug 2021, Nature Scientific Data – OPTIMADE, An API for exchanging materials data
July 2021, Johnson Matthey Technology Review – Accelerating the design of automotive catalyst products using machine learning
Jun 2021, Applied AI Letters – Deep imputation on large-scale drug discovery data
Jul 2020, J Phys Chem – Enhancing non-equilibrium molecular dynamics to model viscosity and density in linear and light branched alkanes
Jun 2020, JCIM – Practical applications of deep learning to impute heterogeneous drug discovery data
Apr 2020, Future Drug Discovery – Imputation versus prediction: applications in machine learning for drug discovery
March 2020, Nature Machine Intelligence – Predicting the State of Charge and Health of Batteries using Data-Driven Machine Learning
Aug 2019, Fluid Phase Equilibria – Predicting physical properties of alkanes with neural networks
Jul 2019, Matter – Structure-mechanical stability relations of metal-organic frameworks via machine learning
Apr 2019, Materials & Design – Probabilistic neural network identification of an alloy for direct laser deposition
Feb 2019, J. Chem. Inf. Model. – Imputation of assay bioactivity data using deep learning
May 2018, Computational Materials Science – Materials data validation and imputation with an artificial neural network
Mar 2018, Scripta Materialia – Probabilistic design of a molybdenum-base alloy using a neural network
Oct 2017, Materials & Design – Design of a nickel-base superalloy using a neural network
