Download published papers and preprints

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 DiscoveryDevelopments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Feb 2024, Computer-Aided DesignA 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 ModelingQuantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling

Sep 2023, Materials Genome Engineering AdvancesToward learning steelmaking – A review on machine learning for basic oxygen furnace process

June 2023, Computational Materials ScienceMachine 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

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