Search

Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variables and their associated noise to predict the final target variable. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Both mixes also fulfill targets on the strength, density, and cost. The specified mixes are experimentally validated against their predictions. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering and beyond.

Abstract The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

Summary: Understanding the formulation and manufacturing parameters that lead to higher energy density and longevity is critical to designing energy-dense graphite electrodes for battery applications. A limited dataset that includes 27 different formulation, manufacturing protocols, and performance properties is reported. Input parameters from formulation and manufacturing are varied: slurry composition, mixing protocol, electrode coating gap size, drying temperature, coating speed, and calendering. Measurable outputs from the rheological characteristics, adhesion, and electrochemical testing are recorded. A database with the inputs and output parameters is populated and used to train an artificial intelligence model. Validation of the model is performed upon test data and an optimized electrode formulation and manufacturing process predicted. The electrode manufactured using the model process shows excellent cycle life and capacity agreement to prediction. The data model can be used to predict and design the formulation and manufacturing process to produce thick, high-coat-weight, graphite-based electrodes.

Abstract More accurate predictions of the biological properties of chemical compounds would guide the selection and design of new compounds in drug discovery and help to address the enormous cost and low success‐rate of pharmaceutical R&D. However, this domain presents a significant challenge for AI methods due to the sparsity of compound data and the noise inherent in results from biological experiments. In this paper, we demonstrate how data imputation using deep learning provides substantial improvements over quantitative structure‐activity relationship (QSAR) machine learning models that are widely applied in drug discovery. We present the largest‐to‐date successful application of deep‐learning imputation to datasets which are comparable in size to the corporate data repository of a pharmaceutical company (678 994 compounds by 1166 endpoints). We demonstrate this improvement for three areas of practical application linked to distinct use cases; (a) target activity data compiled from a range of drug discovery projects, (b) a high value and heterogeneous dataset covering complex absorption, distribution, metabolism, and elimination properties, and (c) high throughput screening data, testing the algorithm's limits on early stage noisy and very sparse data. Achieving median coefficients of determination, R2, of 0.69, 0.36, and 0.43, respectively, across these applications, the deep learning imputation method offers an unambiguous improvement over random forest QSAR methods, which achieve median R2 values of 0.28, 0.19, and 0.23, respectively. We also demonstrate that robust estimates of the uncertainties in the predicted values correlate strongly with the accuracies in prediction, enabling greater confidence in decision‐making based on the imputed values.

Inconsistent therapeutic efficacy of mesenchymal stem cells (MSCs) in regenerative medicine has been documented in many clinical trials. Precise prediction on the therapeutic outcome of a MSC therapy based on the patient's conditions would provide valuable references for clinicians to decide the treatment strategies. In this article, we performed a meta-analysis on MSC therapies for cartilage repair using machine learning. A small database was generated from published in vivo and clinical studies. The unique features of our neural network model in handling missing data and calculating prediction uncertainty enabled precise prediction of post-treatment cartilage repair scores with coefficient of determination of 0.637 ± 0.005. From this model, we identified defect area percentage, defect depth percentage, implantation cell number, body weight, tissue source, and the type of cartilage damage as critical properties that significant impact cartilage repair. A dosage of 17 - 25 million MSCs was found to achieve optimal cartilage repair. Further, critical thresholds at 6% and 64% of cartilage damage in area, and 22% and 56% in depth were predicted to significantly compromise on the efficacy of MSC therapy. This study, for the first time, demonstrated machine learning of patient-specific cartilage repair post MSC therapy. This approach can be applied to identify and investigate more critical properties involved in MSC-induced cartilage repair, and adapted for other clinical indications.