Using Machine Learning to make nanomaterials sustainable

Scott‐Fordsmand, Janeck J.; Amorim, Mónica J.B.

doi:10.1016/j.scitotenv.2022.160303

Cited by 28 publications

(14 citation statements)

References 260 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Even in our simulation studies with identical cohorts, identical model parameters, and identical covariates, we observed that there was significant variation in which covariates were weighted highly in the final model output. This highlights the need to carefully evaluate the results of the model and not rely on a single seed to set the training and test sets for machine-learning modeling to avoid potential pitfalls that stem from training-test bias [ 50 , 56 – 61 ]. While the only covariate represented in this discussion session is Angina, these findings were similar within the other accuracy metrics provided in Table 3 .…”

Section: Discussionmentioning

confidence: 99%

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang

2023

PLoS ONE

View full text Add to dashboard Cite

Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

show abstract

Section: Discussionmentioning

confidence: 99%

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…De fato, algoritmos são capazes de tratar uma grande quantidade de dados, possibilitando obter correlações entre a estrutura e composição dos nanomateriais e a predição de seus efeitos toxicológicos (relação estrutura-toxicidade) com base em bioindicadores e biomarcadores de exposição [47][48][49]. Neste ponto, o emprego de tecnologias ômicas de alto desempenho serão importantes para elucidar alvos bioquímicos-moleculares e alterações de vias metabólicas diretamente envolvidas na resposta aos nanomateriais [49].…”

Section: Informática Aplicada Em Nanossegurançaunclassified

“…A nomenclatura e ontologia para nanomateriais é um aspecto crítico; para suprir essa lacuna, um projeto vinculado ao Versailles Project on Advanced Materials and Standards (VAMAS) está em andamento [51]. Este projeto tem por objetivo estabelecer uma identificação única para cada tipo de nanomaterial seguindo recomendações da International Union of Pure and Applied Chemistry (IUPAC), facilitando assim, a anotação em bancos de dados estruturados e o desenvolvimento de modelos computacionais mais robustos que permitirão uma abordagem de sistemas de informação e ciência intensiva de dados capaz de identificar e predizer efeitos toxicológicos de nanomateriais e derivados, bem como seus potenciais riscos e impactos ambientais [47,48].…”

Section: Informática Aplicada Em Nanossegurançaunclassified

Nanossegurança para inovação sustentável: a avaliação da toxicidade e ciclo de vida de um nanoproduto é essencial para garantir que a sua produção e aplicações prosperem; sem provocar impactos negativos aos trabalhadores, consumidores e ao meio ambiente

Martinez

Côa

Knobel

2022

C&C

View full text Add to dashboard Cite

show abstract

“…[18][19][20] The implementation of ML methods in the environmental risk assessment of NMs in the realm of sustainability is also gaining considerable traction. [21] ML is a powerful tool for finding common patterns and trends in a given dataset, classifying the data, and predicting results based on the available data. Instead of using a human-developed algorithm to solve a problem, the machine uses statistical methods to find its own algorithm based on solutions to similar problems.…”

Section: Introductionmentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

show abstract

Using Machine Learning to make nanomaterials sustainable

Cited by 28 publications

References 260 publications

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Nanossegurança para inovação sustentável: a avaliação da toxicidade e ciclo de vida de um nanoproduto é essencial para garantir que a sua produção e aplicações prosperem; sem provocar impactos negativos aos trabalhadores, consumidores e ao meio ambiente

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Contact Info

Product

Resources

About