Combinatorial screening for new materials in unconstrained composition space with machine learning

With the growth of chemical data, computation power and algorithms, machine learning-assisted high-throughput virtual screening (ML-assisted HTVS) is revolutionizing the research paradigm of new materials. Herein, a combined ML-assisted HTVS...

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Machine learning and molecular dynamics simulations assisted evolutionary design and discovery pipeline to screen the efficient small molecule acceptors for PTB7-Th based organic solar cells with over 15% efficiency

Journal of Materials Chemistry A ◽

10.1039/d1ta09762h ◽

2022 ◽

Author(s):

Jin-Liang Wang ◽

Asif Mahmood ◽

Ahmad Irfan

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Solar Cells ◽

Molecular Dynamics Simulations ◽

Small Molecule ◽

Organic Solar Cells ◽

Evolutionary Design ◽

New Materials ◽

Dynamics Simulations ◽

Discovery Pipeline

Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without experimentation to reduce the...

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Applying machine learning to accelerate new materials development

SCIENTIA SINICA Physica, Mechanica & Astronomica ◽

10.1360/sspma2018-00073 ◽

2018 ◽

Vol 48 (10) ◽

pp. 107001 ◽

Cited By ~ 2

Author(s):

Wei WU ◽

Qiang SUN

Keyword(s):

Machine Learning ◽

New Materials ◽

Materials Development

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Determining Usefulness of Machine Learning in Materials Discovery Using Simulated Research Landscapes

Physical Chemistry Chemical Physics ◽

10.1039/d1cp01761f ◽

2021 ◽

Author(s):

Marcos del Cueto ◽

Alessandro Troisi

Keyword(s):

Machine Learning ◽

Experimental Data ◽

Data Acquisition ◽

Prediction Accuracy ◽

New Materials

When existing experimental data are combined with machine learning (ML) to predict the performance of new materials, the data acquisition bias determines ML usefulness and the prediction accuracy. In this...

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Inverse molecular design using machine learning: Generative models for matter engineering

Science ◽

10.1126/science.aat2663 ◽

2018 ◽

Vol 361 (6400) ◽

pp. 360-365 ◽

Cited By ~ 297

Author(s):

Benjamin Sanchez-Lengeling ◽

Alán Aspuru-Guzik

Keyword(s):

Machine Learning ◽

Technological Progress ◽

Rational Design ◽

Molecular Design ◽

Generative Models ◽

New Materials ◽

Large Space ◽

Starting Point ◽

Rapid Pace ◽

Synthetic Routes

The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

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Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

10.26434/chemrxiv.12436079.v1 ◽

2020 ◽

Author(s):

Kate Higgins ◽

Sai Mani Valleti ◽

Maxim Ziatdinov ◽

Sergei Kalinin ◽

Mahshid Ahmadi

Keyword(s):

Machine Learning ◽

Optimization Methods ◽

Combinatorial Synthesis ◽

Ambient Conditions ◽

Term Stability ◽

Long Term Stability ◽

Composition Space ◽

Materials Research ◽

The Stability

Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for the next-generation solar cells; however, issues regarding long-term stability still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: MAxFAyCs1-x-yPbBr3, MAxFAyCs1-x-yPbI3, (CsxFAyMA1-x-yPb(Brx+yI1-x-y)3) and (CsxMAyFA1-x-yPb(Ix+yBr1-x-y)3). We also develop a machine learning-based workflow to quantify the evolution of each system as a function of composition based on overall changes in photoluminescence spectra, as well as specific peak positions and intensities. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.

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Selecting Nanoparticle Properties to Mitigate Risks to Workers and the Public: A Machine Learning Modeling Framework to Compare Pulmonary Toxicity Risks of Nanomaterials

Volume 15: Safety, Reliability and Risk; Virtual Podium (Posters) ◽

10.1115/imece2013-62687 ◽

2013 ◽

Cited By ~ 2

Author(s):

Jeremy M. Gernand ◽

Elizabeth A. Casman

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Pulmonary Toxicity ◽

Assessment Process ◽

Complex Data ◽

Negative Consequences ◽

New Materials ◽

Modeling Framework ◽

Toxic Response ◽

Consumer Safety

Due to their size and unique chemical properties, nanomaterials have the potential to interact with living organisms in novel ways, leading to a spectrum of negative consequences. Though a relatively new materials science, already nanomaterial variants in the process of becoming too numerous to be screened for toxicity individually by traditional and expensive animal testing. As with conventional pollutants, the resulting backlog of untested new materials means that interim industry and regulatory risk management measures may be mismatched to the actual risk. The ability to minimize toxicity risk from a nanomaterial during the product or system design phase would simplify the risk assessment process and contribute to increased worker and consumer safety. Some attempts to address this problem have been made, primarily analyzing data from in vitro experiments, which are of limited predictive value for the effects on whole organisms. The existing data on the toxicity of inhaled nanomaterials in animal models is sparse in comparison to the number of potential factors that may contribute to or aggravate nanomaterial toxicity, limiting the power of conventional statistical analysis to detect property/toxicity relationships. This situation is exacerbated by the fact that exhaustive chemical and physical characterization of all nanomaterial attributes in these studies is rare, due to resource or equipment constraints and dissimilar investigator priorities. This paper presents risk assessment models developed through a meta-analysis of in vivo nanomaterial rodent-inhalational toxicity studies. We apply machine learning techniques including regression trees and the related ensemble method, random forests in order to determine the relative contribution of different physical and chemical attributes on observed toxicity. These methods permit the use of data records with missing information without substituting presumed values and can reveal complex data relationships even in nonlinear contexts or conditional situations. Based on this analysis, we present a predictive risk model for the severity of inhaled nanomaterial toxicity based on a given set of nanomaterial attributes. This model reveals the anticipated change in the expected toxic response to choices of nanomaterial design (such as physical dimensions or chemical makeup). This methodology is intended to aid nanomaterial designers in identifying nanomaterial attributes that contribute to toxicity, giving them the opportunity to substitute safer variants while continuing to meet functional objectives. Findings from this analysis indicate that carbon nanotube (CNT) impurities explain at most 30% of the variance pulmonary toxicity as measured by polymorphonuclear neutrophils (PMN) count. Titanium dioxide nanoparticle size and aggregation affected the observed toxic response by less than ±10%. Difference in observed effects for a group of metal oxide nanoparticle associated with differences in Gibbs Free Energy on lactate dehydrogenase (LDH) concentrations amount to only 4% to the total variance. Other chemical descriptors of metal oxides were unimportant.

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Advanced Materials Based on Nanosized Hydroxyapatite

Molecules ◽

10.3390/molecules26113190 ◽

2021 ◽

Vol 26 (11) ◽

pp. 3190

Author(s):

Ramón Rial ◽

Michael González-Durruthy ◽

Zhen Liu ◽

Juan M. Ruso

Keyword(s):

Machine Learning ◽

Advanced Materials ◽

Computational Techniques ◽

New Materials ◽

Research Directions ◽

Advantages And Disadvantages ◽

Technological Advances ◽

Nanosized Hydroxyapatite ◽

New Research ◽

The Impact

The development of new materials based on hydroxyapatite has undergone a great evolution in recent decades due to technological advances and development of computational techniques. The focus of this review is the various attempts to improve new hydroxyapatite-based materials. First, we comment on the most used processing routes, highlighting their advantages and disadvantages. We will now focus on other routes, less common due to their specificity and/or recent development. We also include a block dedicated to the impact of computational techniques in the development of these new systems, including: QSAR, DFT, Finite Elements of Machine Learning. In the following part we focus on the most innovative applications of these materials, ranging from medicine to new disciplines such as catalysis, environment, filtration, or energy. The review concludes with an outlook for possible new research directions.

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