Search query: industrial chemistry machine learning

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abstract: We describe five specific guiding principles for applications of machine learning (ML) to problems in chemistry and material sciences, using data from both experiments and simulations. The principles are the following: 1. Use ML for interpolation but with care for extrapolation; 2. Ensure consistency between sources of data and the targeted application; 3. Correlation is not causation; 4. Optimize information extraction when using ML; 5. Combine different methods, including experiments, theory, and computing to provide a larger window of applications. These principles were developed based on the applications that the authors have been actively involved in, in both industrial and academic settings. Each of these guiding principles is illustrated, using examples from biology, chemistry, physics, engineering, or material science. Examples include Mendeleev's periodic table, estimation of interface adhesion in semiconductor materials, measurements in chemical analysis for cancer chemistry, singularities in evolutionary biology, and the development of faster quantum chemistry methods. The use of specific examples, in turn, will help illustrate the basic premise behind each of the principles. We believe that these unique perspectives highlight potential fallacies in applying these techniques broadly to all problems in natural sciences and engineering, without appropriate bounding of accuracy and precision, especially in areas related to the chemical and materials sciences.
author_names: Sushant Shankar

Richard N Zare
title: Chapter 20. A Few Guiding Principles for Practical Applications of Machine Learning to Chemistry and Materials
year: 2020

abstract: Bond dissociation energy (BDE) an indicator of the strength of chemical bonds, exhibits great potential for evaluating and screening high performance materials and catalysts, which are of critical importance in industrial applications. However, the measurement or computation of BDE via conventional experimental or theoretical methods is usually costly and involved, substantially preventing BDE from being applied to large scale and high throughput studies. Therefore, a potentially more efficient approach for estimating BDE is highly desirable. To this end, we have combined first principles calculations and machine learning techniques, including neural networks and random forest, to explore the inner relationships between carbonyl structure and its BDE. Results show that machine learning can not only effectively reproduce the computed BDEs of carbonyls, but also in turn serve as guidance for the rational design of carbonyl structure aimed at optimizing performance.
author_names: Hai-shan Yu

Xijun Wang

Jinxiao Zhang

Sheng Ye

Yan Huang

Yi Luo

Edward H Sharman

Shilu Chen

Jun Jiang
title: Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls.
venue: The journal of physical chemistry. A
year: 2020

abstract: In their research, "Combining automated microfluidic experimentation with machine learning for efficient polymerization design," published in Nature Machine Intelligence, the collaborators, including doctoral student Benjamin Rizkin, employed a custom designed, rapidly prototyped microreactor in conjunction with automation and in situ infrared thermography to study exothermic (heat generating) polymerization reactions that are notoriously difficult to control when limited experimental kinetic data are available. By pairing efficient microfluidic technology with machine learning algorithms to obtain high fidelity datasets based on minimal iterations, they were able to reduce chemical waste by two orders of magnitude and catalytic discovery from weeks to hours.
title: Researchers design intelligent microsystem for faster, more sustainable industrial chemistry
year: 2020

abstract: Abstract We present a study for predicting 15 molecular properties through the combination of a quantum mechanical database, taken from the quantum chemistry QM9 database, and feed forward deep neural networks approaches. The aim of the work is to show that the combination of a priori computed ab initio information and machine learning can support experimental work to speed up the discovery and formulation of novel compounds. The importance of this work also relies on the fact that through this computer aided molecular design approach no approximate or heuristic contribution methods are needed for physical and thermodynamic properties information. We show that using proper hyper parameters tuning of deep neural networks is possible, even with modest computational resources, to design the chemical structure of compounds matching target molecular properties making them feasible for practical industrial applications in diverse areas such as energy, water, food, health and transport economical sectors.
author_names: Darinel Valencia-Marquez

Antonio Flores-Tlacuahuac
title: Improving molecular design through a machine learning approach
year: 2020

abstract: A machine learning technique, namely support vector regression, is implemented to enhance single walled carbon nanotube (SWCNT) thin film performance for transparent and conducting applications. We collected a comprehensive dataset describing the influence of synthesis parameters (temperature and CO2 concentration) on the equivalent sheet resistance (at 90% transmittance in the visible light range) for SWCNT films obtained by a semi industrial aerosol (floating catalyst) CVD with CO as a carbon source and ferrocene as a catalyst precursor. The predictive model trained on the dataset shows principal applicability of the method for refining synthesis conditions towards advanced optoelectronic performance of multi parameter processes such as nanotube growth. Further doping of the improved carbon nanotube films with HAuCl4 results in the equivalent sheet resistance of 39 O/ the lowest value achieved so far for SWCNT films.
author_names: Eldar M Khabushev

Dmitry V Krasnikov

Orysia T Zaremba

Alexey P Tsapenko

Anastasia E Goldt

Albert G Nasibulin
title: Machine Learning for Tailoring Optoelectronic Properties of Single Walled Carbon Nanotube Films.
venue: The journal of physical chemistry letters
year: 2019

abstract: Finding the `ideal" catalyst is a matter of great interest in the communities of chemists and material scientists, partly because of its wide spectrum of industrial applications. Information regarding a physical parameter termed `adsorption energy" which dictates the degrees of adhesion of an adsorbate on a substrate is a primary requirement in selecting the catalyst for catalytic reactions. Both experiments and \textit{in silico} modelling are extensively being used in estimating the adsorption energies, both of which are \textit{Edisonian} approach and demands plenty of resources and are time consuming. In this paper, employing a data mining approach, we predict the adsorption energies of mono atomic and di atomic gases on the surfaces of many transition metals (TMs) in no times. With less than a set of 10 simple atomic features, our predictions of the adsorption energies are within a root mean squared error (RMSE) of 0.4 eV with the quantum many body perturbation theory estimates, a computationally expensive method with a good experimental agreement. Based on the important features obtained from machine learning models, we construct a set of mathematical equation using compressed sensing technique to calculate adsorption energy. We also shows that the RMSE can be further minimized up to 0.10 eV by using the pre computed adsorption energies obtained with conventional exchange and correlation (XC) functional by a new set of scaling relations.
author_names: Sanjay K Nayak

Satadeep Bhattacharjee

Jung-Hae Choi
title: Machine Learning and Scaling Laws for Prediction of Accurate Adsorption Energy.
venue: The journal of physical chemistry. A
year: 2019

abstract: This five day workshop consisted of a two day school introducing machine learning techniques for materials science with a mixture of presentations and hands on tutorials, followed by a three day conference made up of invited presentations, contributed talks, a poster session and a number of panel discussions. The event drew participants from primarily chemistry, physics and computer science backgrounds, all with a focus on applying machine learning to materials discovery or understanding. Some of the invited talks were given by industrial participants, complementing the academic research presenting, and offering a different view. Coffee breaks and a conference dinner/poster session allowed plenty of time for networking amongst this diverse group.
author_names: James Cumby
title: Machine Learning for Materials Science Report 2019
year: 2019

abstract: Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.
author_names: Jiayan Xu

Peijun Hu
title: Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis.
venue: Physical chemistry chemical physics PCCP
year: 2021

abstract: Quantitative analysis of complex mixtures is a great challenge for spectral analysis. Bisphenol A (BpA) is a chemical predominantly used in manufacturing and is being replaced by other analogs due to its potential toxicity. Reliability methods is hence crucial for identification and quantification of bisphenol mixtures. In this study we present an attractive strategy for composition determination of BpA incorporated in its analogue mixtures. Terahertz spectra of four bisphenol components are analyzed using machine learning method (SVR) to learn the underlying model of the frequency against the target concentration of BpA in mixtures. The learned mode predicts the concentrations of the unknown samples with decision coefficient R2 0.98. Absorption spectra for bisphenols mixtures were successfully reconstructed by a hold out validation scheme. The results indicate the terahertz spectroscopy in combination with SVR is robust and accurate in mixture quantitative analysis and should play a significant role for industrial applications in the future.
author_names: Yiwen Sun

Jialiang Huang

Lianxin Shan

Shuting Fan

Zexuan Zhu

Xudong Liu
title: Quantitative analysis of bisphenol analogue mixtures by terahertz spectroscopy using machine learning method.
venue: Food chemistry
year: 2021

abstract: In the past decades the automation industry was evolving at modest pace, mainly driven by innovations in mechanical design and utilizing basic principles from physics or chemistry. What was already known was continuously improved and aspects like requirements for environmental and human protection increased over the years and were more and more formalized leading i.e. to complex procedures for safety designs. The innovations of the IT world which continuously gained speed especially over the last decade were taken over rather reluctantly. But recent trends show a growing push of IT technologies into every field of industrial application, frequently named as industry 4.0 or 4th industrial revolution. We will look at opportunities and threads created by the technological changes, touch preconditions for success and pitfalls for failure. Analyzing the past trends of both IT and industrial automation will allow us for some astonishing predictions for the next decade.
author_names: Attila Michael Bilgic
title: The Disruptive Power of Machine Learning and OoT in Automation Industry
venue: 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)
year: 2018