jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marcus M. Noack | Gregory S. Doerk | Ruipeng Li | Jason K. Streit | Richard A. Vaia | Kevin G. Yager | Masafumi Fukuto
Source: arXiv (Cornell University) (preprint, 2020-06-03)
Field: Materials Science — Materials Chemistry

Autonomous Materials Discovery Driven by Gaussian Process Regression\n with Inhomogeneous Measurement Noise and Anisotropic Kernels A majority of experimental disciplines face the challenge of exploring large\nand high-dimensional parameter spaces in search of new scientific discoveries.\nMaterials science is no exception; the wide variety of synthesis, processing,\nand environmental conditions that influence material properties gives rise to\nparticularly vast parameter spaces. Recent advances have led to an increase in\nefficiency of materials discovery by increasingly automating the exploration\nprocesses. Methods for autonomous experimentation have become more\nsophisticated recently, allowing for multi-dimensional parameter spaces to be\nexplored efficiently and with minimal human intervention, thereby liberating\nthe scientists to focus on interpretations and big-picture decisions. Gaussian\nprocess regression (GPR) techniques have emerged as the method of choice for\nsteering many classes of experiments. We have recently demonstrated the\npositive impact of GPR-driven decision-making algorithms on autonomously\nsteering experiments at a synchrotron beamline. However, due to the complexity\nof the experiments, GPR often cannot be used in its most basic form, but rather\nhas to be tuned to account for the special requirements of the experiments. Two\nrequirements seem to be of particular importance, namely inhomogeneous\nmeasurement noise (input dependent or non-i.i.d.) and anisotropic kernel\nfunctions, which are the two concepts that we tackle in this paper. Our\nsynthetic and experimental tests demonstrate the importance of both concepts\nfor experiments in materials science and the benefits that result from\nincluding them in the autonomous decision-making process.\n Computer science ; Noise (video) ; Process (computing) ; Gaussian process ; Kriging ; Variety (cybernetics) ; Artificial intelligence ; Machine learning ; Gaussian ; Data science ; Physics Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Molecular Biology Techniques and Applications
Authors: Gabriel dos Passos Gomes | Robert Pollice | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-08-12)
Field: Materials Science — Materials Chemistry

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Homogeneous ; Catalysis ; Task (project management) ; Artificial intelligence ; Chemistry ; Field (mathematics) ; Computer science ; Nanotechnology ; Management ; Biochemistry ; Mathematics ; Economics ; Materials science Machine Learning in Materials Science
Authors: Nathaniel H. Park | Dmitry Yu. Zubarev | James L. Hedrick | Vivien Kiyek | Christiaan Corbet | Simon Lottier
Source: ChemRxiv (preprint, 2020-08-03)
Field: Materials Science — Materials Chemistry

A Recommender System for Inverse Design of Polycarbonates and Polyesters The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class. Polycarbonate ; Polyester ; Context (archaeology) ; Polymerization ; Timeline ; Dispersity ; Computer science ; Inverse ; Monomer ; Convergence (economics) ; Class (philosophy) ; Polymer ; Materials science ; Polymer science ; Artificial intelligence ; Polymer chemistry ; Mathematics ; Composite material ; Geology Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Florian Häse | Teresa Tamayo-Mendoza | Carmen Boixo | Jonathan Romero | Loı̈c M. Roch | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-04-09)
Field: Chemistry — Physical and Theoretical Chemistry

Autonomous Titration for Chemistry Classrooms: Preparing Students for Digitized Chemistry Laboratories &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;The digitalization of the economy is one of the drivers of the fourth industrial revolution. This trend is already heavily permeating biology laboratories and rapidly moving into chemistry as well. Notably, automated laboratories enhance process quality and intensification while freeing researchers from repetitive tasks. With these societal changes in place, students need to be prepared for the advanced digitization of chemistry and science by teaching fundamental chemistry concepts in combination with emerging Industry 4.0 technologies, including programming and automation. We describe an undergraduate classroom exercise at the interface of chemistry, computer science and engineering based on the development of an autonomous titration platform. Following an inquiry learning ansatz, the exercise focuses on standard titration experiments which are first executed manually, then automatically and finally in full autonomy by a student-designed robotic platform. We demonstrate that the exercise introduced in this work enables students to learn fundamental concepts in analytical chemistry, naturally integrates basic aspects of programming and automation, and as a consequence promotes and reinforces the detailed understanding of experimental processes and measurements. The exercise is designed in a collaborative active learning framework to encourage complex critical thinking and creative problem solving and thus prepares students for the next-generation chemistry laboratories. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Chemistry ; Automation ; Computer science ; Process (computing) ; Autonomy ; Mathematics education ; Nanotechnology ; Engineering ; Multimedia ; Mathematics ; Political science ; Mechanical engineering ; Materials science Various Chemistry Research Topics ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electrochemical sensors and biosensors
Authors: Hiroki Sugisawa | Tomonori Ida | Roman V. Krems
Source: arXiv (Cornell University) (preprint, 2020-01-20)
Field: Materials Science — Materials Chemistry

Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer The goal of the present work is to obtain accurate potential energy surfaces (PES) for high-dimensional molecular systems with a small number of ${\it ab}$ ${\it initio}$ calculations in a system-agnostic way. We use probabilistic modeling based on Gaussian processes (GPs). We illustrate that it is possible to build an accurate GP model of a 51-dimensional PES based on $5000$ randomly distributed ${\it ab}$ ${\it initio}$ calculations with a global accuracy of $&lt; 0.2$ kcal/mol. Our approach uses GP models with composite kernels designed to enhance the Bayesian information content and represents the global PES as a sum of a full-dimensional GP and several GP models for molecular fragments of lower dimensionality. We demonstrate the potency of these algorithms by constructing the global PES for the protonated imidazole dimer, a molecular system with $19$ atoms. We illustrate that GP models thus constructed can extrapolate the PES from low energies ($&lt; 10,000$ cm$^{-1}$), yielding a PES at high energies ($&gt; 20,000$ cm$^{-1}$). This opens the prospect for new applications of GPs, such as mapping out phase transitions by extrapolation or accelerating Bayesian optimization, for high-dimensional physics and chemistry problems with a restricted number of inputs, i.e. for high-dimensional problems where obtaining training data is very difficult. Extrapolation ; Potential energy surface ; Ab initio ; Gaussian process ; Statistical physics ; Curse of dimensionality ; Gaussian ; Computer science ; Probabilistic logic ; Protonation ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Machine learning ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Thomas D. Swinburne | Danny Pérez
Source: npj Computational Materials (preprint, 2020-12-11)
Field: Materials Science — Materials Chemistry

Automated calculation and convergence of defect transport tensors Abstract Defect diffusion is a key process in materials science and catalysis, but as migration mechanisms are often too complex to enumerate a priori, calculation of transport tensors typically have no measure of convergence and require significant end-user intervention. These two bottlenecks prevent high-throughput implementations essential to propagate model-form uncertainty from interatomic interactions to predictive simulations. In order to address these issues, we extend a massively parallel accelerated sampling scheme, autonomously controlled by Bayesian estimators of statewide sampling completeness, to build atomistic kinetic Monte Carlo models on a state-space irreducible under exchange and space group symmetries. Focusing on isolated defects, we derive analytic expressions for drift and diffusion coefficients, providing a convergence metric by calculating the Kullback–Leibler divergence across the ensemble of diffusion processes consistent with the sampling uncertainty. The autonomy and efficacy of the method is demonstrated on surface trimers in tungsten and Hexa-interstitials in magnesium oxide, both of which exhibit complex, correlated migration mechanisms. Convergence (economics) ; Estimator ; Kinetic Monte Carlo ; Computer science ; Statistical physics ; Monte Carlo method ; Massively parallel ; Divergence (linguistics) ; Metric (unit) ; Mathematical optimization ; Physics ; Mathematics ; Parallel computing ; Engineering Machine Learning in Materials Science ; Nuclear reactor physics and engineering ; Nuclear Materials and Properties
Authors: Mark T. Schnepper
Source: Journal of International Crisis and Risk Communication Research (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Non-Linear Inverse Liquid-Solid Chromatography as a Methodology to Characterize Drug Concentration Losses to Polymeric Materials used in Body-on-a-Chip Devices for Drug Discovery Body-on-a-chip and human-on-a-chip systems are currently being used to augment and could eventually replace animal models in drug discovery and basic biological research. However, hydrophobic molecules, especially therapeutic compounds, tend to adsorb to the polymer materials used to create these microfluidic platforms, which may distort the dose-response curves that feed into Pharmacokinetic/Pharmacodynamic (PK/PD) models which translate preclinical data into predictions of clinical outcomes. Adsorption of hydrophobic molecules to these polymer materials needs better characterization. Inverse Liquid-Solid Chromatography paired with a numerical optimization based on the Langmuir model of adsorption was used to characterize the adsorption isotherm parameters of selected drugs to polydimethylsiloxane (PDMS) and polymethylmethacrylate (PMMA), polymers commonly used in these platforms after extensive modification to an existing HPLC-MS instrument. Surface modification by organosilanes is one method being explored to modify PDMS, but the effect of organosilanes on drug adsorption isotherms are not well characterized. We utilized Inverse Liquid-Solid Chromatography (ILC) to characterize the adsorption parameters of the selected drugs with native PDMS and organosilane-modified (fluoropolymer (13F) and polyethylene glycol (PEG)) PDMS surfaces to correlate the modifications to changes in drug adsorption. We found that the organosilane modifications significantly changed the energy of adsorption. The adsorption isotherms were then compared against concentration measurements of drugs recirculated in these platforms. It was found that the adsorption alone does not account for the drug concentration losses, which was expected as the drugs will diffuse into the bulk of the material. Organosilane surface modifications were successfully made to PDMS parts but were not fully adequate in preventing concentration losses, also due to diffusion into the bulk. Future work will characterize the diffusion and be integrated with the work presented in this dissertation to create a larger model of drug adsorption and diffusion which drives the concentration losses. This research establishes a foundation for a new approach whereby quantifying drug or drug candidate interactions before system dosing and including this data in the PK/PD models, that polymers used in these platforms need not be limited to "less-adsorbing" materials. Drug ; Chromatography ; Materials science ; Drug discovery ; Chemistry ; Medicine ; Pharmacology Analytical Chemistry and Chromatography ; Protein purification and stability
Authors: Hector Medina | Jonathan Polk
Source: 2020 ASEE Virtual Annual Conference Content Access Proceedings (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Repurposing a Nuclear Integrated System Test Facility for Engineering Education Nuclear energy (NE) represents more than 60% of the clean energy in the United States. Due to the great advantage that NE offers, it is important that more education that includes practical experience be made available to both undergraduate as well as graduate students. Furthermore, with the arrival of Small Modular Reactors (SMR) as a commercially available product, it is imperative that educational programs provide hands-on experience that include operations of those complex systems. However, the insurmountable costs and lengthy regulating processes render the aforementioned type of projects unfeasible. Modular design ; Mechatronics ; Curriculum ; Engineering education ; Engineering management ; Systems engineering ; Modularity (biology) ; Repurposing ; Computer science ; Engineering ; Artificial intelligence Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Joshua Reilly | Branka Miljevic | Jasmine Jensen | Roland Agoston
Source: QUT ePrints (Queensland University of Technology) (article, 2020-08-01)
Field: Chemistry — Physical and Theoretical Chemistry

Maintaining the chemistry foundations for our future biochemists: Our COVID-19-driven curriculum transformation Chemistry for Health Sciences (CZB190) is a first year prerequisite for all biochemistry subjects at QUT. In addition, CZB190 develops laboratory-based experimental skills and reasoning. With a cohort of over 220 students, the shift to at-home learning due to COVID-19 was a mammoth task for CZB190 staff and students. We want to showcase how our effective collaboration as lecturers, sessional academics and centralised learning support staff resulted in a supportive experience for CZB190 students as they adapted to the online chemistry classroom. Coronavirus disease 2019 (COVID-19) ; Curriculum ; 2019-20 coronavirus outbreak ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Task (project management) ; Medical education ; Chemistry ; Mathematics education ; Psychology ; Pedagogy ; Engineering ; Medicine ; Virology Various Chemistry Research Topics ; Biomedical and Engineering Education ; Genetics, Bioinformatics, and Biomedical Research
Authors: D. Rousseau | A. Ukleja
Source: arXiv (Cornell University) (preprint, 2020-12-15)
Field: Materials Science — Materials Chemistry

Machine Learning scientific competitions and datasets A number of scientific competitions have been organised in the last few years\nwith the objective of discovering innovative techniques to perform typical High\nEnergy Physics tasks, like event reconstruction, classification and new physics\ndiscovery. Four of these competitions are summarised in this chapter, from\nwhich guidelines on organising such events are derived. In addition, a choice\nof competition platforms and available datasets are described\n Computer science ; Data science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Computational Physics and Python Applications
Authors: James V. Masi
Source:  (article, 2020-09-03)
Field: Chemistry — Organic Chemistry

Electrically And Magnetically Active Polymers: Not Just Insulating Plastics The advent of organic materials for electroluminescent devices has allowed a wide variety of applications in displays, communications, sensors, and the like. The demonstration of organic magnetic materials has offered the challenge of finding polymer (organic) materials in which there is sufficient electronic exchange as well as stability in both thermally and chemically. The intrinsically conductive and semiconductive polymers have allowed designers the liberties of flexibility and conductivity to meet needs from batteries to solar cells. This paper gives an overview of this search past, present, and future and the synthesis of promising new complex polymer building blocks which can yield conductive, semiconductive, luminescent, ferro-, ferri-, and super-paramagnetic materials for devices of the future in power, storage, displays, and communications devices. The use of these materials to produce electrically active polymeric materials has changed our way of thinking about how to fabricate devices with properties heretofore unrealizable. There are three laboratory exercises described in this paper. They have been given both as in-class demonstrations and as student laboratories. Polymer ; Materials science ; Electroluminescence ; Nanotechnology ; Electrical conductor ; Electrically conductive ; Conductive polymer ; Electrical engineering ; Optoelectronics ; Composite material ; Engineering Inorganic and Organometallic Chemistry ; Various Chemistry Research Topics
Authors: Ahmet Turan | Mehmet Buğdaycı
Source: El-Cezeri Fen ve Mühendislik Dergisi (article, 2020-05-30)
Field: Materials Science — Materials Chemistry

CoB Ön Alaşımlarının Alüminotermik Redüksiyon Yöntemiyle Üretilmesi ve Termokimyasal Modellenmesi Bu çalışmada CoB ön alaşımlarının metalotermik (alüminotermik) redüksiyon yöntemi ile üretim parametrelerinin belirlenmesi üzerine çalışılmıştır. Çalışmada redüktan olarak aluminyum kullanılmış ve redüktan stokiyometrisindeki değişimin ürünler üzerindeki etkisi incelenmiştir. Redüksiyon sistemi FactSage 7.1 programıyla termokimyasal olarak modellenmiş, deneysel sonuçlar ile tutarlı bulgulara ulaşılmıştır. Deneysel çalışmalar sonunda elde edilen ürünler, X-ışınları difraksiyon spektrometrisi (XRD), optik mikroskobi, taramalı elektron mikroskobisi (SEM-EDS) ve mikro-vickers sertlik ölçüm yöntemleri ile karakterize edilmiştir. Karakterizasyon sonuçları incelendiğinde metal oluşum miktarının artan aluminyum stokiyometrisi ile yükseldiği gözlenirken, hedef alaşıma en yakın kompozisyonun Al stokiyometrisinin en düşük olduğu %100’lük oranda elde edildiği görülmüştür. Ürünlerin sertlik değişimi incelendiğinde, hedef alaşıma en yakın kompozisyonda olan %100 stokiyometrik aluminyum içeren deneyde 950 HV1’lik değer ile en yüksek sertliğe ulaşıldığı belirlenmiş, yapıya giren Co-Al fazının alaşımın sertlik değerini olumsuz etkilediği görülmüştür. Optik mikroskop incelemeleri sonucunda açık ve koyu renkten oluşan iki faz görülmüş, koyu renkli fazın Co-Al bileşiminden oluşan yapı olduğu sonucuna varılmıştır. Artan Al miktarıyla koyu renkli bu fazın miktarının da arttığı görülmüş, gerek kompozisyondaki farklılıkların, gerekse sertlikteki düşüşlerin bu sebeple oluştuğu düşünülmektedir. Termokimyasal incelemeler ve deneysel çalışmalar sonucunda bu alaşımın alüminotermik redüksiyon ile üretilebileceği ve yapılabilecek ilaveler ile istenilen kompozisyonda alaşım üretiminin mümkün olabileceği sonucuna varılmıştır. Gerçekleştirilen literatür taramasında CoB alaşımlarının alüminotermik olarak üretimine ilişkin çalışma bulunmamış ve bu çalışmanın ilgili ön alaşımın metalotermik üretimi üzerine gelecek çalışmalara yön gösterici olacağı kanaati oluşmuştur. Physics Thermal and Kinetic Analysis ; Advanced Battery Technologies Research ; Lubricants and Their Additives
Authors: Serkan Karakaya*
Source: Çanakkale Onsekiz Mart University AVESIS (article, 2020-11-25)
Field: Chemistry — Analytical Chemistry

Cyclic Voltammetric Detection of a Food Dye: Sunset Yellow at Ni Nanoparticles Decorated Graphite Pencil Electrode (Oral Presentation) Nanoparticle ; Electrode ; Graphite ; Nickel ; Pencil (optics) Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Saffron Plant Research Studies