jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Seifrid | Riley J. Hickman | Andrés Aguilar‐Granda | Cyrille Lavigne | Jenya Vestfrid | Tony Wu | Théophile Gaudin | Emily J. Hopkins | Alán Aspuru‐Guzik
Source:  (preprint, 2021-07-16)
Field: Materials Science — Materials Chemistry

Routescore: Punching the Ticket to More Efficient Materials Development Self-driving labs, in the form of automated experimentation platforms guided by machine learning algorithms have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving labs. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization), and to simulate a self-driving lab that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ~3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and generality of our approach in mixed synthetic planning and optimization. Computer science ; Bottleneck ; Chemical space ; Generality ; Punching ; Space (punctuation) ; Throughput ; Embedded system ; Drug discovery ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Muratahan Aykol | Joseph H. Montoya | Jens S. Hummelshøj
Source: ChemRxiv (preprint, 2021-05-28)
Field: Materials Science — Materials Chemistry

Rational Solid-State Synthesis Routes for Inorganic Materials Rational solid-state synthesis of inorganic compounds is formulated as catalytic nucleation on crystalline reactants, where contributions of reaction and interfacial energies to the nucleation barriers are approximated from high-throughput thermochemical data, and structural and interfacial features of crystals, respectively. Favorable synthesis reactions are then identified by a Pareto analysis of relative nucleation barriers and phase-selectivities of reactions leading to the target. We demonstrate the application of this approach in reaction planning for solid-state synthesis of a range of compounds, including the widely-studied oxides LiCoO2, BaTiO3 and YBa2Cu3O7, as well as other metal oxide, oxyfluoride, phosphate and nitride targets. Pathways for enabling retrosynthesis of inorganics are also discussed. Nucleation ; Solid-state ; Nitride ; Materials science ; Oxide ; Catalysis ; Metal ; Phase (matter) ; Chemical engineering ; Range (aeronautics) ; Inorganic chemistry ; Nanotechnology ; Chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry ; Composite material Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography ; Zeolite Catalysis and Synthesis
Authors: Xim Bokhimi
Source: Frontiers in Chemical Engineering (article, 2021-10-20)
Field: Materials Science — Materials Chemistry

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product. Hydrodesulfurization ; Nanoporous ; Adsorption ; Catalysis ; Computer science ; Process (computing) ; Artificial intelligence ; Heterogeneous catalysis ; Machine learning ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Fabio Urbina | Christopher Lowden | Christopher Culberson | Sean Ekins
Source: ChemRxiv (preprint, 2021-09-30)
Field: Materials Science — Materials Chemistry

MegaSyn: Integrating Generative Molecule Design, Automated Analog Designer and Synthetic Viability Prediction Drug discovery is a multi-stage process, often beginning with the identification of active molecules from a high-throughput screen or machine learning model. Once structure activity relationship trends become well established, identifying new analogs with better properties is important. Synthesizing these new compounds is a logical next step, and is key to research groups that have a synthetic chemistry team or external collaborators. Generative machine learning models have become widely adopted to generate new molecules and explore molecular space, with the goal of discovering novel compounds with desires properties. These generative models have been composed from recurrent neural networks (RNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) and are often combined with transfer learning or scoring of physicochemical properties to steer generative design. While these generative models have proven useful in generating new molecular libraries, often they are not capable of addressing a wide variety of potential problems, and often converge into similar molecular space when combined with a scoring function for desired properties. In addition, generated compounds are often not synthetically feasible, reducing their capabilities outside of virtual composition and limiting their usefulness in real-world scenarios. Here we introduce a suite of automated tools called MegaSyn representing 3 components: a new hill-climb algorithm which makes use of SMILES-based RNN generative models, analog generation software, and retrosynthetic analysis coupled with fragment analysis to score molecules for their synthetic feasibility. We now describe the development and testing of this suite of tools and propose how they might be used to optimize molecules or prioritize promising lead compounds using test case examples. Generative grammar ; Chemical space ; Computer science ; Artificial intelligence ; Machine learning ; Generative model ; Retrosynthetic analysis ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kan Hatakeyama‐Sato
Source: The Brain & Neural Networks (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

Machine Learning for Material Science 材料科学の領域でどのように機械学習の技術が活用されているか概説する．具体例として材料の物性をどのようにして予測するかに焦点を当て，その特徴や課題を整理する．また最近の事例として材料情報の認識のためのグラフニューラルネットワークの活用例を紹介する． Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Ayşen Yağmur KURŞUN | Burçin Yalçın | Merve Güneş | Ghada Tagorti | Bülent Kaya
Source: Afyon Kocatepe University Journal of Sciences and Engineering (article, 2021-12-31)
Field: Materials Science — Materials Chemistry

MgO Nanopartiküllerinin Drosophila melanogaster Üzerindeki Davranışsal Toksisitesinin Değerlendirilmesi Günümüzde nanopartiküllerin üstün fizikokimyasal özelliklerinin belirlenmesinden sonra nanopartiküller ve nanoteknolojiye olan ilgi de hızlı bir şekilde artmış ve kullanım alanları da yaygınlaşmıştır. Üç fiziksel boyutundan en az biri 1-100 nm aralığında olan ve belirli nano-ölçekli özellikler gösterebilen maddeler olarak tanımlanan nanopartiküller, tıp, elektronik, kozmetik, çevresel temizlik gibi birçok farklı alanda yaygın kullanım alanına sahiptir. Bu yaygın kullanımdan dolayı insanların da nanopartiküllere olan maruziyeti gün geçtikçe artmaktadır. Ayrıca nanopartiküllerin çevreye yayılması doğada ki birçok canlı için risk oluşturmaktadır. Bu sebeple de nanopartiküllerin yol açabileceği olumlu/olumsuz biyolojik etkilerin anlaşılabilmesi için yapılan çalışmalar da hız kazanmıştır. Bu bağlamda yapılan çalışmada MgO nanopartiküllerinin farklı konsantrasyonlarının (2, 5 ve 10 mM) Drosophila melanogaster üzerindeki davranışsal toksisiteye etkisinin araştırılması amaçlanmıştır. Davranışsal toksisitenin belirlenmesinde larval ağırlık ve hareketi, ergin birey ağırlığı, pupa oluşturma başarısı, pupa pozisyonu, pupadan çıkış başarısı, negatif jeotaksis ve ömür uzunluğu deneyleri gerçekleştirilerek değerlendirilmiştir. Çalışma sonucunda MgO NP’lerinin genel olarak Drosophila melanogaster’da negatif bir etkiye neden olduğu tespit edilmiştir. Larval hareketin incelendiği deney kapsamında çalışma kapsamında en yüksek doz olarak belirlenen 10 mM’lık derişimin istatistiksel olarak anlamlı bir şekilde azalmaya neden olduğu tespit edilmiştir. Physics ; Molecular biology ; Biology Nanoparticles: synthesis and applications ; Magnesium Oxide Properties and Applications ; Graphene and Nanomaterials Applications
Authors: Radu Fechet | Adrian I. Petrariu | Adrian Graur
Source: Preprints.org (preprint, 2021-11-02)
Field: Materials Science — Materials Chemistry

Partial Discharge and Internet of Things: A Switchgear Cell Maintenance Application using Microclimate Sensors This paper proposes a solution for the development of microclimate monitoring for Low Voltage / High Voltage switchgear using the PRTG Internet of Things (IoT) platform. This IoT-based real time monitoring system can enable predictive maintenance to reduce the risk of electrical station malfunctions due to unfavorable environmental conditions. The combination of humidity and dust can lead to unplanned electrical discharges along the isolators inside a low or medium voltage electric table. If no predictive measures are taken, the situation may deteriorate and lead to significant damage inside and outside the switchgear cell. Thus, the mentioned situation can lead to unprogrammed maintenance interventions that can conduct to the change of the entire affected switchgear cell. Using a low-cost and efficient system, the climate conditions inside and outside the switchgear is monitored and transmitted remotely to a monitoring center. From the results obtained using a 365-day time interval, we can conclude that the proposed system is integrated successfully in the switchgear maintaining process, having as result the reducing of maintenance costs. Switchgear ; Reliability engineering ; Microclimate ; Internet of Things ; Voltage ; Process (computing) ; Computer science ; Automotive engineering ; Environmental science ; Engineering ; Electrical engineering ; Computer security High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Icing and De-icing Technologies
Authors: Haibo Huo | Hai-Dong Yang | Kui Xu | Xinghong Kuang | Jingxiang Xu
Source: Mathematical Problems in Engineering (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Survey on H∞ Robust Control of the Solid Oxide Fuel Cell Excessive use of fuel or being underutilized will make the actual performance of solid oxide fuel cells (SOFCs) affected by a lot, and at the same time, in order to meet the demands of DC load voltage, a controller of the SOFC that is subjected to small varying loads is proposed on the basis of H∞ control theory. For the controller design, a state-space representation of the SOFC by using small-signal linearization is derived. To evaluate the control performance, the presented H∞ controller is tested on the SOFC stack with various load disturbances. The results show that the obtained H∞ controller can mitigate the voltage oscillations and deviations and can keep fuel utilization constant at varying loads. Solid oxide fuel cell ; Stack (abstract data type) ; Controller (irrigation) ; Control theory (sociology) ; Linearization ; Fuel cells ; Representation (politics) ; Voltage ; SIGNAL (programming language) ; Load following power plant ; State-space representation ; Engineering ; Control engineering ; Control (management) ; Computer science ; Nonlinear system ; Distributed generation ; Electrical engineering ; Chemical engineering ; Chemistry ; Algorithm Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Frequency Control in Power Systems
Authors: Valeria Pevtsov | Nathan D. Jacob | Behzad Kordi
Source: Energies (article, 2021-09-23)
Field: Materials Science — Materials Chemistry

Evaluation of Erosion Discharge Characteristics in Inclined Plane Tracking and Erosion Tests on Silicone Rubber under AC and DC Voltages This paper investigates partial discharge (PD) characteristics that lead to the erosion of silicone rubber (SR) polymer under AC stress and DC stress of both polarities. The experiments are performed on high temperature vulcanized (HTV) SR material samples. The inclined plane test apparatus, constructed in accordance with IEC 60587 requirements, is employed to produce the surface partial discharges and the resulting accelerated aging of the specimens. Two commercial instruments are used to obtain the PD data. A concurrent analysis of visual observations of discharge and PD data is performed to classify discharges based on the severity of material degradation that each type causes. Three types of PD are identified and characterized using diagrams of charge magnitude versus time and a signal processing technique called time-frequency mapping. Individual pulse waveshapes of each type of discharge are also analyzed. PD pulse waveforms are analyzed according to their amplitude, energy, pulse width, and frequency spectrum. These pulse waveform parameters are evaluated and compared for the eroding discharge pulses under AC and DC voltage stresses. It is found that the stages of material degradation during IPT are related to the variations in discharge magnitude and the location of pulse clusters on the time-frequency maps. Partial discharge ; Silicone rubber ; Waveform ; Materials science ; Erosion ; Voltage ; Composite material ; Pulse (music) ; Natural rubber ; Vulcanization ; Stress (linguistics) ; Amplitude ; Degradation (telecommunications) ; SIGNAL (programming language) ; Acoustics ; Optics ; Electrical engineering ; Physics ; Geology ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2021-01-10)
Field: Materials Science — Materials Chemistry

Transformative Applications of Machine Learning for Chemical Reactions Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platforms for reaction planning. ML-based techniques can be of particular interest for problems which involve both, computation and experiments. For one, Bayesian inference is a powerful approach to include knowledge from experiment in improving computational models. ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, both, in research and academic teaching. Computer science ; Transformative learning ; Field (mathematics) ; Artificial intelligence ; Machine learning ; Inference ; State (computer science) ; Bayesian inference ; Bayesian probability ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Shuqian Ye | Jiechun Liang | Xi Zhu
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Catalyst deep neural networks (Cat-DNNs) in singlet fission property prediction Many current deep neural network (DNN) models only focus on straightforward optimization over the given database. However, most numerical fitting procedures depart from physical laws. By introducing the concept of "catalysis" from physical chemistry, we propose that the physical correlations among molecular properties could spontaneously act as a catalyst in the DNNs, which increases the accuracy, and more importantly, guides the DNNs in the right way. These Catalysis-DNNs (Cat-DNNs) could precisely predict both the ground and excited-state properties, especially the molecules' screening with singlet fission character. We show that traditional machine learning metrics are not suitable for evaluating model accuracy in physical-chemical tasks and issue new physical errors. We believe that the agile transfer of fundamental physics or chemistry domain knowledge, like the catalyst, could significantly benefit both the architecture and application of artificial intelligence technology in the future. Artificial neural network ; Task (project management) ; Deep neural networks ; Catalysis ; Singlet state ; Property (philosophy) ; Computer science ; Physical property ; Biological system ; Fission ; Singlet fission ; Scheme (mathematics) ; Artificial intelligence ; Statistical physics ; Physics ; Chemistry ; Mathematics ; Nuclear physics ; Quantum mechanics ; Engineering ; Biology ; Neutron ; Mathematical analysis ; Excited state Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Shubham Pandey | Jiaxing Qu | Vladan Stevanović | Peter C. St. John | Prashun Gorai
Source: ChemRxiv (preprint, 2021-04-16)
Field: Materials Science — Materials Chemistry

A Graph Neural Network for Predicting Energy and Stability of Known and Hypothetical Crystal Structures The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerating the screening for new materials over vast chemical spaces. Here, we develop a unique graph neural network model to accurately predict the total energy of both GS and higher-energy hypothetical structures. We use ~16,500 density functional theory calculated total energy from the NREL Materials Database and ~11,000 in-house generated hypothetical structures to train our model, thus making sure that the model is not biased towards either GS or higher-energy structures. We also demonstrate that our model satisfactorily ranks the structures in the correct order of their energies for a given composition. Furthermore, we present a thorough error analysis to explain several failure modes of the model, which highlights both prediction outliers and occasional inconsistencies in the training data. By peeling back layers of the neural network model, we are able to derive chemical trends by analyzing how the model represents learned structures and properties. Artificial neural network ; Computer science ; Outlier ; Stability (learning theory) ; Energy (signal processing) ; Graph ; Graph theory ; Data mining ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics ; Statistics ; Combinatorics Machine Learning in Materials Science ; History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Hossein Abedi | Ala Qattawi
Source: Research Square (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures Shape-memory alloy ; Transformation (genetics) ; Artificial neural network ; Alloy ; Materials science ; Computer science ; Metallurgy ; Artificial intelligence ; Chemistry Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Tianhao Su | Yaning Cui | Zhengheng Lian | Minglang Hu | Minjie Li | Wencong Lu | Wei Ren
Source: The Journal of Physical Chemistry Letters (article, 2021-08-31)
Field: Materials Science — Materials Chemistry

Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple Machine learning (ML) accelerates the rational design and discovery of materials, where the feature plays a critical role in the ML model training. We propose a low-cost electron probability waves (EPW) descriptor based on electronic structures, which is extracted from high-symmetry points in the Brillouin zone. In the task of distinguishing ferromagnetic or antiferromagnetic material, it achieves an accuracy (ACC) at 0.92 and an area under the receiver operating characteristic curve (AUC) at 0.83 by 10-fold cross-validation. Furthermore, EPW excels at classifying metal/semiconductors and judging the direct/indirect bandgap of semiconductors. The distribution of electron clouds is an essential criterion for the origin of ferromagnetism, and EPW acts as an emulation of the electronic structure, which is the key to the achievements. Our EPW-based ML model obtains ACC and AUC equivalent to crystal graph features-based deep learning models for tasks with physical recognitions in electronic states. Brillouin zone ; Emulation ; Antiferromagnetism ; Artificial intelligence ; Ferromagnetism ; Semiconductor ; Computer science ; Condensed matter physics ; Machine learning ; Physics ; Computational physics ; Pattern recognition (psychology) ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications