jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jong‐Pil Im | J.H. Kim | Jae‐Shin Lee | Ji Woo | Sol Yee Im | Yeriaron Kim | Yong‐Sung Eom | Won Young Choi | Jun Kim | Seung Eon Moon
Source: Energies (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

Self-Powered Autonomous Wireless Sensor Node by Using Silicon-Based 3D Thermoelectric Energy Generator for Environmental Monitoring Application In this paper, we present the results of a preliminary study on the self-powered autonomous wireless sensor node by using thermoelectric energy generator based on Silicon (Si) thermoelectric legs, energy management integrated circuit (EMIC), Radio Frequency (RF) module with a temperature and humidity sensor, etc. A novel thermoelectric module structure is designed as an energy generator module, which consists of 127 pairs of Silicon legs and this module is fabricated and tested to demonstrate the feasibility of generating electrical power under the temperature gradient of 70K. EMIC has three key features besides high efficiency, which are maximum power point tracking (MPPT), cold start, and complete self-power operation. EMIC achieved a cold start voltage of 200 mV, peak efficiency of 78.7%, MPPT efficiency 99.4%, and an output power of 34 mW through only the Thermoelectric Generator (TEG) source. To assess the capability of the device as a small scale power source for internet of things (IoT) service, we also tested energy conversion and storage experiments. Finally, the proposed sensor node system which can transmit and monitor the information from the temperature and humidity sensor through the RF module in real time demonstrates the feasibility for variable applications. Thermoelectric generator ; Electrical engineering ; Maximum power point tracking ; Wireless sensor network ; Sensor node ; Node (physics) ; Thermoelectric effect ; Computer science ; Generator (circuit theory) ; Photovoltaic system ; Power (physics) ; Electronic engineering ; Voltage ; Wireless ; Engineering ; Key distribution in wireless sensor networks ; Telecommunications ; Wireless network ; Computer network ; Physics Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: AkshatKumar Nigam | Pascal Friederich | Mario Krenn | Alán Aspuru‐Guzik
Source: International Conference on Learning Representations (article, 2020-04-30)
Field: Materials Science — Materials Chemistry

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA. We show that our algorithm outperforms other generative models in optimization tasks. We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles Chemical space ; Interpretability ; Computer science ; Genetic algorithm ; Discriminator ; Artificial neural network ; Artificial intelligence ; Space (punctuation) ; Quality control and genetic algorithms ; Machine learning ; Algorithm ; Meta-optimization ; Bioinformatics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Adib J. Samin
Source: Journal of Applied Physics (article, 2020-05-04)
Field: Materials Science — Materials Chemistry

A physics-based machine learning study of the behavior of interstitial helium in single crystal W–Mo binary alloys In this work, the behavior of dilute interstitial helium in W–Mo binary alloys was explored through the application of a first principles-informed neural network (NN) in order to study the early stages of helium-induced damage and inform the design of next generation materials for fusion reactors. The neural network (NN) was trained using a database of 120 density functional theory (DFT) calculations on the alloy. The DFT database of computed solution energies showed a linear dependence on the composition of the first nearest neighbor metallic shell. This NN was then employed in a kinetic Monte Carlo simulation, which took into account two pathways for helium migration, the T-T pathway (T: Tetreahedral) and the T-O-T pathway (a second order saddle in both W and Mo) (O: Octahedral). It was determined that the diffusivity of interstitial helium in W–Mo alloys can vary by several orders of magnitude depending on the composition. Moreover, T-O-T pathways were found to dominate the T-T pathways for all alloy compositions for temperatures over about 450 K. Heterogeneous structures were also examined to account for radiation-induced segregation. It was observed that diffusion was fast when W segregated to the grain interior region and Mo to the grain outer region and was slow for the reverse situation. This behavior was explained by studying the energy landscape. Finally, thermodynamic simulations indicated that Mo-rich regions of the alloy were most favorable for binding the interstitial helium and may be the sites for the nucleation of helium bubbles. Helium ; Nucleation ; Materials science ; Density functional theory ; Thermal diffusivity ; Diffusion ; Kinetic Monte Carlo ; Alloy ; Grain boundary ; Binding energy ; Ab initio ; Thermodynamics ; Atomic physics ; Condensed matter physics ; Physics ; Monte Carlo method ; Chemistry ; Computational chemistry ; Metallurgy ; Microstructure Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hideo Doi | Kazuaki Z. Takahashi | Takeshi Aoyagi
Source: The Journal of Chemical Physics (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Mining of effective local order parameters for classifying crystal structures: A machine learning study Determining local structures of molecular systems helps the scientific and technological understanding of the function of materials. Molecular simulations provide microscopic information on molecular systems, but analyzing the resulting local structures is a non-trivial task. Many kinds of order parameters have been developed for detecting such local structures. Bond-orientational order parameters are promising for classifying local structures and have been used to analyze systems with such structures as body-centered cubic, face-centered cubic, hexagonal close-packed, and liquid. A specific set of order parameters derived from Lechner's definitional equation are widely used to classify complex local structures. However, there has been no thorough investigation of the classification capability of other Lechner parameters, despite their potential to precisely distinguish local structures. In this work, we evaluate the classification capability of 112 species of bond-orientational order parameters including Lechner's definitions. A total of 234 248 combinations of these parameters are also evaluated. The evaluation is systematically and automatically performed using machine learning techniques. To distinguish the four types of local structures, we determine the better set of two order parameters by comparing with a conventional set. A set of three order parameters is also suggested for better accuracy. Therefore, the machine learning scheme in the present study enables the systematic, accurate, and automatic mining of effective order parameters for classifying crystal structures. Computer science ; Set (abstract data type) ; Artificial intelligence ; Task (project management) ; Order (exchange) ; Machine learning ; Function (biology) ; Face (sociological concept) ; Local structure ; Data mining ; Algorithm ; Physics ; Engineering ; Chemical physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Cuong Q. Nguyen | Constantine Kreatsoulas | Kim Branson
Source: arXiv (Cornell University) (preprint, 2020-03-12)
Field: Materials Science — Materials Chemistry

Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, limited labeled data often hinders the application of deep learning in this setting. Meanwhile advances in meta-learning have enabled state-of-the-art performances in few-shot learning benchmarks, naturally prompting the question: Can meta-learning improve deep learning performance in low-resource drug discovery projects? In this work, we assess the transferability of graph neural networks initializations learned by the Model-Agnostic Meta-Learning (MAML) algorithm - and its variants FO-MAML and ANIL - for chemical properties and activities tasks. Using the ChEMBL20 dataset to emulate low-resource settings, our benchmark shows that meta-initializations perform comparably to or outperform multi-task pre-training baselines on 16 out of 20 in-distribution tasks and on all out-of-distribution tasks, providing an average improvement in AUPRC of 11.2% and 26.9% respectively. Finally, we observe that meta-initializations consistently result in the best performing models across fine-tuning sets with $k \in \{16, 32, 64, 128, 256\}$ instances. Artificial intelligence ; Computer science ; Machine learning ; Benchmark (surveying) ; Deep learning ; Meta learning (computer science) ; Artificial neural network ; Task (project management) ; Transferability ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Maicon Pierre Lourenço | Alexandre dos Santos Anastácio | Andréia Luísa da Rosa | Thomas Frauenheim | Maurício Chagas da Silva
Source: Journal of Molecular Modeling (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

An adaptive design approach for defects distribution modeling in materials from first-principle calculations Computer science ; Gaussian ; Kriging ; Stability (learning theory) ; Biological system ; Space (punctuation) ; Surrogate model ; First principle ; Sample (material) ; Process (computing) ; Configuration space ; Statistical physics ; Materials science ; Algorithm ; Machine learning ; Density functional theory ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: C. Lawrence Zitnick | Lowik Chanussot | Abhishek Das | Siddharth Goyal | Javier Heras‐Domingo | Caleb Ho | Weihua Hu | Thibaut Lavril | Aini Palizhati | Morgane Rivière | Muhammed Shuaibi | Anuroop Sriram | Kevin Tran | Brandon M. Wood | Junwoong Yoon | Devi Parikh | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2020-10-14)
Field: Materials Science — Materials Chemistry

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand. This may require the storage of power for hours, days, or months. One solution that offers the potential of scaling to nation-sized grids is the conversion of renewable energy to other fuels, such as hydrogen or methane. To be widely adopted, this process requires cost-effective solutions to running electrochemical reactions. An open challenge is finding low-cost electrocatalysts to drive these reactions at high rates. Through the use of quantum mechanical simulations (density functional theory), new catalyst structures can be tested and evaluated. Unfortunately, the high computational cost of these simulations limits the number of structures that may be tested. The use of machine learning may provide a method to efficiently approximate these calculations, leading to new approaches in finding effective electrocatalysts. In this paper, we provide an introduction to the challenges in finding suitable electrocatalysts, how machine learning may be applied to the problem, and the use of the Open Catalyst Project OC20 dataset for model training. Renewable energy ; Energy storage ; Electrocatalyst ; Scalability ; Computer science ; Process engineering ; Wind power ; Process (computing) ; Power (physics) ; Engineering ; Electrical engineering ; Electrochemistry ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Michiko Sasaki | Shenghong Ju | Yibin Xu | Junichiro Shiomi | Masahiro Goto
Source: ACS Combinatorial Science (article, 2020-11-04)
Field: Materials Science — Materials Chemistry

Identifying Optimal Strain in Bismuth Telluride Thermoelectric Film by Combinatorial Gradient Thermal Annealing and Machine Learning The thermoelectric properties of bismuth telluride thin film (BTTF) was tuned by inducing internal strain through a combination of combinatorial gradient thermal annealing (COGTAN) and machine learning. BTTFs were synthesized via magnetron sputter coating and then treated by COGTAN. The crystal structure and thermoelectric properties, namely Seebeck coefficient and thermal conductivity, of the treated samples were analyzed via micropoint X-ray diffraction and scanning thermal probe microimaging, respectively. The obtained combinatorial data reveals the correlation between internal strain and the thermoelectric properties. The Seebeck coefficient of BTTF exhibits largest sensitivity, where the value ranges from 7.9 to -108 μV/K. To further explore the possibility to enhance Seebeck coefficient, the combinatorial data were subjected to machine learning. The trained model predicts that optimal strains of 3-4% and 1-2% along the <i>a</i>- and <i>c</i>-axis, respectively, significantly improve Seebeck coefficient. The technique demonstrated herein can be used to predict and enhance the performance of thermoelectric materials by inducing internal strain. Bismuth telluride ; Chemistry ; Annealing (glass) ; Thermoelectric effect ; Temperature gradient ; Thermal ; Thermoelectric materials ; Composite material ; Thermodynamics ; Materials science Advanced Thermoelectric Materials and Devices ; Advanced Semiconductor Detectors and Materials ; Machine Learning in Materials Science
Authors: Changhong Mu | Zebin Yuan | Xiuqin Ouyang | Pu Sun | Bo Wang
Source: Journal of the Science of Food and Agriculture (article, 2020-11-20)
Field: Chemistry — Analytical Chemistry

Non‐destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning The above results indicated the potential for utilizing deep learning technology to predict the quality indicators of blueberry before harvesting. © 2020 Society of Chemical Industry. Pigment ; Artificial intelligence ; Deep learning ; Chemistry ; Horticulture ; Botany ; Food science ; Computer science ; Biology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Wesley Beckner | Chowdhury Ashraf | James Lee | David A. C. Beck | Jim Pfaendtner
Source: The Journal of Physical Chemistry B (article, 2020-08-24)
Field: Materials Science — Materials Chemistry

Continuous Molecular Representations of Ionic Liquids Designing new ionic liquids (ILs) is of crucial importance for various industrial applications. However, this always leads to a daunting challenge, as the number of possible combinations of cation and anion are very high and it is impossible to experimentally propose and screen a wide pool of potential candidates. However, recent applications of machine learning (ML) models have greatly improved the overall chemical discovery pipeline. In this study, we compare different generative methods for producing ionic liquids. In this comparison, we show the following: (1) when training data is scarce, a transfer learning approach can be applied to variational autoencoders (VAEs) to generate molecular structures of the target molecule type; (2) in a VAE-like structure, separate latent spaces for the cationic and anionic moieties can result in meaningful representations for their combinative, macroscopic properties; (3) interpolating between ILs with desired properties can result in a new IL with attributes similar to the two structural end points. Ionic liquid ; Computer science ; Pipeline (software) ; Cationic polymerization ; Generative grammar ; Ionic bonding ; Molecule ; Artificial intelligence ; Ion ; Biological system ; Theoretical computer science ; Machine learning ; Chemistry ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Computational Drug Discovery Methods
Authors: Jussi Määttä | Viacheslav Bazaliy | Jyri Kimari | Flyura Djurabekova | K. Nordlund | Teemu Roos
Source: arXiv (Cornell University) (article, 2020-04-06)
Field: Materials Science — Materials Chemistry

Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed-form optimization criteria for pruning the models for continuous as well as binary data which arise in a challenging real-world material physics problem. The pruned models are optimized to provide compact and interpretable versions of larger models based on informed assumptions about the data distribution. Visualizations of the pruned models provide insight into the atomic configurations that determine atom-level migration processes in solid matter; these results may inform future research on designing more suitable descriptors for use with machine learning algorithms. Pruning ; Computer science ; Scalability ; Artificial intelligence ; Basis (linear algebra) ; Machine learning ; Function (biology) ; Radial basis function ; Artificial neural network ; Binary number ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrocarbon exploration and reservoir analysis
Authors: Mads-Peter Verner Christiansen | Henrik Lund Mortensen | Søren Ager Meldgaard | Bjørk Hammer
Source: The Journal of Chemical Physics (article, 2020-07-24)
Field: Materials Science — Materials Chemistry

Gaussian representation for image recognition and reinforcement learning of atomistic structure The success of applying machine learning to speed up structure search and improve property prediction in computational chemical physics depends critically on the representation chosen for the atomistic structure. In this work, we investigate how different image representations of two planar atomistic structures (ideal graphene and graphene with a grain boundary region) influence the ability of a reinforcement learning algorithm [the Atomistic Structure Learning Algorithm (ASLA)] to identify the structures from no prior knowledge while interacting with an electronic structure program. Compared to a one-hot encoding, we find a radial Gaussian broadening of the atomic position to be beneficial for the reinforcement learning process, which may even identify the Gaussians with the most favorable broadening hyperparameters during the structural search. Providing further image representations with angular information inspired by the smooth overlap of atomic positions method, however, is not found to cause further speedup of ASLA. Reinforcement learning ; Hyperparameter ; Speedup ; Artificial intelligence ; Representation (politics) ; Computer science ; Gaussian process ; Gaussian ; Graphene ; Algorithm ; Materials science ; Physics ; Nanotechnology ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Billy Chabalenge | Sachin K. Korde | Adrian Kelly | Daniel Neagu | Anant Paradkar
Source: Crystal Growth & Design (article, 2020-06-08)
Field: Materials Science — Materials Chemistry

Understanding Matrix-Assisted Continuous Co-crystallization Using a Data Mining Approach in Quality by Design (QbD) The present study demonstrates the application of decision tree algorithms to the co-crystallization process. Fifty four (54) batches of carbamazepine–salicylic acid co-crystals embedded in poly(ethylene oxide) were manufactured via hot melt extrusion and characterized by powder X-ray diffraction, differnetial scanning calorimetry, and near-infrared spectroscopy. This dataset was then applied in WEKA, which is an open-sourced machine learning software to study the effect of processing temperature, screw speed, screw configuration, and poly(ethylene oxide) concentration on the percentage of co-crystal conversion. The decision trees obtained provided statistically meaningful and easy-to-interpret rules, demonstrating the potential to use the method to make rational decisions during the development of co-crystallization processes. Crystallization ; Process analytical technology ; Materials science ; Decision tree ; Ethylene oxide ; Crystal (programming language) ; Oxide ; Extrusion ; Process engineering ; Chemical engineering ; Computer science ; Data mining ; Polymer ; Engineering ; Composite material ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Nuraidayani Effendy | H.A.A. Sidek | Halimah Mohamed Kamari | Mohd Hafiz Mohd Zaid | Caceja Elyca Anak Budak | Muhammad Kashfi Shabdin | Mohammad Zulhasif Ahmad Khiri | Siti Aisyah Abdul Wahab
Source: Journal of Materials Research and Technology (article, 2020-10-06)
Field: Materials Science — Ceramics and Composites

Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions Artificial neural networks (ANN) is known as one of the artificial intelligence tools which are inspired by the biological nerve system, have a capability to predict the physical and elastic parameter of glasses without melting the raw materials. The experimental of bismuth-tellurite glasses with the composition yBi2O3 - (1-y)TeO2 where y = 0, 0.05, 0.07, 0.10, 0.13, 0.15 have been fabricated using melting and quenching methods. These works were discovered that the prediction value by artificial neural networks for density, ultrasonic velocity, and elastic moduli of bismuth-tellurite glass composition gives a very good agreement as compared with the experimental measurements. The goodness of fit from the graph used R2 value to represent the relationship between the data presented from the experiment and prediction model. The great fit of coefficient R2 value elucidates in all figures is around 0.99942–1.0000 which is considered to be very satisfactory. Materials science ; Bismuth ; Artificial neural network ; Ultrasonic sensor ; Biological system ; Artificial intelligence ; Metallurgy ; Computer science ; Acoustics Glass properties and applications ; Luminescence Properties of Advanced Materials ; Phase-change materials and chalcogenides
Authors: Saeid Atashrouz | Amir M. Rahmani
Source: Neural Computing and Applications (article, 2020-03-30)
Field: Chemistry — Inorganic Chemistry

Predicting hydrogen storage capacity of metal–organic frameworks using group method of data handling Hydrogen storage ; Computer science ; Artificial neural network ; Leverage (statistics) ; Group method of data handling ; Mean squared error ; Data mining ; Machine learning ; Hydrogen ; Mathematics ; Statistics ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications