jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sami M. Ibn Shamsah | Taoreed O. Owolabi
Source: Chinese Journal of Physics (article, 2020-10-19)
Field: Materials Science — Materials Chemistry

Newtonian mechanics based hybrid machine learning method of characterizing energy band gap of doped zno semiconductor Mean squared error ; Band gap ; Materials science ; Semiconductor ; Support vector machine ; Root mean square ; Machine learning ; Artificial intelligence ; Computer science ; Statistical physics ; Physics ; Mathematics ; Statistics ; Optoelectronics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; GaN-based semiconductor devices and materials
Authors: Lang Wu | Yue Xiao | Mithun Ghosh | Qiang Zhou | Qing Hao
Source: ES Materials & Manufacturing (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction for Bandgaps of Inorganic Materials Machine learning approaches are explored to predict the bandgaps of inorganic compounds using known compositional features, based on a dataset of 3896 compounds with experimentally measured bandgaps. In particular, among various existing methods, we propose a new method, random forest with Gaussian process model as leaf nodes (RF-GP), and show its advantages. We have also investigated ensemble learning methods, which produce superior results over other traditional machine learning methods, but at the cost of extra computational load and further reduced interpretability. Interpretability ; Random forest ; Machine learning ; Computer science ; Artificial intelligence ; Process (computing) ; Gaussian process ; Ensemble learning ; Gaussian ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Grigoriy Kimaev | Luis Ricardez‐Sandoval
Source: The Journal of Physical Chemistry C (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Discrimination for Parameter Estimation and Optimal Product Design of Thin Films Manufactured by Chemical Vapor Deposition Industrial production of valuable chemical products often involves the manipulation of phenomena evolving at different temporal and spatial scales. Product properties can be captured accurately using computationally expensive stochastic multiscale models that explicitly consider the feedbacks between different scales. However, product design quality is often tampered by uncertainties affecting process operation. In this work, we used artificial neural networks (ANNs) to estimate an uncertain parameter, accurately predict product properties under uncertainty, and achieve orders-of-magnitude computational savings of a multiscale model of thin film formation by chemical vapor deposition. ANNs were trained using multiple realizations of the uncertain parameter to capture the behavior of the thin film’s two key microscale properties: roughness and growth rate. Next, mean square error and maximum likelihood estimation were used for parameter estimation and to find the ANN that could generate the closest predictions to the real-time measurements collected from the process in the presence of uncertainty. The chosen ANNs were employed to seek for the optimal operating conditions to enable the fabrication process to meet product quality specifications. ANNs are a promising technique for product property prediction and efficient decision making in the design of optimal operating conditions for chemical processes under uncertainty. Artificial neural network ; Microscale chemistry ; Computer science ; Process (computing) ; Product (mathematics) ; Uncertainty quantification ; Surface roughness ; Estimation theory ; Quality (philosophy) ; Process engineering ; Mathematical optimization ; Artificial intelligence ; Algorithm ; Machine learning ; Materials science ; Mathematics ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Heat Transfer and Optimization
Authors: Seiji Kajita | Nobuko Ohba | Akitoshi Suzumura | Shin Tajima | Ryoji Asahi
Source: NPG Asia Materials (article, 2020-04-23)
Field: Materials Science — Materials Chemistry

Discovery of superionic conductors by ensemble-scope descriptor Abstract Machine learning accelerates virtual screening in which material candidates are selected from existing databases, facilitating materials discovery in a broad chemical search space. Machine learning models quickly predict a target property from explanatory material features called descriptors. However, a major bottleneck of the machine learning model is an insufficient amount of training data in materials science, especially data with non-equilibrium properties. Here, we develop an alternative virtual-screening process via ensemble-based machine learning with one handcrafted and two generic descriptors to maximize the inference ability even using a small training dataset. A joint representation with the three descriptors translates the physical and chemical properties of a material as well as its underlying short- and long-range atomic structures to describe a multifaceted perspective of the material. As an application, the ensemble-scope descriptor learning model was trained with only 29 entries in the training dataset, and it selected potential oxygen-ion conductors from 13,384 oxides in the inorganic crystal structure database. The experiments confirmed that we successfully discovered five compounds that have not been reported, to the best of our knowledge, as oxygen-ion conductors. Chemical space ; Bottleneck ; Computer science ; Machine learning ; Scope (computer science) ; Artificial intelligence ; Virtual screening ; Ensemble learning ; Inference ; Representation (politics) ; Electrical conductor ; Property (philosophy) ; Perspective (graphical) ; Process (computing) ; Materials science ; Drug discovery ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Haishan Yu | Ying Wang | Xijun Wang | Jinxiao Zhang | Sheng Ye | Yan Huang | Yi Luo | Edward Sharman | Shi‐Lu Chen | Jun Jiang
Source: The Journal of Physical Chemistry A (article, 2020-04-21)
Field: Materials Science — Materials Chemistry

Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls 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 the 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 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. Dissociation (chemistry) ; Bond-dissociation energy ; Computer science ; Random forest ; Computation ; Artificial neural network ; Machine learning ; Artificial intelligence ; Catalysis ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Chenlu Guo | Jian Wu | Jens Rosenberg | Tangi Roussel | Shuhui Cai | Congbo Cai
Source: Magnetic Resonance in Medicine (article, 2020-06-30)
Field: Materials Science — Materials Chemistry

Fast chemical exchange saturation transfer imaging based on PROPELLER acquisition and deep neural network reconstruction Although the deep neural network is trained entirely on synthesized data, it works well on reconstructing experimental data. The proof of concept study demonstrates that the combination of the PROPELLER sampling scheme and the deep neural network enables considerable acceleration of saturated image acquisition and may find applications in CEST MRI. Propeller ; Imaging phantom ; Computer science ; Artificial neural network ; Artificial intelligence ; Data acquisition ; Iterative reconstruction ; Sampling (signal processing) ; Acceleration ; Computer vision ; Physics ; Optics ; Engineering Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Stephen Xie | Parker Kotlarz | Richard G. Hennig | Juan C. Nino
Source: Computational Materials Science (article, 2020-04-03)
Field: Materials Science — Materials Chemistry

Machine learning of octahedral tilting in oxide perovskites by symbolic classification with compressed sensing Electronegativity ; Support vector machine ; Octahedron ; Machine learning ; Perovskite (structure) ; Computer science ; Artificial intelligence ; Materials science ; Algorithm ; Mathematics ; Crystal structure ; Chemistry ; Physics ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Abhishek Tewari | Siddharth Dixit | Niteesh Sahni | Stéphane Bordas
Source: Data-Centric Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine learning approaches to identify and design low thermal conductivity oxides for thermoelectric applications Abstract The search space for new thermoelectric oxides has been limited to the alloys of a few known systems, such as ZnO, SrTiO 3 , and CaMnO 3 . Notwithstanding the high power factor, their high thermal conductivity is a roadblock in achieving higher efficiency. In this paper, we apply machine learning (ML) models for discovering novel transition metal oxides with low lattice thermal conductivity ( $ {k}_L $ ). A two-step process is proposed to address the problem of small datasets frequently encountered in material informatics. First, a gradient-boosted tree classifier is learnt to categorize unknown compounds into three categories of $ {k}_L $ : low, medium, and high. In the second step, we fit regression models on the targeted class (i.e., low $ {k}_L $ ) to estimate $ {k}_L $ with an $ {R}^2&gt;0.9 $ . Gradient boosted tree model was also used to identify key material properties influencing classification of $ {k}_L $ , namely lattice energy per atom, atom density, band gap, mass density, and ratio of oxygen by transition metal atoms. Only fundamental materials properties describing the crystal symmetry, compound chemistry, and interatomic bonding were used in the classification process, which can be readily used in the initial phases of materials design. The proposed two-step process addresses the problem of small datasets and improves the predictive accuracy. The ML approach adopted in the present work is generic in nature and can be combined with high-throughput computing for the rapid discovery of new materials for specific applications. Materials informatics ; Thermal conductivity ; Machine learning ; Thermoelectric effect ; Computer science ; Materials science ; Thermoelectric materials ; Lattice (music) ; Artificial intelligence ; Heterojunction ; Thermodynamics ; Physics ; Optoelectronics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Marco Ragone | Vitaliy Yurkiv | Boao Song | Ajaykrishna Ramsubramanian | Reza Shahbazian‐Yassar | Farzad Mashayek
Source: Computational Materials Science (article, 2020-04-07)
Field: Materials Science — Materials Chemistry

Atomic column heights detection in metallic nanoparticles using deep convolutional learning Column (typography) ; Nanoparticle ; Convolutional neural network ; Metal ; Materials science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Computer science ; Metallurgy Machine Learning in Materials Science ; Machine Learning and ELM ; Electron and X-Ray Spectroscopy Techniques
Authors: Muhammed Shuaibi | Saurabh Sivakumar | Rui Qi Chen | Zachary W Ulissi
Source: Machine Learning Science and Technology (article, 2020-11-20)
Field: Materials Science — Materials Chemistry

Enabling robust offline active learning for machine learning potentials using simple physics-based priors Abstract Machine learning surrogate models for quantum mechanical simulations have enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning (AL) and uncertainty estimates. When starting with small datasets, convergence of AL approaches is a major outstanding challenge which has limited most demonstrations to online AL. In this work we demonstrate a Δ-machine learning (ML) approach that enables stable convergence in offline AL strategies by avoiding unphysical configurations with initial datasets as little as a single data point. We demonstrate our framework’s capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70%–90%. The approach is incorporated and developed alongside AMP torch , an open-source ML potential package, along with interactive Google Colab notebook examples. Convergence (economics) ; Active learning (machine learning) ; Prior probability ; Simple (philosophy) ; Field (mathematics) ; Energy (signal processing) ; Online machine learning ; Work (physics) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Michiel Larmuseau | Michael Sluydts | Koenraad Theuwissen | Lode Duprez | Tom Dhaene | Stefaan Cottenier
Source: npj Computational Materials (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Compact representations of microstructure images using triplet networks Abstract The microstructure of a material, typically characterized through a set of microscopy images of two-dimensional cross-sections, is a valuable source of information about the material and its properties. Every pixel of the image is a degree of freedom causing the dimensionality of the information space to be extremely high. This makes it difficult to recognize and extract all relevant information from the images. Human experts circumvent this by manually creating a lower-dimensional representation of the microstructure. However, the question of how a microstructure image can be best represented remains open. From the field of deep learning, we present triplet networks as a method to build highly compact representations of the microstructure, condensing the relevant information into a much smaller number of dimensions. We demonstrate that these representations can be created even with a limited amount of example images, and that they are able to distinguish between visually very similar microstructures. We discuss the interpretability and generalization of the representations. Having compact microstructure representations, it becomes easier to establish processing–structure–property links that are key to rational materials design. Interpretability ; Representation (politics) ; Computer science ; Microstructure ; Generalization ; Curse of dimensionality ; Pixel ; Artificial intelligence ; Set (abstract data type) ; Computer vision ; Mathematics ; Materials science ; Mathematical analysis Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Image Processing Techniques and Applications
Authors: Yixuan Zhang | Chen Shen | Teng Long | Hongbin Zhang
Source: Journal of Physics Condensed Matter (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Thermal conductivity of h-BN monolayers using machine learning interatomic potential Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. In this work, we applied the recently developed machine learning interatomic potential (MLIP) to evaluate the thermal conductivity of hexagonal boron nitride monolayers. The MLIP is obtained using the Gaussian approximation potential method, and the resulting lattice dynamical properties and thermal conductivity are compared with those obtained from explicit frozen phonon calculations. It is observed that accurate thermal conductivity can be obtained based on MLIP constructed with about 30% representative configurations, and the high-order force constants provide a more reliable benchmark on the quality of MLIP than the harmonic approximation. Thermal conductivity ; Phonon ; Interatomic potential ; Boron nitride ; Materials science ; Condensed matter physics ; Work (physics) ; Gaussian ; Thermal ; Monolayer ; Thermodynamics ; Chemistry ; Nanotechnology ; Physics ; Molecular dynamics ; Composite material ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Nuraidayani Effendy | H.A.A. Sidek | Halimah Mohamed Kamari | Mohd Hafiz Mohd Zaid | Siti Aisyah Abdul Wahab
Source: Journal of Alloys and Compounds (article, 2020-04-29)
Field: Materials Science — Ceramics and Composites

Ultrasonic and artificial intelligence approach: Elastic behavior on the influences of ZnO in tellurite glass systems Elastic modulus ; Materials science ; Ultrasonic sensor ; Indentation hardness ; Binary number ; Artificial neural network ; Characterization (materials science) ; Oxide ; Ternary operation ; Natural bond orbital ; Mineralogy ; Composite material ; Microstructure ; Metallurgy ; Nanotechnology ; Computer science ; Mathematics ; Acoustics ; Density functional theory ; Chemistry ; Artificial intelligence ; Physics ; Computational chemistry Glass properties and applications ; Phase-change materials and chalcogenides ; Surface Roughness and Optical Measurements
Authors: Zhaoping Xiong | Ziqiang Cheng | Chi Xu | Xinyuan Lin | Xiaohong Liu | Dingyan Wang | Xiaomin Luo | Yong Zhang | Nan Qiao | Mingyue Zheng | Hualiang Jiang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-20)
Field: Materials Science — Materials Chemistry

Facing small and biased data dilemma in drug discovery with federated learning Abstract Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of these data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success of AI-powered drug discovery. We here simulate the federated learning process with 7 aqueous solubility datasets from different sources, among which there are overlapping molecules with high or low biases in the recorded values. Beyond the benefit of gaining more data, we also demonstrate federated training has a regularization effect making it superior than centralized training on the pooled datasets with high biases. Further, two more cases are studied to test the usability of federated learning in drug discovery. Our work demonstrates the application of federated learning in predicting drug related properties, but also highlights its promising role in addressing the small data and biased data dilemma in drug discovery. Computer science ; Dilemma ; Drug discovery ; Usability ; Data science ; Federated learning ; Data quality ; Machine learning ; Process (computing) ; Artificial intelligence ; Data mining ; Human–computer interaction ; Engineering ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Tong Liu | Hui Jiang | Quansheng Chen
Source: Microchemical Journal (article, 2020-07-29)
Field: Chemistry — Analytical Chemistry

Qualitative identification of rice actual storage period using olfactory visualization technique combined with chemometrics analysis Chemometrics ; Visualization ; Identification (biology) ; Computer science ; Chromatography ; Chemistry ; Data mining ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; GABA and Rice Research
Authors: Pascal Pernot | Bing Huang | Andreas Savin
Source: Machine Learning Science and Technology (article, 2020-06-30)
Field: Materials Science — Materials Chemistry

Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models Abstract Quantum machine learning models have been gaining significant traction within atomistic simulation communities. Conventionally, relative model performances are being assessed and compared using learning curves (prediction error vs. training set size). This article illustrates the limitations of using the Mean Absolute Error (MAE) for benchmarking, which is particularly relevant in the case of non-normal error distributions. We analyze more specifically the prediction error distribution of the kernel ridge regression with SLATM representation and L 2 distance metric (KRR-SLATM-L2) for effective atomization energies of QM7b molecules calculated at the level of theory CCSD(T)/cc-pVDZ. Error distributions of HF and MP2 at the same basis set referenced to CCSD(T) values were also assessed and compared to the KRR model. We show that the true performance of the KRR-SLATM-L2 method over the QM7b dataset is poorly assessed by the Mean Absolute Error, and can be notably improved after adaptation of the learning set. Benchmarking ; Computer science ; Approximation error ; Metric (unit) ; Ranking (information retrieval) ; Kernel (algebra) ; Mean absolute error ; Artificial intelligence ; Set (abstract data type) ; Mathematics ; Machine learning ; Algorithm ; Statistics ; Mean squared error ; Discrete mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Firooze Niazvand | Amir Cheshmi | Maryam Zand | Reyhaneh NasrAzadani | Beena Kumari | Ali Raza | Shima Nasibi
Source: Journal of Composites and Compounds (article, 2020-10-01)
Field: Materials Science — Biomaterials

An overview of the development of composites containing Mg and Zn for drug delivery Drug delivery is known as the administration of drugs using suitable vehicle for achieving effective treatment with no unwanted effects. In recent years, various composite materials have been developed and evaluated for being used in different biomedical fields such as wound dressings, cardiac prosthesis, tissue engineering, and drug delivery. Zinc is the second most available element after Fe in our body. Nanoparticles based on metal oxides, such as zinc oxides and Zn-containing composites, can be considered as viable platforms for some biomedical uses, especially for drug delivery applications. Mg composite biomaterials are also suggested for diverse biomedical applications due to their good mechanical properties, biocompatibility, and bioactivity. This paper highlights applications of zinc and magnesium-based composites in development of drug delivery systems. Biocompatibility ; Drug delivery ; Materials science ; Zinc ; Drug ; Composite number ; Nanotechnology ; Biomedical engineering ; Composite material ; Medicine ; Metallurgy ; Pharmacology Electrospun Nanofibers in Biomedical Applications ; Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties