jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junde Li | Swaroop Ghosh
Source: 2022 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE) (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. Variational autoencoder is one of the computer-aided design methods which explores the chemical space based on an existing molecular dataset. Quantum machine learning has emerged as an atypical learning method that may speed up some classical learning tasks because of its strong expressive power. However, near-term quantum computers suffer from limited num-ber of qubits which hinders the representation learning in high dimensional spaces. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs. Extensive experimental results are reported for different dimensions including 8x8 and 32x32 after choosing suitable architectural strategies. The performance of quantum generative autoencoder is compared with the corre-sponding classical counterpart throughout all experiments. The results show that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period. Autoencoder ; Computer science ; Quantum computer ; Qubit ; Scalability ; Quantum ; Artificial intelligence ; Theoretical computer science ; Quantum machine learning ; Deep learning ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Osamu Sakai | Satoru Kawaguchi | Tomoyuki Murakami
Source: Japanese Journal of Applied Physics (review, 2022-06-08)
Field: Materials Science — Materials Chemistry

Complexity visualization, dataset acquisition, and machine-learning perspectives for low-temperature plasma: a review Abstract Low-temperature plasma plays various roles in industrial material processing as well as provides a number of scientific targets, both from theoretical and experimental points of view. Such rich features in variety are based on its complexities, arising from diverse parameters in constituent gas-phase species, working gas pressure, input energy density, and spatial boundaries. When we consider causalities in these complexities, direct application of machine-learning methods is not always possible since levels of complexities are so high in comparison with other scientific research targets. To overcome this difficulty, progresses in plasma diagnostics and data acquisition systems are inevitable, and the handling of a large number of data elements is one of the key issues for this purpose. In this topical review, we summarize previous and current achievements of visualization, acquisition, and analysis methods for complex plasma datasets which may open a scientific and technological category mixed with rapid machine-learning advancements and their relevant outcomes. Although these research trends are ongoing, many reports published so far have already convinced us of various expanding aspects of low-temperature plasma leading to the potential for scientific progress as well as developments of intellectual design in industrial plasma processes. Visualization ; Computer science ; Data science ; Variety (cybernetics) ; Artificial intelligence Machine Learning in Materials Science ; Semiconductor materials and devices ; Laser-induced spectroscopy and plasma
Authors: Lan Yang | Jichen Li | Feiyang Chen | Kuang Yu
Source: The Journal of Chemical Physics (article, 2022-11-16)
Field: Materials Science — Materials Chemistry

A transferrable range-separated force field for water: Combining the power of both physically-motivated models and machine learning techniques An accurate, transferrable, and computationally efficient potential energy surface is of paramount importance for all molecular mechanics simulations. In this work, by using water as an example, we demonstrate how one can construct a reliable force field by combining the advantages of both physically motivated and data-driven machine learning methods. Different from the existing water models based on molecular many-body expansion, we adopt a separation scheme that is completely based on distances, which is more convenient for generic molecular systems. The geometry dependence of atomic charges and dispersion coefficients are also introduced to improve the accuracy of the long-range part of the potential. The new potential provides a physically interpretable energy decomposition, and it is more accurate than the conventional physically motived potentials. Most importantly, through this study, we show that the information we learn from small clusters can be extrapolated into larger systems, thus providing a general recipe for the intermolecular force field development at the coupled-cluster singles and doubles plus perturbative triples level of theory in the future. Force field (fiction) ; Range (aeronautics) ; Computer science ; Field (mathematics) ; Statistical physics ; Intermolecular force ; Construct (python library) ; Cluster (spacecraft) ; Work (physics) ; Energy (signal processing) ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Molecule ; Aerospace engineering ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Min Li | Dayuan Wang | Chifang Peng | Zhouping Wang
Source: Sensors and Actuators B Chemical (article, 2022-12-16)
Field: Chemistry — Analytical Chemistry

Simultaneous qualitative and quantitative analysis of flavonols in Kaempferia galangal L. and honey by machine learning-based fluorescence sensor array Flavonols ; Myricetin ; Kaempferol ; Chemistry ; Quercetin ; Sensor array ; Fluorescence ; Analytical Chemistry (journal) ; Chromatography ; Biological system ; Computer science ; Machine learning ; Biochemistry ; Biology ; Antioxidant Spectroscopy and Chemometric Analyses ; Medicinal Plants and Neuroprotection ; Advanced Chemical Sensor Technologies
Authors: Patrizio Graziosi | Zhen Li | Neophytos Neophytou
Source: Applied Physics Letters (article, 2022-02-14)
Field: Materials Science — Materials Chemistry

Bipolar conduction asymmetries lead to ultra-high thermoelectric power factor Low bandgap thermoelectric materials suffer from bipolar effects at high temperatures, with increased electronic thermal conductivity and reduced Seebeck coefficient, leading to a reduced power factor and a low ZT figure of merit. In this work we show that the presence of strong transport asymmetries between the conduction and valence bands can allow high phonon-limited electronic conductivity at finite Seebeck coefficient values, leading to largely enhanced power factors. The power factors that can be achieved can be significantly larger compared to their maximum unipolar counterparts, allowing for doubling of the ZT figure of merit. We identify this behavior in low-bandgap cases from the half-Heusler material family. Using both advanced electronic Boltzmann transport calculations for realistic material band structures and model parabolic electronic bands, we elaborate on the parameters that determine this effect. We then develop a series of descriptors that can guide machine learning studies in identifying such classes of materials with extraordinary power factors at nearly undoped conditions. For this we test more than 3000 analytical band structures and their features, and more than 120 possible descriptors, to identify the most promising ones that contain: (i) only band structure features for easy identification from material databases and (ii) band structure and transport parameters that provide much higher correlations, but for which parameter availability can be somewhat more scarce. Figure of merit ; Seebeck coefficient ; Condensed matter physics ; Thermoelectric effect ; Thermal conductivity ; Materials science ; Band gap ; Thermoelectric materials ; Thermal conduction ; Power factor ; Electronic band structure ; Boltzmann constant ; Engineering physics ; Optoelectronics ; Power (physics) ; Thermodynamics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Zhijie Qin | Zhang Zhong-fu | Xiangdong Hua | Wanneng Yang | Xiuying Liang | Ruifang Zhai | Chenglong Huang
Source: Scientific Reports (article, 2022-02-24)
Field: Chemistry — Analytical Chemistry

Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 s per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The best accuracy for indica and japonica identification was 99.950%, while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research. Preprocessor ; Point cloud ; Segmentation ; Identification (biology) ; Computer science ; Artificial intelligence ; Cluster analysis ; Extraction (chemistry) ; Materials science ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Xiaoning Wang | Haidi Wang | Qiquan Luo | Jinlong Yang
Source: The Journal of Chemical Physics (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

Structural and electrocatalytic properties of copper clusters: A study via deep learning and first principles Determining the atomic structure of clusters has been a long-term challenge in theoretical calculations due to the high computational cost of density-functional theory (DFT). Deep learning potential (DP), as an alternative way, has been demonstrated to be able to conduct cluster simulations with close-to DFT accuracy but at a much lower computational cost. In this work, we update 34 structures of the 41 Cu clusters with atomic numbers ranging from 10 to 50 by combining global optimization and the DP model. The calculations show that the configuration of small Cu<sub>n</sub> clusters (n = 10-15) tends to be oblate and it gradually transforms into a cage-like configuration as the size increases (n > 15). Based on the updated structures, their relative stability and electronic properties are extensively studied. In addition, we select three different clusters (Cu<sub>13</sub>, Cu<sub>38</sub>, and Cu<sub>49</sub>) to study their electrocatalytic ability of CO<sub>2</sub> reduction. The simulation indicates that the main product is CO for these three clusters, while the selectivity of hydrocarbons is inhibited. This work is expected to clarify the ground-state structures and fundamental properties of Cu<sub>n</sub> clusters, and to guide experiments for the design of Cu-based catalysts. Copper ; Materials science ; Nanotechnology ; Computer science ; Metallurgy Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Prabahar Ravichandran | Sadhasivam Viswanathan | Sridhar Ravichandran | Ya‐Jun Pan | Young Chang
Source: Cereal Chemistry (article, 2022-03-13)
Field: Chemistry — Analytical Chemistry

Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning Abstract Background and Objectives Grain quality is a complex trait in rice, compared with other staple crops as it is predominantly consumed as a whole grain. Although considered secondary to yield, to align with consumer preferences, breeders are increasingly interested in quality. At the early stages of a breeding program, grain quality‐related traits are often ignored as they are arduous and time‐consuming. Near‐infrared spectroscopy (NIRS) could be a suitable high‐throughput alternative to conventional wet chemistry and image processing‐related methods to be adopted for early screening. This study aims to quantify traits essential for rice breeders such as amylose, chalkiness, length, width, and the length/width ratio in rice samples with NIRS. We used conventional algorithms such as principal component analysis (PCA), partial least square regression (PLSR), multilayer perceptron (MLP), support vector classification (SVC), and linear discriminant analysis (LDA) to compare with the proposed convolutional neural network (CNN) for regression and classification. Findings Our results showed that the proposed CNN outperformed the conventional models in estimating all traits. Unlike conventional models, CNN models could be developed with raw spectra with minimal to no preprocessing, and along with the transfer‐learning capabilities, the time required for model development could be significantly reduced. Conclusion We recommend NIRS for quantitative estimation of amylose and chalkiness in rice and rather use classification/categorized estimation for other physical dimension‐related traits such as length and length/width ratio. Significance and Novelty We found NIRS to be an appropriate alternative to wet chemistry and image‐based methods for screening lines at the early stages of the breeding program. Estimation of physical parameters such as length and length/width ratio with NIRS is novel and appears reasonable for high‐throughput applications. Principal component analysis ; Pattern recognition (psychology) ; Linear discriminant analysis ; Artificial intelligence ; Preprocessor ; Mathematics ; Partial least squares regression ; Computer science ; Biological system ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Food composition and properties
Authors: Ruoxi Sun | Dai, Hanjun | Yu, Adams Wei
Source: arXiv (Cornell University) (preprint, 2022-07-13)
Field: Materials Science — Materials Chemistry

Does GNN Pretraining Help Molecular Representation? Extracting informative representations of molecules using Graph neural networks (GNNs) is crucial in AI-driven drug discovery. Recently, the graph research community has been trying to replicate the success of self-supervised pretraining in natural language processing, with several successes claimed. However, we find the benefit brought by self-supervised pretraining on small molecular data can be negligible in many cases. We conduct thorough ablation studies on the key components of GNN pretraining, including pretraining objectives, data splitting methods, input features, pretraining dataset scales, and GNN architectures, to see how they affect the accuracy of the downstream tasks. Our first important finding is, self-supervised graph pretraining do not always have statistically significant advantages over non-pretraining methods in many settings. Secondly, although noticeable improvement can be observed with additional supervised pretraining, the improvement may diminish with richer features or more balanced data splits. Thirdly, hyper-parameters could have larger impacts on accuracy of downstream tasks than the choice of pretraining tasks, especially when the scales of downstream tasks are small. Finally, we provide our conjectures where the complexity of some pretraining methods on small molecules might be insufficient, followed by empirical evidences on different pretraining datasets. Replicate ; Computer science ; Graph ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yao‐Te Yen | Yu‐Syuan Lin | Yin‐Jue Chang | Ming‐Ta Li | San‐Chong Chyueh | Huan‐Tsung Chang
Source: Advanced Materials Technologies (article, 2022-06-22)
Field: Materials Science — Materials Chemistry

Nanomaterial‐Based Sensor Arrays With Deep Learning for Screening of Illicit Drugs Abstract Rapid and accurate screening techniques are demanded for illicit drugs that have raised international tensions. Bovine serum albumin‐stabilized gold nanoclusters (BSA‐Au NCs), carbon dots (C dots), thiosalicylic acid‐stabilized silver nanoclusters (TA‐Ag NCs), and Marquis reagent as photoluminescent sensing probes for five common illicit drugs are demonstrated in this study. Cocaine, 4‐chloroethcathinone (4‐CEC), and ketamine induce different degrees of photoluminescence changes of BSA‐Au NCs, C dots, and TA‐Ag NCs. Detection of heroin and methamphetamine (MA) is based on their formation of fluorescence polymer particles with Marquis reagent. To provide a unique pattern for each analyte, 2 × 4 sensor arrays are prepared. A deep learning‐drug screening platform and system (DL‐DSPS) is established and applied to differentiate the five illicit drugs, each with unique code based on its response to the probes. For example, codes of (−1, −1, 1, 0), (0, −1, 1, 0), (0, 0, 1, 0), (0, 0, 1, 1), and (0, 0, 0, 1) are for 4‐CEC, cocaine, ketamine, heroin, and MA, respectively. The cost‐effective and compact DL‐DSPS is validated for screening of real‐world illicit drug samples, with results in good agreement with that from GC‐MS, showing its potential for multi‐drug screening at crime scenes. Nanoclusters ; Analyte ; Heroin ; Materials science ; Nanomaterials ; Drug detection ; Photoluminescence ; Reagent ; Drug ; Fluorescence ; Nanotechnology ; Illicit drug ; Nanoparticle ; Drug delivery ; Bovine serum albumin ; Chemistry ; Chromatography ; Organic chemistry ; Pharmacology ; Optoelectronics ; Medicine ; Physics Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications ; Mercury impact and mitigation studies
Authors: Tao Wang | Xiaoxing Tan | Yadong Wei | Hao Jin
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Unveiling the layer-dependent electronic properties in transition-metal dichalcogenide heterostructures assisted by machine learning The electronic properties of layered two-dimensional (2D) transition-metal dichalcogenide (TMD) van der Waals (vdW) heterostructures are strongly dependent on their layer number (N). However, extremely large computational resources are required to investigate the layer-dependent TMD vdW heterostructures for every possible combination if <i>N</i> varies in a large range. Fortunately, the machine learning (ML) technique provides a feasible way to probe this problem. In this work, based on the density functional theory (DFT) calculations combined with the ML technique, we effectively predict the layer-dependent electronic properties of TMD vdW heterostructures composed of MoS<sub>2</sub>, WS<sub>2</sub>, MoSe<sub>2</sub>, WSe<sub>2</sub>, MoTe<sub>2</sub>, or WTe<sub>2</sub>, in which the layer number varies from 2-10. The cross-validation scores of our trained ML models in predicting the bandgaps as well as the band edge positions exceed 90%, suggesting excellent performance. The predicted results show that in the case of a few-layer system, the number of layers has a significant effect on the electronic properties. The bandgap and band alignment could be dramatically changed from bilayer to triple-layer heterostructures. However, with the increase of the number of layers, the electronic properties change, and some general trends can be summarized. When the layer number is larger than 8, the properties of the TMD heterostructures tend to be stable, and the influence of the layer number decreases. Based on these results, our work not only sheds light on the understanding of the layer-dependent electronic properties of multi-layer TMD vdW heterostructures, but also provides an efficient way to accelerate the discovery of functional materials. Heterojunction ; Transition metal ; Layer (electronics) ; van der Waals force ; Materials science ; Transition layer ; Electronic structure ; Nanotechnology ; Metal ; Condensed matter physics ; Chemical physics ; Optoelectronics ; Chemistry ; Physics ; Molecule ; Metallurgy 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Xinyue Han | Dandan Li | Shurong Wang | Yuxiang Lin | Yun Liu | Ling Lin | Liang Qiao
Source: Materials Today Bio (article, 2022-11-04)
Field: Chemistry — Spectroscopy

Serum amino acids quantification by plasmonic colloidosome-coupled MALDI-TOF MS for triple-negative breast cancer diagnosis Triple-negative breast cancer (TNBC) is characterized with high diffusion, metastasis and recurrence. The early treatment and early diagnosis of TNBC are highly important for the survival of TNBC patients. Nevertheless, traditional methods for TNBC diagnosis, i.e. imaging examinations and tissue biopsy, cannot achieve early diagnosis, are invasive and associated with the risk of arousing tumor spreading. Herein, we developed colloidosomes-coupled matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to quantify free serum amino acids for the diagnosis of TNBC. Gold nanoparticles (AuNPs) were used to form water-in-oil colloidosomes and to encapsulate deproteinated serum for MALDI-TOF MS analysis. With small sample spot size (200-300 μm) and densely packed AuNPs monolayer, the dried sample spot from colloidosomes can facilitate MALDI-TOF MS analysis of small molecule metabolites with high sensitivity, high reproducibility and good quantification performance. We used the method to quantify free amino acids in human serum. A cohort of 30 TNBC patients, 30 breast lump patients and 30 healthy controls were recruited for the study. It was found that the concentrations of free amino acids in TNBC patients were significantly lower than that of heathy controls, and the concentrations were also significantly different from that of breast lump patients. Based on the quantities of serum free amino acids, we have built a machine learning-based classification model to differentiate TNBC patients from the controls, including healthy controls and breast lump patients, and the sensitivity, specificity and accuracy were 95%, 100% and 97%, respectively. The assay consumes less than 1 ​μL serum per analysis, and takes only minutes to analyze a sample. With the advantages of low cost, low sample consumption, high throughput in analysis and high accuracy in identification, the non-invasive liquid biopsy method is promising to be applied to clinical diagnosis of TNBC. Triple-negative breast cancer ; Breast cancer ; Amino acid ; Matrix-assisted laser desorption/ionization ; Medicine ; Reproducibility ; Cancer ; Internal medicine ; Chemistry ; Chromatography ; Desorption ; Biochemistry ; Organic chemistry ; Adsorption Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies