jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xi Hu | Nan Wang | Xia Guo | Zeyu Liang | Heng Sun | Hongwei Liao | Fan Xia | Yunan Guan | Yoon Suk Lee | Daishun Ling | Fangyuan Li
Source: Nano-Micro Letters (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

A Sub-Nanostructural Transformable Nanozyme for Tumor Photocatalytic Therapy The structural change-mediated catalytic activity regulation plays a significant role in the biological functions of natural enzymes. However, there is virtually no artificial nanozyme reported that can achieve natural enzyme-like stringent spatiotemporal structure-based catalytic activity regulation. Here, we report a sub-nanostructural transformable gold@ceria (STGC-PEG) nanozyme that performs tunable catalytic activities via near-infrared (NIR) light-mediated sub-nanostructural transformation. The gold core in STGC-PEG can generate energetic hot electrons upon NIR irradiation, wherein an internal sub-nanostructural transformation is initiated by the conversion between CeO<sub>2</sub> and electron-rich state of CeO<sub>2-x</sub>, and active oxygen vacancies generation via the hot-electron injection. Interestingly, the sub-nanostructural transformation of STGC-PEG enhances peroxidase-like activity and unprecedentedly activates plasmon-promoted oxidase-like activity, allowing highly efficient low-power NIR light (50 mW cm<sup>-2</sup>)-activated photocatalytic therapy of tumors. Our atomic-level design and fabrication provide a platform to precisely regulate the catalytic activities of nanozymes via a light-mediated sub-nanostructural transformation, approaching natural enzyme-like activity control in complex living systems. Photocatalysis ; Medicine ; Chemistry Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Sarkar M. A. Kawsar | Ajoy Kumer | Nasrin S. Munia | Mohammed A. Hosen | Unesco Chakma | Shopnil Akash
Source: Organic Communications (article, 2022-06-28)
Field: Chemistry — Organic Chemistry

Chemical descriptors, PASS, molecular docking, molecular dynamics and ADMET predictions of glucopyranoside derivatives as inhibitors to bacteria and fungi growth The methyl -D-glucopyranoside and its derivatives have been estimated as the antimicrobial agents against numerous human pathogens, which is constantly amplifying the attention of medicinal chemists to design new bioactive molecules and their structure-activity relationship (SAR) while the computational tools are the most lucid and trustable avenue to perform their theoretical profile building up. Firstly, the predictionof activity spectra for substances (PASS) value has illustrated initially information about the antifungal, antibacterial, antiviral, and anticancer potential. It was observed that the PASS predicted pathogens supported their score higher in fungal species than bacteria. However, the "Lipinski five rule" has been monitored for drug-likeness properties. After confirming their biological significance, molecular docking has been completed against both the bacteria and fungi and these docked complexes have been optimized for molecular dynamics through the water system. A molecular docking study against nine bacterial and fungal pathogens revealed promising binding affinity and non-bonding interaction mostly for derivatives (5-8). The chemical descriptors have been obtained using the density functional theory (DFT) and predict their chemical stability and softness in the biological system. The molecular dynamics study was found to be the best stability of all docked complexes. At last, the ADMET properties have been calculated and provide the safe use and non-carcinogenic fact with low toxicity for both aquatic and non-aquatic species. Finally, it is concluded that these selected derivatives (5-8) are highly antifungal potential molecules than antibacterial potential which has been varied with respect to their structural side chain in the D-glucopyranoside sequence. Bacteria ; Docking (animal) ; Molecular dynamics ; Chemistry ; Computational biology ; Combinatorial chemistry ; Biology ; Nanotechnology ; Computational chemistry ; Biochemical engineering ; Materials science ; Engineering ; Genetics Synthesis and biological activity ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Hongliang Xin
Source: Nature Energy (article, 2022-09-07)
Field: Materials Science — Materials Chemistry

Catalyst design with machine learning Catalysis ; Perovskite (structure) ; Oxide ; Process engineering ; Process (computing) ; Materials science ; Range (aeronautics) ; Key (lock) ; Computer science ; Nanotechnology ; Chemical engineering ; Chemistry ; Engineering ; Metallurgy ; Composite material Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Rodayna Hmede | Frédéric Chapelle | Yuri Lapusta
Source: Sensors (review, 2022-07-27)
Field: Materials Science — Materials Chemistry

Review of Neural Network Modeling of Shape Memory Alloys Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning. Shape-memory alloy ; Artificial neural network ; Artificial intelligence ; Computer science ; Actuator ; SMA* ; Finite element method ; Deep learning ; Nonlinear system ; Smart material ; Robotics ; Computation ; Memory model ; Artificial muscle ; Automation ; Mechanical engineering ; Control engineering ; Robot ; Engineering ; Structural engineering ; Materials science ; Algorithm ; Nanotechnology Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Topology Optimization in Engineering
Authors: Nagababu Andraju | Greg W. Curtzwiler | Yun Ji | Evguenii Kozliak | Prakash Ranganathan
Source: ACS Applied Materials & Interfaces (review, 2022-09-14)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review There has been a tremendous increase in demand for virgin and postconsumer recycled (PCR) polymers due to their wide range of chemical and physical characteristics. Despite the numerous potential benefits of using a data-driven approach to polymer design, major hurdles exist in the development of polymer informatics due to the complicated hierarchical polymer structures. In this review, a brief introduction on virgin polymer structure, PCR polymers, compatibilization of polymers to be recycled, and their characterization using sensor array technologies as well as factors affecting the polymer properties are provided. Machine-learning (ML) algorithms are gaining attention as cost-effective scalable solutions to exploit the physical and chemical structures of polymers. The basic steps for applying ML in polymer science such as fingerprinting, algorithms, open-source databases, representations, and polymer design are detailed in this review. Further, a state-of-the-art review of the prediction of various polymer material properties using ML is reviewed. Finally, we discuss open-ended research questions on ML application to PCR polymers as well as potential challenges in the prediction of their properties using artificial intelligence for more efficient and targeted PCR polymer discovery and development. Polymer ; Materials science ; Scalability ; Computer science ; Compatibilization ; Polymer nanocomposite ; Nanotechnology ; Process engineering ; Polymer blend ; Copolymer ; Database ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Tony Wu | Andrés Aguilar‐Granda | Kazuhiro Hotta | Sahar Alasvand Yazdani | Robert Pollice | Jenya Vestfrid | Han Hao | Cyrille Lavigne | Martin Seifrid | Nicholas H. Angello | Fatima Bencheikh | Jason E. Hein | Martin D. Burke | Chihaya Adachi | Alán Aspuru‐Guzik
Source: Advanced Materials (article, 2022-11-14)
Field: Materials Science — Materials Chemistry

A Materials Acceleration Platform for Organic Laser Discovery Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, an automated platform is introduced for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. This platform encompasses automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, discovered eight potential candidates for organic lasers is discovered. The lasing threshold of four molecules in thin-film devices and find two molecules with state-of-the-art performance is tested. These promising results show the potential of automated synthesis and screening for accelerated materials development. Nanotechnology ; Materials science ; Characterization (materials science) ; Workflow ; Automation ; Lasing threshold ; Commercialization ; Laser ; Identification (biology) ; Computer science ; Systems engineering ; Mechanical engineering ; Optoelectronics ; Engineering ; Physics Machine Learning in Materials Science ; Nonlinear Optical Materials Studies ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hamada A. A. Noreldeen | Kai‐Yuan Huang | Gang-Wei Wu | Hua‐Ping Peng | Hao‐Hua Deng | Wei Chen
Source: Analytical Chemistry (article, 2022-06-18)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Sensor Array: 3D Fluorescence Spectra of Gold Nanoclusters for Qualitative and Quantitative Analysis of Vitamin B<sub>6</sub> Derivatives Vitamin B<sub>6</sub> derivatives (VB6Ds) are of great importance for all living organisms to complete their physiological processes. However, their excess in the body can cause serious problems. What is more, the qualitative and quantitative analysis of different VB6Ds may present significant challenges due to the high similarity of their chemical structures. Also, the transfer of deep learning model from one task to a similar task needs to be present more in the fluorescence-based biosensor. Therefore, to address these problems, two deep learning models based on the intrinsic fingerprint of 3D fluorescence spectra have been developed to identify five VB6Ds. The accuracy ranges of a deep neural network (DNN) and a convolutional neural network (CNN) were 94.44-97.77% and 97.77-100%, respectively. After that, the developed models were transferred for quantitative analysis of the selected VB6Ds at a broad concentration range (1-100 μM). The determination coefficient (<i>R</i><sup>2</sup>) values of the test set for DNN and CNN were 93.28 and 97.01%, respectively, which also represents the outperformance of CNN over DNN. Therefore, our approach opens new avenues for qualitative and quantitative sensing of small molecules, which will enrich fields related to deep learning, analytical chemistry, and especially sensor array chemistry. Chemistry ; Convolutional neural network ; Deep learning ; Nanoclusters ; Fluorescence ; Artificial intelligence ; Pattern recognition (psychology) ; Quantitative analysis (chemistry) ; Transfer of learning ; Set (abstract data type) ; Biological system ; Artificial neural network ; Analytical Chemistry (journal) ; Computer science ; Chromatography ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Christopher Kuenneth | Jessica Lalonde | Babetta L. Marrone | Carl N. Iverson | Rampi Ramprasad | Ghanshyam Pilania
Source: Communications Materials (article, 2022-12-03)
Field: Materials Science — Materials Chemistry

Bioplastic design using multitask deep neural networks Abstract Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials. Bioplastic ; Computer science ; Biochemical engineering ; Commodity ; Artificial intelligence ; Engineering ; Business ; Waste management Machine Learning in Materials Science ; biodegradable polymer synthesis and properties ; Chemistry and Chemical Engineering
Authors: Gyoung S. Na | Hyunju Chang
Source: npj Computational Materials (article, 2022-10-08)
Field: Materials Science — Materials Chemistry

A public database of thermoelectric materials and system-identified material representation for data-driven discovery Abstract Thermoelectric materials have received much attention as energy harvesting devices and power generators. However, discovering novel high-performance thermoelectric materials is challenging due to the structural diversity and complexity of the thermoelectric materials containing alloys and dopants. For the efficient data-driven discovery of novel thermoelectric materials, we constructed a public dataset that contains experimentally synthesized thermoelectric materials and their experimental thermoelectric properties. For the collected dataset, we were able to construct prediction models that achieved R 2 -scores greater than 0.9 in the regression problems to predict the experimentally measured thermoelectric properties from the chemical compositions of the materials. Furthermore, we devised a material descriptor for the chemical compositions of the materials to improve the extrapolation capabilities of machine learning methods. Based on transfer learning with the proposed material descriptor, we significantly improved the R 2 -score from 0.13 to 0.71 in predicting experimental ZTs of the materials from completely unexplored material groups. Thermoelectric materials ; Thermoelectric effect ; Extrapolation ; Materials science ; Dopant ; Thermoelectric generator ; Representation (politics) ; Computer science ; Doping ; Optoelectronics ; Thermodynamics ; Mathematics ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Sichao Li | Amanda S. Barnard
Source: Chemistry of Materials (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Inverse Design of MXenes for High-Capacity Energy Storage Materials Using Multi-Target Machine Learning There is significant interest in discovering high-capacity battery materials, prompting the investigation of the electrochemical energy storage potential of the two-dimensional early transition metal carbides known as MXenes. Predicting the relationship between the composition of a MXene and electrochemical properties is a focus of considerable research. In this paper we classify the specific MXene chemical formula using a new categorical descriptor and simultaneously predict multiple target electrochemical properties. We then invert the design challenge and predict the formula for MXenes based on a set of battery performance criteria. This approach involves a workflow that includes multi-target regression and multi-target classification, focusing on the physicochemical features most pertinent to battery design. The final inverse model recommends Li2M2C and Mg2M2C (M = Sc, Ti, Cr) as candidates for more focused research, based on desirable ranges of gravimetric capacity, voltage, and induced charge. MXenes ; Battery (electricity) ; Categorical variable ; Inverse ; Gravimetric analysis ; Computer science ; Workflow ; Materials science ; Machine learning ; Nanotechnology ; Chemistry ; Mathematics ; Physics ; Thermodynamics MXene and MAX Phase Materials ; Advancements in Battery Materials ; Advanced Memory and Neural Computing
Authors: Shoichi Ishida | Kei Terayama | Ryosuke Kojima | Kiyosei Takasu | Yasushi Okuno
Source: Journal of Chemical Information and Modeling (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called "ReTReK" that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development. Retrosynthetic analysis ; CASP ; Intuition ; Computer science ; Artificial intelligence ; Machine learning ; Engineering ; Protein structure prediction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Muhammad Kasim | Susi Lehtola | S. M. Vinko
Source: The Journal of Chemical Physics (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

DQC: A Python program package for differentiable quantum chemistry Automatic differentiation represents a paradigm shift in scientific programming, where evaluating both functions and their derivatives is required for most applications. By removing the need to explicitly derive expressions for gradients, development times can be shortened and calculations can be simplified. For these reasons, automatic differentiation has fueled the rapid growth of a variety of sophisticated machine learning techniques over the past decade, but is now also increasingly showing its value to support ab initio simulations of quantum systems and enhance computational quantum chemistry. Here, we present an open-source differentiable quantum chemistry simulation code and explore applications facilitated by automatic differentiation: (1) calculating molecular perturbation properties, (2) reoptimizing a basis set for hydrocarbons, (3) checking the stability of self-consistent field wave functions, and (4) predicting molecular properties via alchemical perturbations. Automatic differentiation ; Python (programming language) ; Computer science ; Differentiable function ; Quantum chemistry ; Ab initio ; Computational science ; Quantum ; Theoretical computer science ; Computational chemistry ; Chemistry ; Programming language ; Physics ; Quantum mechanics ; Molecule ; Mathematics ; Supramolecular chemistry ; Pure mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Lipeng Jiang | Xue Jiang | Changxin Wang | Pei Liu | Yan Zhang | Guocai Lv | Turab Lookman | Yanjing Su
Source: ACS Applied Materials & Interfaces (article, 2022-11-09)
Field: Materials Science — Materials Chemistry

Rapid Discovery of Efficient Long-Wavelength Emission Garnet:Cr NIR Phosphors via Multi-Objective Optimization High-efficiency long-wavelength emission near-infrared (NIR) phosphors are the key to next-generation LED light sources. However, high-efficiency phosphors usually exhibit narrow-band emission at shorter wavelengths due to the crystal field intensity. In this paper, we utilize multi-objective optimization to discover the NIR phosphor Gd<sub>3</sub>Mg<sub>0.5</sub>Al<sub>1.5</sub>Ga<sub>2.5</sub>Ge<sub>0.5</sub>O<sub>12</sub>:0.04Cr<sup>3+</sup>. It exhibits a broadband NIR emission from 650 to 1100 nm peaking at 763 nm, with a full width at half maximum (FWHM) of 150 nm, an internal quantum efficiency (IQE)/external quantum efficiency (EQE) of 90%/53.1%, and good thermal stability (85.3% @ 150 °C). The packaging results show that ∼53.2 mW of output power is achieved at 915 mW input power, which suggests promising applications for NIR pc-LED. Our approach is based on the data of emission wavelength (WL) and IQE for garnet:Cr NIR phosphors to construct machine learning models. An active learning strategy is used to make tradeoffs between WL and IQE, and we are able to find the targeted phosphor after only four iterations of synthesis and characterization. Phosphor ; Materials science ; Quantum efficiency ; Full width at half maximum ; Optoelectronics ; Wavelength ; Emission intensity ; Near-infrared spectroscopy ; Optics ; Luminescence ; Physics Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties