jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dunn, A | Wang, Q | Ganose, A | Dopp, D | Jain, A
Source:  (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~104 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer. Benchmarking ; Algorithm ; Property (philosophy) ; Computer science ; Test (biology) ; Set (abstract data type) ; Data mining ; Programming language ; Geology Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Zhan Ban | Peng Yuan | Fubo Yu | Ting Peng | Qixing Zhou | Xiangang Hu
Source: Proceedings of the National Academy of Sciences (article, 2020-04-24)
Field: Materials Science — Biomaterials

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by <i>R</i><sup>2</sup> (less than 0.40). Here, the performance with <i>R</i><sup>2</sup> over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good <i>R</i><sup>2</sup> (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty. Corona (planetary geology) ; Nanomedicine ; Nanotoxicology ; Nanoparticle ; Proteomics ; Chemistry ; Biophysics ; Nanotechnology ; Materials science ; Computational biology ; Biology ; Biochemistry Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Polymer Surface Interaction Studies
Authors: Yun Zhang | Xiaojie Xu
Source: Optik (article, 2020-05-05)
Field: Materials Science — Materials Chemistry

Machine learning optical band gaps of doped-ZnO films Materials science ; Dopant ; Band gap ; Doping ; Spintronics ; Grain size ; Microstructure ; Thin film ; Optoelectronics ; Engineering physics ; Nanotechnology ; Condensed matter physics ; Composite material ; Ferromagnetism ; Physics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Chalcogenide Semiconductor Thin Films
Authors: Chuan Jing | Biqin Dong | Yuxin Zhang
Source: Energy & environment materials (article, 2020-08-01)
Field: Materials Science — Materials Chemistry

Chemical Modifications of Layered Double Hydroxides in the Supercapacitor Layered double hydroxides are one class or kind of 2D layered materials that are considered promising for use in the supercapacitor. Although there have been many studies on the structure, composition, properties, and electrode fabrication of layered double hydroxides, none summarize the effects of various modification methods on the structure and performance of layered double hydroxides in the supercapacitor. In a bid to fill this gap, in this review, we summarize the progress of modification methods such as exfoliation, intercalation, vacancy, doping, phase transformation, and composition regulation of layered double hydroxides in the field of the supercapacitor and put forward some opinions regarding the progress of research on the methods used in modifying the layered double hydroxides. Layered double hydroxides ; Supercapacitor ; Exfoliation joint ; Materials science ; Intercalation (chemistry) ; Double layered ; Fabrication ; Electrode ; Nanotechnology ; Inorganic chemistry ; Graphene ; Chemistry ; Composite material ; Electrochemistry ; Hydroxide Layered Double Hydroxides Synthesis and Applications ; Supercapacitor Materials and Fabrication ; Polyoxometalates: Synthesis and Applications
Authors: Hongtao Zhang | Huadong Fu | Xingqun He | Changsheng Wang | Lei Jiang | Long‐Qing Chen | Jianxin Xie
Source: Acta Materialia (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening Materials science ; Electronegativity ; Ultimate tensile strength ; Electrical conductor ; Atomic radius ; Electrical resistivity and conductivity ; RADIUS ; Toughness ; Range (aeronautics) ; Dielectric ; Conductivity ; Core (optical fiber) ; Composite material ; Computer science ; Optoelectronics ; Electrical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Corrosion Behavior and Inhibition
Authors: Rongxuan Zhao | Lingling Huang | Yongtian Wang
Source: PhotoniX (article, 2020-10-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances in multi-dimensional metasurfaces holographic technologies Abstract Holography has attracted tremendous interest due to its capability of storing both the amplitude and phase of light field and reproducing vivid three-dimensional scenes. However, the large pixel size, low resolution, small field-of-view (FOV) and limited space-bandwidth of traditional spatial light modulator (SLM) devices restrict the possibility of improving the quality of reconstructed images. With the development of nanofabrication technologies, metasurfaces have shown great potential in manipulating the amplitude, phase, polarization, frequency or simultaneously multiple parameters of output light in ultrashort distance with subwavelength resolution by tailoring the scattering behaviour of consisted nanostructures. Such flexibilities make metasurface a promising candidate for holographic related applications. Here, we review recent progresses in the field of metasurface holography. From the perspective of the fundamental properties of light, we classify the metasurface holography into several categories such as phase-only holography, amplitude-only holography, complex amplitude holography and so on. Then, we introduce the corresponding working principles and design strategies. Meanwhile, some emerging types of metasurface holography such as tunable holography, nonlinear holography, Janus (or directional related) and bilayer metasurfaces holography are also discussed. At last, we make our outlook on metasurface holography and discuss the challenges we may face in the future. Holography ; Optics ; Computer-generated holography ; Digital holography ; Light field ; Holographic display ; Bandwidth (computing) ; Amplitude ; Computer science ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Optical Imaging Technologies
Authors: Barbara Mikulak-Klucznik | Patrycja Gołębiowska | Alison A. Bayly | Oskar Popik | Tomasz Klucznik | Sara Szymkuć | Ewa Gajewska | Piotr Dittwald | Olga Staszewska‐Krajewska | Wiktor Beker | Tomasz Badowski | Karl A. Scheidt | Karol Molga | Jacek Młynarski | Milan Mrksich | Bartosz A. Grzybowski
Source: Nature (article, 2020-10-13)
Field: Chemistry — Organic Chemistry

Computational planning of the synthesis of complex natural products Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years<sup>1-7</sup>. However, the field has progressed greatly since the development of early programs such as LHASA<sup>1,7</sup>, for which reaction choices at each step were made by human operators. Multiple software platforms<sup>6,8-14</sup> are now capable of completely autonomous planning. But these programs 'think' only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary<sup>15,16</sup> and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program's knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships<sup>17,18</sup>, allowing it to 'strategize' over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization. Natural (archaeology) ; Chemistry ; Computer science ; Biochemical engineering ; Biology ; Engineering ; Paleontology Chemical synthesis and alkaloids ; Microbial Natural Products and Biosynthesis ; Plant biochemistry and biosynthesis
Authors: Alexander Dunn | Qi Wang | Alex Ganose | Daniel Dopp | Anubhav Jain
Source: npj Computational Materials (article, 2020-09-15)
Field: Materials Science — Materials Chemistry

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer. Test suite ; Benchmark (surveying) ; Suite ; Random forest ; Test set ; Hyperparameter ; Benchmarking ; Test data Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Selection and Properties
Authors: Chen Chen | Jie Hou | John J. Tanner | Jianlin Cheng
Source: International Journal of Molecular Sciences (review, 2020-04-20)
Field: Chemistry — Spectroscopy

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks. Proteomics ; Computational biology ; Computer science ; Bioinformatics ; Identification (biology) ; Quantitative proteomics ; Data science ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Bioinformatics and Genomic Networks
Authors: Felix Strieth‐Kalthoff | Frederik Sandfort | Marwin Segler | Frank Glorius
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine learning the ropes: principles, applications and directions in synthetic chemistry Machine learning (ML) has emerged as a general, problem-solving paradigm with many applications in computer vision, natural language processing, digital safety, or medicine. By recognizing complex patterns in data, ML bears the potential to modernise the way how many chemical challenges are approached. In this review, an introduction to ML is given from the perspective of synthetic chemistry: starting from the fundamentals regarding algorithms and best-practice workflows, the review covers different applications of machine learning in synthesis planning, property prediction, molecular design, and reactivity prediction. In particular, different approaches of representing and utilizing organic molecules will be discussed - providing synthetic chemists both with the understanding and the tools required to apply machine learning in the context of their research, and pointers for further studying. Chemist ; Perspective (graphical) ; Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Justin S. Smith | R.I. Zubatyuk | Benjamin Nebgen | Nicholas Lubbers | Kipton Barros | Adrián E. Roitberg | Olexandr Isayev | Sergei Tretiak
Source: Scientific Data (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry. Extrapolation ; Density functional theory ; Multipole expansion ; Coupled cluster ; Data set ; Quantum chemistry ; Computer science ; Dimensionality reduction ; Chemistry ; Molecule ; Statistical physics ; Physics ; Computational chemistry ; Computational science ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Supramolecular chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Brian C. Searle | Kristian E. Swearingen | Christopher A. Barnes | Tobias Schmidt | Siegfried Gessulat | Bernhard Küster | Mathias Wilhelm
Source: Nature Communications (article, 2020-03-25)
Field: Chemistry — Spectroscopy

Generating high quality libraries for DIA MS with empirically corrected peptide predictions Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa. Computer science ; Workflow ; Proteome ; Plasmodium falciparum ; Computational biology ; Fragmentation (computing) ; Biology ; Database ; Malaria ; Bioinformatics ; Operating system Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Bacterial Identification and Susceptibility Testing
Authors: Nataša Avramović | Boris Mandić | Ana Savić-Radojević | Tatjana Simić
Source: Pharmaceutics (review, 2020-03-25)
Field: Materials Science — Biomaterials

Polymeric Nanocarriers of Drug Delivery Systems in Cancer Therapy Conventional chemotherapy is the most common therapeutic method for treating cancer by the application of small toxic molecules thatinteract with DNA and causecell death. Unfortunately, these chemotherapeutic agents are non-selective and can damage both cancer and healthy tissues,producing diverse side effects, andthey can have a short circulation half-life and limited targeting. Many synthetic polymers have found application as nanocarriers of intelligent drug delivery systems (DDSs). Their unique physicochemical properties allow them to carry drugs with high efficiency,specificallytarget cancer tissue and control drug release. In recent years, considerable efforts have been made to design smart nanoplatforms, including amphiphilic block copolymers, polymer-drug conjugates and in particular pH- and redox-stimuli-responsive nanoparticles (NPs). This review is focused on a new generation of polymer-based DDSs with specific chemical functionalities that improve their hydrophilicity, drug loading and cellular interactions.Recentlydesigned multifunctional DDSs used in cancer therapy are highlighted in this review. Nanocarriers ; Drug delivery ; Drug ; Nanotechnology ; Cancer therapy ; Cancer ; Drug carrier ; Nanomedicine ; Cancer treatment ; Pharmacology ; Materials science ; Medicine ; Nanoparticle ; Internal medicine Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Advanced Polymer Synthesis and Characterization
Authors: Shao‐Hua Xiang | Bin Tan
Source: Nature Communications (article, 2020-07-29)
Field: Chemistry — Organic Chemistry

Advances in asymmetric organocatalysis over the last 10 years Beyond esoteric interest, organocatalysis has now become one major pillar of asymmetric catalysis. Here, we discuss how new activation modes are conquering challenging stereoselective transformations and the recent integration of organocatalysis with emerging photo- and electrocatalysis, as well as artificial intelligence. Organocatalysis has become a major pillar of (asymmetric) catalysis. Here, the authors discuss recent trends in organocatalytic activation modes for challenging stereoselective transformations and the emerging integration with other fields, such as photoredox catalysis and electrosynthesis. Organocatalysis ; Enantioselective synthesis ; Pillar ; Electrocatalyst ; Chemistry ; Nanotechnology ; Catalysis ; Materials science ; Organic chemistry ; Engineering Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Synthesis and Catalysis
Authors: Weiwei Wu | Liang Huang | Erkang Wang | Shaojun Dong
Source: Chemical Science (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Atomic engineering of single-atom nanozymes for enzyme-like catalysis Enzyme mimics, especially nanozymes, play a crucial role in replacing natural enzymes for diverse applications related to bioanalysis, therapeutics and other enzyme-like catalysis. Nanozymes are catalytic nanomaterials with enzyme-like properties, which currently face formidable challenges with respect to their intricate structure, properties and mechanism in comparison with enzymes. The latest emergence of single-atom nanozymes (SAzymes) undoubtedly promoted the nanozyme technologies to the atomic level and provided new opportunities to break through their inherent limitations. In this perspective, we discuss key aspects of SAzymes, including the advantages of the single-site structure, and the derived synergetic enhancements of enzyme-like activity, catalytic selectivity and the mechanism, as well as the superiority in biological and catalytic applications, and then highlight challenges that SAzymes face and provide relevant guidelines from our point of view for the rational design and extensive applications of SAzymes, so that SAzyme may achieve its full potential as the next-generation nanozyme. Catalysis ; Atom (system on chip) ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Computer science ; Organic chemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Electrochemical sensors and biosensors