jablonkagroup/rise_ai_scientists huggingface dataset:
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: ChemRxiv (preprint, 2022-02-16)
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, we introduce an automated platform for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. Our platform encompassed 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, we have discovered 8 potential candidates for organic lasers. We tested the lasing threshold of 4 molecules in thin-film devices and found 2 molecules with state-of-the-art performance. These promising results show the potential of automated synthesis and screening for accelerated materials development. Workflow ; Automation ; Characterization (materials science) ; Computer science ; Nanotechnology ; Lasing threshold ; Laser ; Commercialization ; Identification (biology) ; Materials science ; Engineering ; Mechanical engineering ; Physics ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nonlinear Optical Materials Studies
Authors: Venkateswaran Shekar | Vincent F. Yu | Benjamin J. Garcia | D. Benjamin Gordon | Gemma E. Moran | David M. Blei | Loı̈c M. Roch | Alberto García-Durán | Mansoor Ani Najeeb | Margaret Zeile | Philip W. Nega | Zhi Li | Mina A. Kim | Emory M. Chan | Alexander J. Norquist | Sorelle A. Friedler | Joshua Schrier
Source: ChemRxiv (preprint, 2022-07-20)
Field: Materials Science — Materials Chemistry

Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models Machine learning is a useful tool for accelerating materials discovery, however it is a challenge to develop accurate methods that successfully transfer between domains while also broadening the scope of reaction conditions considered. In this paper, we consider how active- and transfer-learning methods can be used as building blocks for predicting reaction outcomes of metal halide perovskite synthesis. We then introduce a serendipity-based recommendation system that guides these methods to balance novelty and accuracy. The model-agnostic recommendation system is tested across active- and transfer-learning algorithms, using laboratory experiments for training and testing and a time-separated hold out that includes four different chemical systems. The serendipity recommendation system achieves high accuracy while increasing the scope of the synthesis conditions explored. Serendipity ; Novelty ; Scope (computer science) ; Recommender system ; Computer science ; Transfer of learning ; Knowledge transfer ; Machine learning ; Artificial intelligence ; Data science ; Knowledge management Machine Learning in Materials Science ; Expert finding and Q&A systems ; Advanced Photocatalysis Techniques
Authors: Tatiana Konstantinova | Lutz Wiegart | Maksim Rakitin | Anthony M. DeGennaro | Andi Barbour
Source: Physical Review Research (article, 2022-09-21)
Field: Materials Science — Materials Chemistry

Machine Learning for analysis of speckle dynamics: quantification and outlier detection X-ray photon correlation spectroscopy (XPCS) provides an understanding of complex dynamics in materials that are tied to their synthesis, properties, and behaviors. Analysis of XPCS data for dynamics that are far from equilibrium is labor intense and often can impede the discovery process, especially in experiments with high collection rates. Moreover, binning and averaging, involved in the analysis for alleviating poor signal-to-noise ratio, leads to a loss of temporal resolution and the accumulation of systematic error for the parameters quantifying the dynamics. Here, we integrate a denoising autoencoder model into workflows for the analysis of nonequilibrium two-time intensity-intensity correlation functions. Noise reduction allows for extracting the parameters that characterize the sample's dynamics with the temporal resolution limited only by frame rates. Not only does it improve the quantitative usage of the data, but it also creates the potential for automating the analytical workflow, which is a key to high-throughput or autonomous XPCS experiments. Various approaches for the uncertainty quantification and extension of the model for anomalies' detection are discussed. Computer science ; Outlier ; Artificial intelligence ; Autoencoder ; Noise (video) ; Temporal resolution ; Anomaly detection ; Machine learning ; Pattern recognition (psychology) ; Data mining ; Biological system ; Deep learning ; Optics ; Physics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Nuclear Physics and Applications
Authors: Nathaniel H. Park | Matteo Manica | Jannis Born | Dmitry Yu. Zubarev | Nil Adell Mill | James L. Hedrick | Pedro L. Arrechea | Tim Erdmann
Source: ChemRxiv (preprint, 2022-06-27)
Field: Materials Science — Materials Chemistry

An extensible software platform for accelerating polymer discovery through informatics and artificial intelligence development The inherent architectural complexity and stochasticity of polymeric materials combined with their myriad application domains present significant challenges for accurate data modeling and representation—hindering development of new artificial intelligence (AI) models and informatics needed to accelerate polymer research and discovery. To address these difficulties, we developed an extensible and re-composable data model for the organization of experimental data coupled with a flexible (hyper)graph representations of polymeric materials and supramolecular complexes. Implemented in a web application, these advances enabled the curation of more than 2600 experimental records across disparate experiment types, including small-molecule organic synthesis, polymer synthesis, supramolecular complex synthesis, and the assay of antimicrobial materials. The unified dataset enabled creation of chem- and polymer informatics and generative models for new ring-opening polymerization catalysts. These results demonstrate how an extensible data model and flexible polymer representation enable a customizable approach for unifying experimental materials data across multiple domains and provides a critical link to merge AI and automation in accelerated discovery workflows. Workflow ; Computer science ; Extensibility ; Automation ; Informatics ; Software ; Data science ; Merge (version control) ; Software engineering ; Database ; Programming language ; Engineering ; Information retrieval Machine Learning in Materials Science ; Scientific Computing and Data Management ; Data Visualization and Analytics
Authors: D. K. Aswal | S. V. Nakhe | Prashant Shukla | Nishant Chaudhary | Tapas Ganguli | B. N. Upadhyay
Source: Current Science (article, 2022-08-10)
Field: Materials Science — Materials Chemistry

An overview of key enabling technologies for DAE’s nuclear programme Homi Jehangir Bhabha envisioned that the success of India's nuclear energy programme would depend upon how soon the country becomes self-reliant in the manufacturing of required essential technologies, e.g. ultra-high vacuum systems, cryogenics and associated technologies, precision electronics, control and instrumentation, robotics, particle accelerators, plasma, lasers, etc. He initiated R&D programmes at TIFR, Mumbai, on few of these technologies immediately after taking over as Director in 1945, and expanded them once DAE was formed in 1954. In this article, we present a brief overview of R&D carried out by DAE during the past seven decades in the areas of key enabling technologies needed for the advancement of our nuclear programme. The tremendous efforts of DAE resulted in achieving selfreliance in the nuclear power programme as well as in the development of necessary technologies for investigation of fundamental science and for facilitation of societal and industrial growth in the country. DAE led the nation to not only become a partner in several international science megaprojects, e.g. LHC, FAIR, Fermilab, ITER, etc. but also took a leadership position in national megaprojects, i.e. MACE, LIGO-India and ADS. Key (lock) ; Computer science ; Process management ; Systems engineering ; Engineering ; Computer security Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering
Authors: Paul Jusner | Stefano Barbini | Sonja Schiehser | Markus Bacher | Elisabeth Schwaiger | Antje Potthast | Thomas Rosenau
Source: Cellulose (article, 2022-08-23)
Field: Materials Science — Biomaterials

Impact of residual extractives on the thermal stability of softwood Kraft pulp Abstract Wood extractives are known to cause problems during the production of pulp and paper and to impact their mechanical properties. In the present work, we systematically explored whether minute residual amounts of extractives that reprecipitate on the fiber surface in the final stages of the pulping process would lower the thermal resilience of softwood Kraft pulp. Pulp samples subjected to different extraction methods were characterized by means of thermogravimetric analysis. The compound classes constituting the respective extracts were identified and quantified via gas chromatography-mass spectrometry/flame ionization detector (GC-MS/FID) and the influence of the individual compound classes on the thermal stability of the samples was investigated separately. Soxhlet extraction with an ethanol/toluene solvent system clearly led to increased thermal stability. Besides fatty acids and resin acids also sterols and sterol esters that do not carry carboxylic acid functionalities contributed to the deterioration of the thermal pulp properties. Lateral order indices derived from Fourier transformed infrared spectra and crystallinity indices derived from solid-state 13 C nuclear magnetic resonance spectra indicate that upon thermal degradation, the presence of even minute amounts of extractives leads to an increased degree of supramolecular disorder. Moreover, the characterization of the samples’ carbohydrate composition by means of acidic hydrolysis showed a tendency towards facilitated pyrolysis of the hemicelluloses if extractives were present in the pulp. Pulp (tooth) ; Kraft process ; Softwood ; Thermal stability ; Chemistry ; Kraft paper ; Gas chromatography ; Flame ionization detector ; Hemicellulose ; Thermogravimetric analysis ; Lignin ; Hot water extraction ; Chromatography ; Materials science ; Organic chemistry ; Extraction (chemistry) ; Composite material Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Biofuel production and bioconversion
Authors: Hongliang Zhang | Kun Li | Hai Jin | Kangle Li | Xiaonan Li | Peng Liu | Zongren Peng
Source: Frontiers in Chemistry (article, 2022-06-15)
Field: Materials Science — Materials Chemistry

Space Charge Dynamics in Epoxy Resins Under the Influence of a Long-Term High Electric Field at Various Temperatures As the main insulating material of an ultra-high voltage (UHV) resin impregnated paper (RIP) bushing, epoxy resin accumulates space charge under the DC voltage, which would affect the insulation properties of the bushing. In this article, the evolution of space charge accumulation and dissipation within the epoxy resin samples was measured under the influence of a long-term (24 h) electric field of 60 kV/mm at 40°, 60°, 80°, and 100°C. The experimental results show that the space charge transport in the epoxy sample was a long-time dynamic process. When the temperature was not greater than 60°C, only homocharge accumulation was observed within the samples, and the space charge distribution did not reach a stable state even after 24 h of voltage application. When the temperature was not lower than 80°C, the positive heterocharge accumulation was observed at the interface between cathode and epoxy. Through analysis by fitting and numerical simulation, it was verified that the positive heterocharge accumulation was caused by the limitation of the hole extraction, and the transport of holes within the samples obeyed the jump conductance model. Epoxy ; Bushing ; Space charge ; Materials science ; Electric field ; Composite material ; Voltage ; Cathode ; Analytical Chemistry (journal) ; Electrical engineering ; Chemistry ; Electron ; Physics ; Physical chemistry ; Chromatography High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Kai Li | Jifeng Wang | Yuanyuan Song | Ying Wang
Source: Research Square (preprint, 2022-11-09)
Field: Materials Science — Materials Chemistry

Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries Abstract Development of ionic polymer electrolytes (IPEs) without flammable organics is a critical strategy to enable safe and high-energy lithium metal batteries (LMBs). As critical components in IPEs, ionic liquids (ILs) with high ionic conductivity and wide electrochemical window are promising candidates to enable LMBs. Here, we describe a fast and robust machine learning workflow embedded with quantum chemistry calculation and graph convolutional neural network to discover promising ILs for IPEs. By selecting subsets of the recommended ILs, combining with a rigid-rod polyelectrolyte and a predetermined lithium salt, we develop a class of large area and mechanically strong IPE membranes with thickness ~ 50 μm. The symmetric cells exhibit stable cycling performance at 1 mA cm -1 (0.5 mAh cm -2 ) up to 800 h at room temperature (RT) and excellent reversibility at 6 mA cm -2 (3 mAh cm -2 ) at 80 °C. With LiFePO 4 loading ~ 10.3 mg cm -2 , the full cells deliver outstanding capacity retention for &gt; 350 cycles (&gt; 96% with 0.5 C at RT; &gt; 80% with 2 C at 50 °C), fast charge/discharge capability (146 mAh g -1 with 5 C at 80 °C) and ultrahigh coulombic efficiency (&gt; 99.92%). This performance is rarely reported by any single-layer polymer electrolytes without any organic plasticizers/oligomers for LMBs. Polymer electrolytes ; Lithium metal ; Electrolyte ; Ionic bonding ; Lithium (medication) ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Ionic conductivity ; Ion ; Psychology ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: David A. Winkler
Source: Australian Journal of Chemistry (article, 2022-09-13)
Field: Materials Science — Materials Chemistry

Probing the properties of molecules and complex materials using machine learning The application of machine learning to predicting the properties of small and large discrete (single) molecules and complex materials (polymeric, extended or mixtures of molecules) has been increasing exponentially over the past few decades. Unlike physics-based and rule-based computational systems, machine learning algorithms can learn complex relationships between physicochemical and process parameters and their useful properties for an extremely diverse range of molecular entities. Both the breadth of machine learning methods and the range of physical, chemical, materials, biological, medical and many other application areas have increased markedly in the past decade. This Account summarises three decades of research into improved cheminformatics and machine learning methods and their application to drug design, regenerative medicine, biomaterials, porous and 2D materials, catalysts, biomarkers, surface science, physicochemical and phase properties, nanomaterials, electrical and optical properties, corrosion and battery research. Cheminformatics ; Nanotechnology ; Molecular machine ; Computer science ; Process (computing) ; Machine learning ; Materials science ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Dominik Lemm | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2022-05-11)
Field: Materials Science — Materials Chemistry

Improved decision making with similarity based machine learning: Applications in chemistry Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, 'the bigger the data the better'. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific queries, enabling decision making in data scarce scenarios in chemistry. By solely relying on query and training data proximity to choose training points, only a fraction of data is necessary to converge to competitive performance. After introducing SML for the harmonic oscillator and the Rosenbrock function, we describe applications to scarce data scenarios in chemistry which include quantum mechanics based molecular design and organic synthesis planning. Finally, we derive a relationship between the intrinsic dimensionality and volume of feature space, governing the overall model accuracy. Curse of dimensionality ; Computer science ; Scarcity ; Similarity (geometry) ; Chemical space ; Machine learning ; Artificial intelligence ; Space (punctuation) ; Feature (linguistics) ; Data mining ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Dhia K. Suker
Source: Engineering Technology & Applied Science Research (article, 2022-04-09)
Field: Materials Science — Materials Chemistry

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy Indexing of Electron Backscatter Diffraction (EBSD) is a well-established method of crystalline material characterization that provides phase and orientation information about the crystals on the material surface. A deep learning Convolutional Neural Network was trained to predict crystal orientation from the EBSD patterns based on the mean disorientation error between the predicted crystal orientation and the ground truth. The CNN is trained using EBSD images for different deformation conditions of AA5083. Electron backscatter diffraction ; Materials science ; Convolutional neural network ; Orientation (vector space) ; Artificial intelligence ; Crystal (programming language) ; Deformation (meteorology) ; Crystallography ; Pattern recognition (psychology) ; Computer science ; Microstructure ; Metallurgy ; Composite material ; Geometry ; Mathematics ; Chemistry Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; X-ray Diffraction in Crystallography
Authors: Rahul Mukherjee | Shubhabrata Datta
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Biomaterials

Designing Mg alloys – A machine learning approach Ultimate tensile strength ; Artificial neural network ; Materials science ; Alloy ; Elongation ; Automotive industry ; Aerospace ; Toughness ; Mechanical engineering ; Machine learning ; Computer science ; Composite material ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Hydrogen Storage and Materials
Authors: Oliver Christensen | Rasmus D. Schlosser | Rasmus Buus Nielsen | Jes Johansen | Mads Koerstz | Jan H. Jensen | Kurt V. Mikkelsen
Source: The Journal of Physical Chemistry A (article, 2022-03-04)
Field: Materials Science — Materials Chemistry

A Neural Network Approach for Property Determination of Molecular Solar Cell Candidates The dihydroazulene/vinylheptafulvene (DHA/VHF) photocouple is a promising candidate for molecular solar heat batteries, storing and releasing energy in a closed cycle. Much work has been done on improving the energy storage capacity and the half-life of the high-energy isomer via substituent functionalization, but similarly important is keeping these improved properties in common polar solvents, along with being soluble in these, which is tied to the dipole properties. However, the number of possible derivatives makes an overview of this combinatorial space impossible both for experimental work and traditional computational chemistry. Due to the time-consuming nature of running many thousands of computations, we look to machine learning, which bears the advantage that once a model has been trained, it can be used to rapidly estimate approximate values for the given system. Applying a convolutional neural network, we show that it is possible to reach good agreement with traditional computations on a scale that allows us to rapidly screen tens of thousands of the DHA/VHF photocouple, eliminating bad candidates and allowing computational resources to be directed toward meaningful compounds. Computation ; Computer science ; Work (physics) ; Convolutional neural network ; Property (philosophy) ; Chemical space ; Artificial neural network ; Scale (ratio) ; Artificial intelligence ; Energy (signal processing) ; Biological system ; Biochemical engineering ; Chemistry ; Algorithm ; Engineering ; Mathematics ; Mechanical engineering ; Drug discovery ; Physics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Organic Chemistry Cycloaddition Reactions
Authors: Yong Zhao | Edirisuriya M. Dilanga Siriwardane | Zhenyao Wu | Nihang Fu | Mohammed Al‐Fahdi | Ming Hu | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2022-03-27)
Field: Materials Science — Materials Chemistry

Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700\% compared to FTCP, one of the latest structure generators and by more than 45\% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1,869 materials out of 2,000 are successfully optimized and deposited into the Carolina Materials Database \url{www.carolinamatdb.org}, of which 39.6\% have negative formation energy and 5.3\% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability. Generative grammar ; Symmetry (geometry) ; Task (project management) ; Heuristic ; Stability (learning theory) ; Crystal (programming language) ; Density functional theory ; Atom (system on chip) ; Generative model ; Computer science ; Diversity (politics) ; Artificial intelligence ; Physics ; Materials science ; Machine learning ; Mathematics ; Engineering ; Geometry ; Quantum mechanics ; Systems engineering ; Sociology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials