jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiang Fu | Tian Xie | Andrew Rosen | Tommi Jaakkola | Jake A. Smith
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Chemistry — Inorganic Chemistry

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modular nature has enabled the use of template-based methods to generate hypothetical MOFs by combining molecular building blocks in accordance with known network topologies. However, the ability of these methods to identify top-performing MOFs is often hindered by the limited diversity of the resulting chemical space. In this work, we propose MOFDiff: a coarse-grained (CG) diffusion model that generates CG MOF structures through a denoising diffusion process over the coordinates and identities of the building blocks. The all-atom MOF structure is then determined through a novel assembly algorithm. Equivariant graph neural networks are used for the diffusion model to respect the permutational and roto-translational symmetries. We comprehensively evaluate our model's capability to generate valid and novel MOF structures and its effectiveness in designing outstanding MOF materials for carbon capture applications with molecular simulations. Modular design ; Computer science ; Metal-organic framework ; Diffusion ; Graph ; Topology (electrical circuits) ; Nanotechnology ; Materials science ; Theoretical computer science ; Chemistry ; Engineering ; Physics Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Boyu Zhao | Jiahao Cheng | Junlong Gao | David M. Haddleton | Paul Wilson
Source: Macromolecular Chemistry and Physics (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Active Learning as a Tool for Optimizing “Plug‐n‐Play” Electrochemical Atom Transfer Radical Polymerization Abstract A recently reported “plug‐n‐play” approach to simplified electrochemical atom transfer radical polymerization (seATRP) is investigated using machine learning. It is shown that Bayesian optimization via an active learning (AL) algorithm accelerates optimization of the polymerization of oligo(ethylene glycol methyl ether acrylate) 480 (OEGA 480 ) in water. Molecular weight distribution ( M w / M n ; dispersity; Ɖ m ) is the output selected for optimization targeting poly(oligo[ethylene glycol methyl ether acrylate]) (POEGA 480 ) with low dispersity ( Ɖ m &lt; 1.30). Input variables included applied potential ( E app ), [M] and [M]/[I], which led to a potential space of 275 possible reaction conditions. From a training data set of seven reactions, selected to yield uncontrolled POEGA 480 with higher dispersities ( Ɖ m &gt; 1.5), ten iteration loops are performed. During each iteration the algorithm suggests the next reaction conditions. The reactions are then performed and the conversion, number average molecular weight ( M n ) and Ɖ m values are recorded and the Ɖ m values fed back into the algorithm. Overall, 80% of the experiments yield POEGA with Ɖ m &lt; 1.30. Conversely, only 30% of experiments performed using reaction conditions selected at random from the possible reaction space yield POEGA with Ɖ m &lt; 1.30. This study suggests that adopting AL methods can improve the efficiency of optimizing a given seATRP reaction. Dispersity ; Ethylene glycol ; Atom-transfer radical-polymerization ; Polymerization ; Yield (engineering) ; Molar mass distribution ; Acrylate ; Methyl acrylate ; Ether ; Polymer chemistry ; Electrochemistry ; Chemistry ; Materials science ; Monomer ; Physical chemistry ; Organic chemistry ; Polymer ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Liangyong Wang | Yaolong Zhu | Chenyang Gan
Source: Journal of Process Control (article, 2023-06-17)
Field: Materials Science — Materials Chemistry

Nonlinear model predictive control of crystal size in batch cooling crystallization processes Model predictive control ; Standard deviation ; Nonlinear system ; Crystallization ; Control theory (sociology) ; Artificial neural network ; Crystal (programming language) ; Mathematics ; Computer science ; Engineering ; Control (management) ; Artificial intelligence ; Statistics ; Physics Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Chromatography in Natural Products
Authors: Tarek Iraki | Lukas Morand | Johannes Dornheim | Norbert Link | Dirk Helm
Source: Journal of Intelligent Manufacturing (article, 2023-05-26)
Field: Materials Science — Materials Chemistry

A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties Abstract Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties. Microstructure ; Task (project management) ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Optimization problem ; Artificial neural network ; Identification (biology) ; Core (optical fiber) ; Machine learning ; Algorithm ; Pattern recognition (psychology) ; Materials science ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Industrial Vision Systems and Defect Detection
Authors: Juan C. Zapata Trujillo | Maria M. Pettyjohn | L. K. McKemmish
Source: Monthly Notices of the Royal Astronomical Society (article, 2023-07-04)
Field: Chemistry — Spectroscopy

High-throughput quantum chemistry: empowering the search for molecular candidates behind unknown spectral signatures in exoplanetary atmospheres ABSTRACT The identification of molecules in exoplanetary atmospheres is only possible thanks to the availability of high-resolution molecular spectroscopic data. However, due to its intensive and time-consuming generation process, at present, only 100 molecules have high-resolution spectroscopic data available, limiting new molecular detections. Using routine quantum chemistry calculations (i.e. scaled harmonic frequency calculations using the B97-1/def2-TZVPD model chemistry with median errors of 10 cm−1), here we present a complementary high-throughput approach to rapidly generate approximate vibrational spectral data for 2743 molecules made from the biologically most important elements C, H, N, O, P, and S. Though these data are not accurate enough to enable definitive molecular detections and do not seek to replace the need for high-resolution data, it has powerful applications in identifying potential molecular candidates responsible for unknown spectral features. We explore this application for the $4.1\,\mu{\rm m}$ (2439 cm−1) feature in the atmospheric spectrum of WASP-39b, listing potential alternative molecular species responsible for this spectral line, together with SO2. Further applications of this big data compilation also include identifying molecules with strong absorption features that are likely detectable at quite low abundances and providing a training set for machine learning predictions of vibrational frequencies. Characterizing exoplanetary atmospheres through molecular spectroscopy is essential to understanding the planet’s physico-chemical processes and likelihood of hosting life. Our rapidly generated quantum chemistry big data set will play a crucial role in supporting this understanding by giving directions into possible initial identifications of the more unusual molecules to emerge. Physics ; Quantum chemistry ; Molecule ; Astrophysics ; Quantum mechanics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: Kishalay Das | Bidisha Samanta | Pawan Goyal | Seung‐Cheol Lee | Satadeep Bhattacharjee | Niloy Ganguly
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

CrysGNN: Distilling Pre-trained Knowledge to Enhance Property Prediction for Crystalline Materials In recent years, graph neural network (GNN) based approaches have emerged as a powerful technique to encode complex topological structure of crystal materials in an enriched repre- sentation space. These models are often supervised in nature and using the property-specific training data, learn relation- ship between crystal structure and different properties like formation energy, bandgap, bulk modulus, etc. Most of these methods require a huge amount of property-tagged data to train the system which may not be available for different prop- erties. However, there is an availability of a huge amount of crystal data with its chemical composition and structural bonds. To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge amount of unla- belled material data. Further, we extract distilled knowledge from CrysGNN and inject into different state of the art prop- erty predictors to enhance their property prediction accuracy. We conduct extensive experiments to show that with distilled knowledge from the pre-trained model, all the SOTA algo- rithms are able to outperform their own vanilla version with good margins. We also observe that the distillation process provides significant improvement over the conventional ap- proach of finetuning the pre-trained model. We will release the pre-trained model along with the large dataset of 800K crys- tal graph which we carefully curated; so that the pre-trained model can be plugged into any existing and upcoming models to enhance their prediction accuracy. Leverage (statistics) ; Computer science ; ENCODE ; Artificial intelligence ; Distillation ; Graph ; Property (philosophy) ; Machine learning ; Artificial neural network ; Crystal (programming language) ; Data mining ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Geochemistry and Geologic Mapping
Authors: Ji-Hun Oh | Anna Oldani | Alex Solecki | Tonghun Lee
Source: Fuel (article, 2023-08-30)
Field: Chemistry — Analytical Chemistry

Learning to predict sustainable aviation fuel properties: A deep uncertainty quantification viewpoint Uncertainty quantification ; Heteroscedasticity ; Jet fuel ; Computer science ; Aviation ; Dempster–Shafer theory ; Probabilistic logic ; Artificial intelligence ; Deep learning ; Aviation fuel ; Trustworthiness ; Machine learning ; Chemistry ; Aerospace engineering ; Engineering Petroleum Processing and Analysis ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Jonas Busk | Mikkel N. Schmidt | Ole Winther | Tejs Vegge | Peter Bjørn Jørgensen
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Graph neural network interatomic potential ensembles with calibrated aleatoric and epistemic uncertainty on energy and forces Inexpensive machine learning (ML) potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framework for training and recalibrating graph neural network ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated <i>post hoc</i> using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy. The method is demonstrated and evaluated on two challenging, publicly available datasets, ANI-1x (Smith <i>et al. J. Chem. Phys.</i>, 2018, <b><i>148</i></b>, 241733.) and Transition1x (Schreiner <i>et al. Sci. Data</i>, 2022, <b>9</b>, 779.), both containing diverse conformations far from equilibrium. A detailed analysis of the predictive performance and uncertainty calibration is provided. In all experiments, the proposed method achieved low prediction error and good uncertainty calibration, with predicted uncertainty correlating with expected error, on energy and forces. To the best of our knowledge, the method presented in this paper is the first to consider a complete framework for obtaining calibrated epistemic and aleatoric uncertainty predictions on both energy and forces in ML potentials. Uncertainty quantification ; Artificial neural network ; Statistical physics ; Graph ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Theoretical computer science Machine Learning in Materials Science ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: Dinor Nagar | Nikita Vladimirov | Christian T. Farrar | Or Perlman
Source: Scientific Reports (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch-McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 63-99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson's r > 0.99, p < 0.0001, normalized root mean square error < 3%). Magnetization transfer ; Computer science ; Magnetic resonance imaging ; Biological system ; Ground truth ; In silico ; Saturation (graph theory) ; Artificial intelligence ; Nuclear magnetic resonance ; Magnetization ; Mean squared error ; Chemistry ; Physics ; Magnetic field ; Mathematics ; Biology ; Statistics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Ryan C. Fortenberry
Source: ACS Physical Chemistry Au (review, 2023-12-08)
Field: Chemistry — Spectroscopy

A Vision for the Future of Astrochemistry in the Interstellar Medium by 2050 By 2050, many, but not nearly all, unattributed astronomical spectral features will be conclusively linked to molecular carriers (as opposed to nearly none today in the visible and IR); amino acids will have been observed remotely beyond our solar system; the largest observatories ever constructed on the surface of the Earth or launched beyond it will be operational; high-throughput computation either from brute force or machine learning will provide unprecedented amounts of reference spectral and chemical reaction data; and the chemical fingerprints of the universe delivered by those of us who call ourselves astrochemists will provide astrophysicists with unprecedented resolution for determining how the stars evolve, planets form, and molecules that lead to life originate. Astrochemistry is a relatively young field, but with the entire universe as its playground, the discipline promises to persist as long as telescopic observations are made that require reference data and complementary chemical modeling. While the recent commissionings of the <i>James Webb Space Telescope</i> and Atacama Large Millimeter Array are ushering in the second "golden age" of astrochemistry (with the first being the radio telescopic boom period of the 1970s), this current period of discovery should facilitate unprecedented advances within the next 25 years. Astrochemistry forces the asking of hard questions beyond the physical conditions of our "pale blue dot", and such questions require creative solutions that are influential beyond astrophysics. By 2050, more creative solutions will have been provided, but even more will be needed to answer the continuing question of our astrochemical ignorance. Astrochemistry ; Astrobiology ; Astronomy ; Planet ; Physics ; Solar System ; Astrophysics ; Interstellar medium ; Galaxy Molecular Spectroscopy and Structure ; Astrophysics and Star Formation Studies ; Atmospheric Ozone and Climate
Authors: Luneng Zhao | Yuan Chang | Shi Qiu | Hongsheng Liu | Jijun Zhao | Junfeng Gao
Source: Advanced Energy and Sustainability Research (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

High Mechanical Energy Storage Capacity of Ultranarrow Carbon Nanowires Bundles by Machine Learning Driving Predictions Energy storage and renewable energy sources are critical for addressing the growing global energy demand and reducing the negative environmental impacts of fossil fuels. Carbon nanomaterials are extensively explored as high reliable, reusable, and high‐density mechanical energy storage materials. In this context, machine learning techniques, specifically machine learning potentials (MLPs), are employed to explore the elastic properties of 1D carbon nanowires (CNWs) as a promising candidate for mechanical energy storage applications. The study focuses on the elastic energy storage properties of these CNWs, utilizing MLPs trained with data from first‐principles molecular dynamics simulations. It is found that these materials exhibit an exceptionally high tensile elastic energy storage capacity, with a maximum storage density ranging from 2262 to 2680 kJ kg −1 . Furthermore, it is discovered that some CNWs exhibit a superior torsional energy storage capacity compared to their tensile energy storage capacity. Overall, this research demonstrates the effectiveness of machine learning‐based computational approaches in accelerating the exploration and optimization of novel materials. It also highlights the potential of CNWs as promising candidates for future energy storage applications. Energy storage ; Renewable energy ; Materials science ; Context (archaeology) ; Fossil fuel ; Computer data storage ; Nanotechnology ; Computer science ; Process engineering ; Mechanical engineering ; Engineering ; Power (physics) ; Electrical engineering ; Physics ; Waste management Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Supercapacitor Materials and Fabrication
Authors: Chang‐Ho Hong | Jaehoon Kim | Jaesun Kim | Jisu Jung | Suyeon Ju | Jeong Min Choi | Seungwu Han
Source: Science and Technology of Advanced Materials Methods (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Applications and training sets of machine learning potentials Recently, machine learning potentials (MLPs) have been attracting interest as an alternative to the computationally expensive density-functional theory (DFT) calculations. The data-driven approach in MLPs requires carefully curated training datasets, which define the valid domain of simulations. Therefore, acquiring training datasets that comprehensively span the domain of the desired simulations is important. In this review, we attempt to set guidelines for the systematic construction of training datasets according to target simulations. To this end, we extensively analyze the training sets in previous literature according to four application types: thermal properties, diffusion properties, structure prediction, and chemical reactions. In each application, we summarize characteristic reference structures and discuss specific parameters for DFT calculations such as MD conditions. We hope this review serves as a comprehensive guide for researchers and practitioners aiming to harness the capabilities of MLPs in material simulations. Computer science ; Machine learning ; Training set ; Domain (mathematical analysis) ; Set (abstract data type) ; Artificial intelligence ; Training (meteorology) ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Nikolay Kondratyuk | R. E. Ryltsev | Vladimir Ankudinov | N. M. Chtchelkatchev
Source: Journal of Molecular Liquids (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

First-principles calculations of the viscosity in multicomponent metallic melts: Al-Cu-Ni as a test case Viscosity ; Ternary operation ; Thermodynamics ; Eutectic system ; Materials science ; Molecular dynamics ; Chemistry ; Statistical physics ; Computer science ; Physics ; Alloy ; Metallurgy ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Block Copolymer Self-Assembly