jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Magnus Kløve | Sanna Sommer | Bo B. Iversen | Bjørk Hammer | Wilke Dononelli
Source: Advanced Materials (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

A Machine‐Learning‐Based Approach for Solving Atomic Structures of Nanomaterials Combining Pair Distribution Functions with Density Functional Theory Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid-state chemistry and physics. Pair distribution function (PDF) analysis of X-ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases, a reliable structural motif is needed as a starting configuration for structure refinements. Here, an algorithm that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model, which combines density functional theory calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape, is presented. Due to the nature of this landscape, even metastable configurations and stacking disorders can be identified. Pair distribution function ; Materials science ; Nanocrystalline material ; Metastability ; Density functional theory ; Nanomaterials ; Stacking ; Crystal structure ; Neutron scattering ; Amorphous solid ; Crystal structure prediction ; Statistical physics ; Distribution function ; Chemical physics ; Biological system ; Scattering ; Nanotechnology ; Computational chemistry ; Crystallography ; Physics ; Quantum mechanics ; Chemistry ; Nuclear magnetic resonance X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystal Structures and Properties
Authors: Daniel Varivoda | Rongzhi Dong | Sadman Sadeed Omee | Jianjun Hu
Source: Applied Physics Reviews (article, 2023-05-23)
Field: Materials Science — Materials Chemistry

Materials property prediction with uncertainty quantification: A benchmark study Uncertainty quantification (UQ) has increasing importance in the building of robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on gathering new training data from uncertain regions. There are several categories of UQ methods, each considering different types of uncertainty sources. Here, we conduct a comprehensive evaluation on the UQ methods for graph neural network-based materials property prediction and evaluate how they truly reflect the uncertainty that we want in error bound estimation or active learning. Our experimental results over four crystal materials datasets (including formation energy, adsorption energy, total energy, and bandgap properties) show that the popular ensemble methods for uncertainty estimation are NOT always the best choice for UQ in materials property prediction. For the convenience of the community, all the source code and datasets can be accessed freely at https://github.com/usccolumbia/materialsUQ. Uncertainty quantification ; Computer science ; Property (philosophy) ; Benchmark (surveying) ; Propagation of uncertainty ; Code (set theory) ; Source code ; Machine learning ; Artificial intelligence ; Data mining ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xinhu Xu | Lifeng Ma | Hui Guo | Chang‐Ping Feng | YanSong Wang | Zhian Mao
Source: Composites Science and Technology (article, 2023-05-22)
Field: Materials Science — Polymers and Plastics

A predictive model for the relationship between processing conditions and properties of thermoplastic vulcanizates (TPVs) via machine learning Materials science ; Artificial neural network ; Composite material ; Thermoplastic ; Work (physics) ; Polymer ; Mechanical engineering ; Computer science ; Machine learning Polymer Nanocomposites and Properties
Authors: Rebecca Birolo | Federica Bravetti | Eugenio Alladio | Emanuele Priola | Gianluca Bianchini | Rubina Novelli | Andrea Aramini | Roberto Gobetto | Michele R. Chierotti
Source: Crystal Growth & Design (article, 2023-10-19)
Field: Chemistry — Physical and Theoretical Chemistry

Speeding Up the Cocrystallization Process: Machine Learning-Combined Methods for the Prediction of Multicomponent Systems Pharmaceutical cocrystals are crystalline materials composed of at least two molecules, i.e., an active pharmaceutical ingredient (API) and a coformer, assembled by noncovalent forces. Cocrystallization is successfully applied to improve the physicochemical properties of APIs, such as solubility, dissolution profile, pharmacokinetics, and stability. However, choosing the ideal coformer is a challenging task in terms of time, efforts, and laboratory resources. Several computational tools and machine learning (ML) models have been proposed to mitigate this problem. However, the challenge of achieving a robust and generalizable predictive method is still open. In this study, we propose a new approach to quickly predict the formation of cocrystals, employing partial least squares-discriminant analysis, random forest, and neural networks. The models were based on the data sets of 13 structurally different APIs with both positive and negative cocrystallization outcomes. At the same time, the features were specially selected from a variety of molecular descriptors to explain the phenomenon of the cocrystallization. All of the proposed ML models showed a cross-validation accuracy higher than 83%. Furthermore, this approach was successfully applied to drive the cocrystallization experimental tests of 2-phenylpropionic acid, showcasing the high potential of the ML models in practice. Computer science ; Process (computing) ; Cheminformatics ; Machine learning ; Active ingredient ; Stability (learning theory) ; Artificial intelligence ; Biochemical engineering ; Biological system ; Chemistry ; Computational chemistry ; Engineering ; Bioinformatics Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Supriya Atta | Joy Qiaoyi Li | Tuan Vo‐Dinh
Source: The Analyst (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis Polycyclic aromatic hydrocarbons (PAHs) have attracted a lot of environmental concern because of their carcinogenic and mutagenic properties, and the fact they can easily contaminate natural resources such as drinking water and river water. This study presents a simple and sensitive point-of-care SERS detection of PAHs combined with machine learning algorithms to predict the PAH content more precisely and accurately in real-life samples such as drinking water and river water. We first synthesized multibranched sharp-spiked surfactant-free gold nanostars (GNSs) that can generate strong surface-enhanced Raman scattering (SERS) signals, which were further coated with cetyltrimethylammonium bromide (CTAB) for long-term stability of the GNSs as well as to trap PAHs. We utilized CTAB-capped GNSs for solution-based 'mix and detect' SERS sensing of various PAHs including pyrene (PY), nitro-pyrene (NP), anthracene (ANT), benzo[a]pyrene (BAP), and triphenylene (TP) spiked in drinking water and river water using a portable Raman module. Very low limits of detection (LOD) were achieved in the nanomolar range for the PAHs investigated. More importantly, the detected SERS signal was reproducible for over 90 days after synthesis. Furthermore, we analyzed the SERS data using artificial intelligence (AI) with machine learning algorithms based on the convolutional neural network (CNN) model in order to discriminate the PAHs in samples more precisely and accurately. Using a CNN classification model, we achieved a high prediction accuracy of 90% in the nanomolar detection range and an f1 score (harmonic mean of precision and recall) of 94%, and using a CNN regression model, achieved an RMSE<sub>conc</sub> = 1.07 × 10<sup>-1</sup> μM. Overall, our SERS platform can be effectively and efficiently used for the accurate detection of PAHs in real-life samples, thus opening up a new, sensitive, selective, and practical approach for point-of-need SERS diagnosis of small molecules in complex practical environments. Pyrene ; Environmental chemistry ; Chemistry ; Polycyclic aromatic hydrocarbon ; Detection limit ; Environmental science ; Chromatography ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: David O. Obada | Emmanuel Okafor | Simeon A. Abolade | Aniekan Magnus Ukpong | David Dodoo‐Arhin | Akinlolu Akande
Source: Materials Science in Semiconductor Processing (article, 2023-03-24)
Field: Materials Science — Materials Chemistry

Explainable machine learning for predicting the band gaps of ABX3 perovskites Electronegativity ; Machine learning ; Artificial intelligence ; Band gap ; Perovskite (structure) ; Ranking (information retrieval) ; Materials science ; Feature (linguistics) ; Computer science ; Ionization energy ; Ionic radius ; Density functional theory ; Artificial neural network ; Algorithm ; Ionization ; Physics ; Ion ; Computational chemistry ; Chemistry ; Quantum mechanics ; Optoelectronics ; Crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Ming Ma | Huchen Zhou | Suhan Gao | Nan Li | Wenjuan Guo | Zhao Dai
Source: Polymers (article, 2023-06-25)
Field: Materials Science — Biomaterials

Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network Electrospinning technology enables the fabrication of electrospun nanofibers with exceptional properties, which are highly influenced by their diameter. This work focuses on the electrospinning of polyacrylonitrile (PAN) to obtain PAN nanofibers under different processing conditions. The morphology and size of the resulting PAN nanofibers were characterized using scanning electron microscopy (SEM), and the corresponding diameter data were measured using Nano Measure 1.2 software. The processing conditions and corresponding nanofiber diameter data were then inputted into an artificial neural network (ANN) to establish the relationship between the electrospinning process parameters (polymer concentration, applied voltage, collecting distance, and solution flow rate), and the diameter of PAN nanofibers. The results indicate that the polymer concentration has the greatest influence on the diameter of PAN nanofibers. The developed neural network prediction model provides guidance for the preparation of PAN nanofibers with specific dimensions. Electrospinning ; Nanofiber ; Polyacrylonitrile ; Materials science ; Scanning electron microscope ; Artificial neural network ; Polymer ; Composite material ; Nanotechnology ; Computer science ; Artificial intelligence Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Andre Niyongabo Rubungo | Craig B. Arnold | Barry P. Rand | Adji Bousso Dieng
Source: arXiv (Cornell University) (preprint, 2023-10-21)
Field: Materials Science — Materials Chemistry

LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions The prediction of crystal properties plays a crucial role in the crystal design process. Current methods for predicting crystal properties focus on modeling crystal structures using graph neural networks (GNNs). Although GNNs are powerful, accurately modeling the complex interactions between atoms and molecules within a crystal remains a challenge. Surprisingly, predicting crystal properties from crystal text descriptions is understudied, despite the rich information and expressiveness that text data offer. One of the main reasons is the lack of publicly available data for this task. In this paper, we develop and make public a benchmark dataset (called TextEdge) that contains text descriptions of crystal structures with their properties. We then propose LLM-Prop, a method that leverages the general-purpose learning capabilities of large language models (LLMs) to predict the physical and electronic properties of crystals from their text descriptions. LLM-Prop outperforms the current state-of-the-art GNN-based crystal property predictor by about 4% in predicting band gap, 3% in classifying whether the band gap is direct or indirect, and 66% in predicting unit cell volume. LLM-Prop also outperforms a finetuned MatBERT, a domain-specific pre-trained BERT model, despite having 3 times fewer parameters. Our empirical results may highlight the current inability of GNNs to capture information pertaining to space group symmetry and Wyckoff sites for accurate crystal property prediction. Crystal (programming language) ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Machine learning ; Graph ; Property (philosophy) ; Algorithm ; Natural language processing ; Theoretical computer science ; Geography ; Cartography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Samuel Genheden | Per‐Ola Norrby | Ola Engkvist
Source: Journal of Chemical Information and Modeling (article, 2023-03-24)
Field: Materials Science — Materials Chemistry

AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models We introduce the AiZynthTrain Python package for training synthesis models in a robust, reproducible, and extensible way. It contains two pipelines that create a template-based one-step retrosynthesis model and a RingBreaker model that can be straightforwardly integrated in retrosynthesis software. We train such models on the publicly available reaction data set from the U.S. Patent and Trademark Office (USPTO), and these are the first retrosynthesis models created in a completely reproducible end-to-end fashion, starting with the original reaction data source and ending with trained machine-learning models. In particular, we show that employing new heuristics implemented in the pipeline greatly improves the ability of the RingBreaker model for disconnecting ring systems. Furthermore, we demonstrate the robustness of the pipeline by training on a more diverse but proprietary data set. We envisage that this framework will be extended with other synthesis models in the future. Python (programming language) ; Heuristics ; Computer science ; Retrosynthetic analysis ; Extensibility ; Robustness (evolution) ; Pipeline (software) ; Training set ; Pipeline transport ; Code refactoring ; Artificial intelligence ; Software ; Set (abstract data type) ; Data mining ; Machine learning ; Programming language ; Engineering ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Chen Qu | Qi Yu | Paul L. Houston | Riccardo Conte | Apurba Nandi | Joel M. Bowman
Source: Journal of Chemical Theory and Computation (article, 2023-05-30)
Field: Materials Science — Materials Chemistry

Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds Polarizable force fields are pervasive in the fields of computational chemistry and biochemistry; however, their empirical or semiempirical nature gives them both weaknesses and strengths. Here, we have developed a hybrid water potential, named q-AQUA-pol, by combining our recent <i>ab initio</i> q-AQUA potential with the TTM3-F water potential. The new potential demonstrates unprecedented accuracy ranging from gas-phase clusters, e.g., the eight low-lying isomers of the water hexamer, to the condensed phase, e.g., radial distribution functions, the self-diffusion coefficient, triplet OOO distribution, and the temperature dependence of the density. This represents a significant advancement in the field of polarizable machine learning potential and computational modeling. Interfacing ; Polarizability ; Random hexamer ; Force field (fiction) ; Chemical physics ; Field (mathematics) ; Ab initio ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Statistical physics ; Molecule ; Quantum mechanics ; Mathematics ; Crystallography Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: FanDing Li | Xuedong Chen | Peng Xu | Zhichao Fan | Quan Wang | ChengSi Lyu | Qian Zhang | Hao Yu | HengAn Wu
Source: Thin-Walled Structures (article, 2023-04-24)
Field: Materials Science — Metals and Alloys

Optimal design of thin-layered composites for type IV vessels: Finite element analysis enhanced by ANN Finite element method ; Artificial neural network ; Pressure vessel ; Computation ; Structural engineering ; Process (computing) ; Type (biology) ; Dome (geology) ; Materials science ; Function (biology) ; Layer (electronics) ; Work (physics) ; Computer science ; Mechanical engineering ; Algorithm ; Composite material ; Engineering ; Artificial intelligence ; Geology Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis
Authors: Andrea Grisafi | Augustin Bussy | Mathieu Salanne | Rodolphe Vuilleumier
Source: Physical Review Materials (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Predicting the charge density response in metal electrodes The computational study of energy storage and conversion processes calls for simulation techniques that can reproduce the electronic response of metal electrodes under electric fields. Despite recent advancements in machine-learning methods applied to electronic-structure properties, predicting the nonlocal behavior of the charge density in electronic conductors remains a major open challenge. We combine long-range and equivariant kernel methods to predict the Kohn-Sham electron density of metal electrodes in response to various kinds of electric field perturbations. By taking slabs of gold as an example, we first show how the nonlocal electronic polarization generated by the interaction with an ionic species can be accurately reproduced in electrodes of arbitrary thickness. A finite-field extension of the method is then derived, which allows us to predict the charge transfer and the electrostatic potential drop induced by the application of a homogeneous and constant electric field. Finally, we demonstrate the capability of the method to reproduce the charge density response in a gold/electrolyte capacitor under an applied voltage, predicting the system polarization with a greater accuracy than state-of-the-art classical atomic-charge models. Materials science ; Electric field ; Polarization (electrochemistry) ; Electrode ; Electrolyte ; Charge (physics) ; Ionic bonding ; Electrostatic induction ; Voltage ; Charge density ; Condensed matter physics ; Computational physics ; Physics ; Ion ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Xiong Xu | Fangbiao Li | Chang Niu | Min Li | Hui Wang
Source: Applied Physics Letters (article, 2023-01-23)
Field: Materials Science — Materials Chemistry

Machine learning assisted investigation of the barocaloric performance in ammonium iodide Using the ab initio-based training database, we trained the potential function for ammonium iodide (NH4I) based on a deep neural network-based model. On the basis of this potential function, we simulated the temperature-driven β ⇒ α-phase transition of NH4I with isobaric isothermal ensemble via molecular dynamics simulations, the results of which are in good agreement with recent experimental results. As it increases near the phase transition temperature, a quarter of ionic bonds of NH4+-I− break so that NH4+ starts to rotate randomly in a disorderly manner, being able to store thermal energy without a temperature rise. It is found that NH4I possesses a giant isothermal entropy change (∼93 J K−1 kg−1) and adiabatic temperature (∼27 K) at low driving pressure (∼10 MPa). In addition, through partial substitution of I by Br in NH4I, it is found that the thermal conductivity can be remarkably improved, ascribed to the enhancement of lifetime of low frequency phonons contributed by bromine and iodine. The present work provides a method and important guidance for the future exploration and design of barocaloric material for practical applications. Thermodynamics ; Adiabatic process ; Isothermal process ; Iodide ; Phase transition ; Thermal conductivity ; Ionic bonding ; Silver iodide ; Chemistry ; Work (physics) ; Materials science ; Inorganic chemistry ; Ion ; Nanotechnology ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Simon Batzner | Albert Musaelian | Boris Kozinsky
Source: Nature Reviews Physics (review, 2023-07-19)
Field: Materials Science — Materials Chemistry

Advancing molecular simulation with equivariant interatomic potentials Robustness (evolution) ; Computer science ; Statistical physics ; Euclidean geometry ; Euclidean space ; Symmetry (geometry) ; Molecular dynamics ; Equivariant map ; Euclidean distance ; Artificial intelligence ; Theoretical physics ; Theoretical computer science ; Physics ; Mathematics ; Quantum mechanics ; Pure mathematics ; Chemistry ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Tom Bailey | Adam Jackson | Razvan-Antonio Berbece | Ke‐Jun Wu | Nicole Hondow | Elaine Martin
Source: Journal of Chemical Information and Modeling (article, 2023-07-18)
Field: Chemistry — Inorganic Chemistry

Gradient Boosted Machine Learning Model to Predict H<sub>2</sub>, CH<sub>4</sub>, and CO<sub>2</sub> Uptake in Metal–Organic Frameworks Using Experimental Data Predictive screening of metal-organic framework (MOF) materials for their gas uptake properties has been previously limited by using data from a range of simulated sources, meaning the final predictions are dependent on the performance of these original models. In this work, experimental gas uptake data has been used to create a Gradient Boosted Tree model for the prediction of H<sub>2</sub>, CH<sub>4</sub>, and CO<sub>2</sub> uptake over a range of temperatures and pressures in MOF materials. The descriptors used in this database were obtained from the literature, with no computational modeling needed. This model was repeated 10 times, showing an average <i>R</i><sup>2</sup> of 0.86 and a mean absolute error (MAE) of ±2.88 wt % across the runs. This model will provide gas uptake predictions for a range of gases, temperatures, and pressures as a one-stop solution, with the data provided being based on previous experimental observations in the literature, rather than simulations, which may differ from their real-world results. The objective of this work is to create a machine learning model for the inference of gas uptake in MOFs. The basis of model development is experimental as opposed to simulated data to realize its applications by practitioners. The real-world nature of this research materializes in a focus on the application of algorithms as opposed to the detailed assessment of the algorithms. Inference ; Experimental data ; Range (aeronautics) ; Metal-organic framework ; Computer science ; Work (physics) ; Mean absolute error ; Machine learning ; Artificial intelligence ; Data mining ; Chemistry ; Materials science ; Thermodynamics ; Mathematics ; Mean squared error ; Statistics ; Physics ; Adsorption ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science