jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Fang | Junren Li | Ming Zhao | Tan Li | Jian–Guang Lou
Source: Nature Communications (article, 2023-04-28)
Field: Materials Science — Materials Chemistry

Single-step retrosynthesis prediction by leveraging commonly preserved substructures Retrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models. Chemists cannot readily derive useful insights from traditional approaches that rely largely on atom-level decoding in the string representations, because human experts tend to interpret reactions by analyzing substructures that comprise a molecule. It is well-established that some substructures are stable and remain unchanged in reactions. In this paper, we developed a substructure-level decoding model, where commonly preserved portions of product molecules were automatically extracted with a fully data-driven approach. Our model achieves improvement over previously reported models, and we demonstrate that its performance can be boosted further by enhancing the accuracy of these substructures. Analyzing substructures extracted from our machine learning model can provide human experts with additional insights to assist decision-making in retrosynthesis analysis. Retrosynthetic analysis ; Computer science ; Task (project management) ; String (physics) ; Machine translation ; Artificial intelligence ; Substructure ; Decoding methods ; Natural language processing ; Translation (biology) ; Machine learning ; Chemistry ; Algorithm ; Engineering ; Systems engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Chengcheng Liu | Xuandong Wang | Weidong Cai | Jiahui Yang | Hang Su
Source: Materials (article, 2023-08-15)
Field: Materials Science — Metals and Alloys

Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R<sup>2</sup> = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R<sup>2</sup> = 0.99, MAE = 2.65 MPa; elongation: R<sup>2</sup> = 0.84, MAE = 1.42%; reduction in area: R<sup>2</sup> = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions. Austenitic stainless steel ; Hyperparameter ; Machine learning ; Algorithm ; Kriging ; Austenite ; Genetic algorithm ; Materials science ; Interpretability ; Hyperparameter optimization ; Artificial intelligence ; Ultimate tensile strength ; Bayesian optimization ; Computer science ; Metallurgy ; Support vector machine ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Tommy Liu | Amanda S. Barnard
Source: Cell Reports Physical Science (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

The emergent role of explainable artificial intelligence in the materials sciences The combination of rational machine learning with creative materials science makes materials informatics a powerful way of discovering, designing, and screening new materials. However, moving from a promising prediction to a practical strategy often requires more than just an instructive structure-property relationship; understanding how a machine learning method uses the structural feature to predict the target properties becomes critical. Explainable artificial intelligence (XAI) is an emerging field in computer science based in statistics that can augment materials informatics workflows. XAI can be used as a forensic analysis to understand the consequences of data, model, and application decisions or as a model refinement method capable of distinguishing important features from nuisance variables. Here, we outline the state of the art in XAI and highlight methods most useful to the physical sciences. This practical guide focuses on characteristics of XAI methods that are relevant to materials informatics and will become increasingly important as more researchers move toward using deeper neural networks and large language models. Computer science ; Field (mathematics) ; Informatics ; Data science ; Workflow ; Artificial intelligence ; Property (philosophy) ; Materials informatics ; Management science ; Machine learning ; Engineering informatics ; Health informatics ; Engineering ; Mathematics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Software Engineering Research
Authors: V. V. Kulyk | Ivan Izonin | V. І. Vavrukh | Roman Tkachenko | Zoia Duriagina | B. D. Vasyliv | Monika Kováčová
Source: Acta Metallurgica Slovaca (article, 2023-06-20)
Field: Materials Science — Ceramics and Composites

PREDICTION OF HARDNESS, FLEXURAL STRENGTH, AND FRACTURE TOUGHNESS OF ZRO2 BASED CERAMICS USING ENSEMBLE LEARNING ALGORITHMS Flexural strength, hardness, and fracture toughness are the basic mechanical properties of ceramic materials. Manufacturers widely use this set of properties to ensure the durability of ceramic products. However, many factors, such as chemical and phase compositions, sintering temperature, average grain size, density, and others, affect these properties, making it challenging to estimate corresponding reliability parameters correctly. Experimental examination of the impact of these factors on the mechanical properties of ceramics is a rather time-consuming and resource-consuming procedure. This work aims to predict the mechanical properties of zirconia ceramics using machine learning tools. The authors have created an experimental database for predicting the hardness, flexural strength, and fracture toughness of ZrO2-based ceramics based on chemical composition, phase composition, microstructural features, and sintering temperature on the mechanical properties of zirconia ceramics. The authors compare compared the effectiveness of using different machine learning algorithms and have found a high accuracy of the predicted values of each of the three mechanical properties using boosting ensemble methods. Also they developed a stacked ensemble of machine learning methods to improve the accuracy of determining the hardness property prediction task. We obtained the increase in accuracy of more than 10% (R2) using our approach. Materials science ; Flexural strength ; Ceramic ; Fracture toughness ; Sintering ; Composite material ; Durability ; Algorithm ; Toughness ; Cubic zirconia ; Machine learning ; Computer science Advanced ceramic materials synthesis ; Machine Learning in Materials Science
Authors: Riccardo Alessandri | Juan Pablo
Source: Macromolecules (article, 2023-05-11)
Field: Materials Science — Materials Chemistry

Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions The properties of soft electronic materials depend on the coupling of electronic and conformational degrees of freedom over a wide range of spatiotemporal scales. The description of such properties requires multiscale approaches capable of, at the same time, accessing electronic properties and sampling the conformational space of soft materials. This could in principle be realized by connecting the coarse-grained (CG) methodologies required for adequate conformational sampling to conformationally averaged electronic property distributions via backmapping to atomistic-resolution level models and repeated quantum-chemical calculations. Computational demands of such approaches, however, have hindered their application in high-throughput computer-aided soft materials discovery. Here, we present a method that, combining machine learning and CG techniques, can replace traditional backmapping-based approaches without sacrificing accuracy. We illustrate the method for an emerging class of soft electronic materials, namely, nonconjugated, radical-containing polymers, promising materials for all-organic energy storage. Supervised machine learning models are trained to learn the dependence of electronic properties on polymer conformation at CG resolutions. We then parametrize CG models that retain electronic structure information, simulate CG condensed phases, and predict the electronic properties of such phases solely from the CG degrees of freedom. We validate our method by comparing it against a full backmapping-based approach and find good agreement between both methods. This work demonstrates the potential of the proposed method to accelerate multiscale workflows and provides a framework for the development of CG models that retain electronic structure information. Degrees of freedom (physics and chemistry) ; Electronic structure ; Computer science ; Statistical physics ; Workflow ; Biological system ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Block Copolymer Self-Assembly
Authors: Yejun Gu | Christopher D. Stiles | Jaafar A. El‐Awady
Source: Acta Materialia (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

A statistical perspective for predicting the strength of metals: Revisiting the Hall–Petch relationship using machine learning Materials science ; Microstructure ; Crystallite ; Randomness ; Grain size ; Probabilistic logic ; Flow stress ; Stress (linguistics) ; Context (archaeology) ; Machine learning ; Artificial intelligence ; Metallurgy ; Computer science ; Statistics ; Mathematics Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Susmi Anna Thomas | Jayesh Cherusseri | Mohammad Khalid
Source: Energy Storage (review, 2023-06-18)
Field: Materials Science — Polymers and Plastics

A comprehensive review on <scp>tungsten oxide</scp> nanostructures‐based electrochromic supercapacitors and machine learning models for design and process parameter optimization Abstract Electrochromism has received significant research recently due to its unique electrochromic feature for various applications such as smart buildings, smart doors, e‐skins, display devices, and so forth. Transition metal oxides are the major choice as electrode materials for electrochromic devices. Among the various electrochromic oxides available so far, tungsten oxide (WO 3 ) achieves great interest due to its peculiar properties such as high coloration efficiency, low‐cost, high stability, and so forth. WO 3 is an example of cathodic electrochromic material in which the material gets color under ion insertion. In this review article, we discuss the recent developments in WO 3 ‐based electrochromic supercapacitors. Initially, the synthesis and characterizations of WO 3 are discussed, and later the electrochromic performance evaluation using various electrochemical characterization tools, such as cyclic voltammetry, galvanostatic charge/discharge cycling, and so on, was reviewed. Furthermore, various theoretical models that describe the performance of electrochromic supercapacitors are explained in detail. Finally, the future perspectives of WO 3 ‐based electrochromic supercapacitors are discussed. The most recent advances in machine learning (ML) are being utilized to investigate the mechanism and enhance the strategic performance of WO 3 in the field of electrochromic. There is ongoing research in developing ML models for predicting the optimal design of electrochromic supercapacitors in terms of both material selection and performance parameters. Such models are essential in the pursuit of next‐generation smart windows, which are integral to the construction of sustainable buildings and the creation of a more environment‐friendly planet. Electrochromism ; Supercapacitor ; Electrochromic devices ; Materials science ; Nanotechnology ; Computer science ; Electrochemistry ; Electrode ; Chemistry Transition Metal Oxide Nanomaterials ; Conducting polymers and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Mashroor S. Nitol | Khanh Dang | Saryu Fensin | M. I. Baskes | Doyl Dickel | Christopher D. Barrett
Source: Physical Review Materials (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Hybrid interatomic potential for Sn To design materials for extreme applications, it is important to understand and predict phase transitions and their influence on material properties under high pressures and temperatures. Atomistic modeling can be a useful tool to assess these behaviors. However, this can be difficult due to the lack of fidelity of the interatomic potentials in reproducing this high pressure and temperature extreme behavior. Here, a hybrid EAM-R---which is the combination of embedded atom method (EAM) and rapid artificial neural network potential---for Tin (Sn) is described which is capable of accurately modeling the complex sequence of phase transitions between different metallic polymorphs as a function of pressure. This hybrid approach ensures that a basic empirical potential like EAM is used as a lower energy bound. By using the final activation function, the neural network contribution to energy must be positive, assuring stability over the whole configuration space. This implementation has the capacity to reproduce density functional theory results at 6 orders of magnitude slower than a pair potential for molecular dynamics simulation, including elastic and plastic characteristics and relative energies of each phase. Using calculations of the Gibbs free energy, it is demonstrated that the potential precisely predicts the experimentally observed phase changes at temperatures and pressures across the whole phase diagram. At 10.2 GPa, the present potential predicts a first-order phase transition between body-centered tetragonal (BCT) $\ensuremath{\beta}$-Sn and another polymorph of BCT-Sn. This structure transforms into body-centered cubic near the experimentally reported value at 33 GPa. Thus, the Sn potential developed in this paper can be used to study complex deformation mechanisms under extreme conditions of high pressure and strain rates unlike existing potentials. Moreover, the framework developed in this paper can be extended for different material systems with complex phase diagrams. Materials science ; Interatomic potential ; Embedded atom model ; Tetragonal crystal system ; Molecular dynamics ; Phase diagram ; Phase (matter) ; Phase transition ; Atom (system on chip) ; Potential energy ; Thermodynamics ; Chemical physics ; Atomic physics ; Computational chemistry ; Physics ; Computer science ; Chemistry Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Dhruv Menon | Swaroop Chakraborty
Source: Frontiers in Toxicology (review, 2023-06-23)
Field: Chemistry — Inorganic Chemistry

How safe are nanoscale metal-organic frameworks? Owing to the size scales that can be accessed, the nanoscale has opened doors to new physical and chemical properties, not seen in the bulk. These properties are leveraged by nanomaterials (NMs) across a plethora of applications. More recently, nanoscale metal-organic frameworks (nMOFs) have witnessed explosive growth due to the modularity of their chemical constituents, the ability to modify their composition and structure, and exceptional properties such as permanent porosity and high surface areas. These properties have prompted the investigation of these materials for applications in biological and environmental contexts. However, one aspect that is often ignored in these discussions is their safety at a nanoscale. In this mini review, we aim to initiate a discussion on the safety and toxicity of nMOFs, drawing parallels with the existing guidelines and literature on the safety of inorganic NMs. We first describe why nMOFs are of considerable interest to the scientific community followed by a discussion on routes through which they can be exposed to the environment and living organisms, particularly shedding light on their transformation mechanisms. The review also discusses the factors affecting toxicity of nMOFs, such as their size, shape, morphology, and composition. We briefly highlight potential mechanisms of toxicity and conclude with describing the need to transition towards data-intensive computational approaches such as machine learning to establish nMOFs as credible materials for their envisioned applications. Nanotechnology ; Computer science ; Parallels ; Data science ; Materials science ; Engineering ; Mechanical engineering Metal-Organic Frameworks: Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis
Authors: Dariush Ebrahimibagha | Sergio Arroyo Armida | Shubhabrata Datta | Mallar Ray
Source: Computational Materials Science (article, 2023-11-11)
Field: Materials Science — Materials Chemistry

Machine learning based models to investigate the thermoelectric performance of carbon nanotube-polyaniline nanocomposites Thermoelectric effect ; Polyaniline ; Carbon nanotube ; Materials science ; Nanocomposite ; Thermoelectric materials ; Seebeck coefficient ; Thermal conductivity ; Nanotechnology ; Composite material ; Thermodynamics ; Polymer ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Thermal properties of materials
Authors: Guangshuai Han | Yixuan Sun | Yining Feng | Guang Lin | Na Lü
Source: Advanced Electronic Materials (article, 2023-05-25)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Guided Thermoelectric Materials Design and Discovery Abstract Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data‐driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials’ uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p‐type TE materials and 8 promising n‐type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials. Bottleneck ; Thermoelectric materials ; Computer science ; Property (philosophy) ; Materials informatics ; Representation (politics) ; Materials science ; Process (computing) ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Marcel F. Langer | Florian Knoop | Christian Carbogno | Matthias Scheffler | Matthias Rupp
Source: Physical review. B./Physical review. B (article, 2023-09-13)
Field: Materials Science — Materials Chemistry

Heat flux for semilocal machine-learning potentials The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. Machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales at a fraction of the cost. In this Letter, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semilocal interactions beyond the initial interaction cutoff. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency. The approach is demonstrated and validated by calculating the thermal conductivity of zirconium dioxide across temperatures. Computer science ; Thermal conductivity ; Heat flux ; Flux (metallurgy) ; Class (philosophy) ; Cutoff ; Energy (signal processing) ; Fraction (chemistry) ; Thermal ; Statistical physics ; Machine learning ; Artificial intelligence ; Physics ; Algorithm ; Heat transfer ; Mechanics ; Thermodynamics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Materials and Properties