jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kurudi V Vedavyasa | Ashok Kumar
Source: arXiv (Cornell University) (preprint, 2024-05-29)
Field: Materials Science — Materials Chemistry

Classification analysis of transition-metal chalcogenides and oxides using quantum machine learning Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for computations. QML accelerates materials research with active screening of chemical space, identifying novel materials for practical applications and classifying structurally diverse materials given their measured properties. This study analyzes the performance of three efficient quantum machine learning algorithms viz., variational quantum eigen solver (VQE), quantum support vector machine (QSVM) and quantum neural networks (QNN) for the classification of transition metal chalcogenides and oxides (TMCs &amp;TMOs). The analysis is performed on three datasets of different sizes containing 102, 192 and 350 materials with TMCs and TMOs labelled as +1 and -1 respectively. By employing feature selection, classical machine learning achieves 100% accuracy whereas QML achieves the highest performance of 99% and 98% for test and train data respectively on QSVC. This study establishes the competence of QML models in materials classification and explores the quantum circuits in terms of over-fitting using the circuit descriptors expressibility and entangling capability. In addition, the perspectives on QML in materials research with noisy intermediate scale quantum (NISQ) devices is given. Transition metal ; Quantum ; Quantum chemical ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Quantum mechanics ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Andrij Vasylenko
Source: arXiv (Cornell University) (preprint, 2024-08-05)
Field: Chemistry — Physical and Theoretical Chemistry

Learning Atoms from Crystal Structure Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongly affected by the choice of the numerical representation for describing the composition, structure and chemical elements. Structure controls the properties, but often only the composition of a candidate material is available. Existing elemental descriptors lack direct access to structural insights such as the coordination geometry of an element. In this study, we introduce Local Environment-induced Atomic Features, LEAFs, which incorporate information about the statistically preferred local coordination geometry for atoms in crystal structure into descriptors for chemical elements, enabling the modelling of materials solely as compositions without requiring knowledge of their crystal structure. In the crystal structure, each atomic site can be described by similarity to common local structural motifs; by aggregating these features of similarity from the experimentally verified crystal structures of inorganic materials, LEAFs formulate a set of descriptors for chemical elements and compositions. The direct connection of LEAFs to the local coordination geometry enables the analysis of ML model property predictions, linking compositions to the underlying structure-property relationships. We demonstrate the versatility of LEAFs in structure-informed property predictions for compositions, mapping of chemical space in structural terms, and prioritising elemental substitutions. Based on the latter for predicting crystal structures of binary ionic compounds, LEAFs achieve the state-of-the-art accuracy of 86 per cent. These results suggest that the structurally informed description of chemical elements and compositions developed in this work can effectively guide synthetic efforts in discovering new materials. Crystal structure ; Crystal (programming language) ; Materials science ; Crystallography ; Chemistry ; Computer science ; Programming language History and advancements in chemistry ; X-ray Diffraction in Crystallography
Authors: Anh‐Luan Phan | Daniele Soccodato | Alessia Di Vito | Alessandro Pecchia | Matthias Auf der Maur
Source:  (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Empirical Tight-Binding Parameterizations for Accurate Heterostructure and Alloy Calculations Empirical tight-binding is a valuable and reliable tool for the calculation of electronic and optical properties in semiconductor heterostructures and alloys. It has been applied in many contexts, ranging from inorganic semiconductors like arsenides, antimonides, nitrides and their alloys to 2D materials and hybrid perovskites. Here we present recent activities in development and application of empirical tight-binding, in particular regarding disordered alloys. We show some limitations of the most used schemes, and how they can be overcome by more recent parameterizations, and we introduce an alternative machine-learning based parameterization scheme. Tight binding ; Heterojunction ; Alloy ; Materials science ; Computer science ; Statistical physics ; Optoelectronics ; Condensed matter physics ; Physics ; Electronic structure ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Saaketh Desai | Rémi Dingreville | Prasad P. Iyer
Source:  (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning discovers parsimonious equations governing incoherent emission steering from semiconductor metasurfaces We develop neural network based equation learners to discover governing equations relating metasurfaceincoherent emission steering to critical features defining its spatial refractive index profiles. Semiconductor ; Computer science ; Artificial intelligence ; Physics ; Optoelectronics Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Terahertz technology and applications
Authors: Victor Li | Yu Sun
Source:  (article, 2024-10-11)
Field: Chemistry — Physical and Theoretical Chemistry

A computational Approach to Assess the Effect of Training Set on Generated Molecules Using Deep Learning-driven Scaffold Hopping Discovery and development of a new drug is a risky, time-consuming and costly process. To speed up the process and increase the success rate, scaffold hopping, an important technique for small-molecule drug design, has received extensive attention. In recent years, some skeleton transition deep learning models have been developed. However, most of these scaffold hopping models use the same training set and do not consider the impact of the training set on the model-generated molecules. As a result, this project aims to develop a novel computational approach to assess the effects of training set using deep learningdriven scaffold hopping. We apply our approach to a dataset on four key drug-like properties, commonly shared by approved market drugs: hydrogen bond donor, hydrogen bond acceptor, lipophilicity, and molecular weight. In our results, we show the significant impact of training sets on generated molecules by intentionally selecting the training set. Scaffold ; Computer science ; Set (abstract data type) ; Artificial intelligence Various Chemistry Research Topics
Authors: Vanika Singhal | Deepa Anand | C Florintina | Harshit Dubey | Radhika Madhavan | Chitresh Bhushan | Dattesh Shanbhag
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Metal artifact synthesis: Enabling inclusive Deep learning for patients with implants Motivation: AI medical imaging solutions are impacted by the presence metal implants and a design of appropriate synthesis method can improve robustness of DL models. Goal(s): Simulation of patient medical condition like metal artifacts in MRI medical images. Approach: The proposed method blends regions from template images containing metal artifacts into target images by using metal segmentation mask for selection, blending this region into a chosen target image RoI . Results: Improvement in knee classification accuracy of 8% and decrease in spine plane distance error by 25-40% and plane angle error by 4-30% using the proposed approach. Impact: A data adaptive metal simulation method in semantically relevant regions in anatomy ensures robust of DL models in patients with metal implants who hitherto would not have benefitted from AI driven tasks . Artifact (error) ; Computer science ; Deep learning ; Metal ; Artificial intelligence ; Materials science ; Metallurgy Machine Learning in Materials Science
Authors: YICHEN ZOU
Source: Applied and Computational Engineering (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Review of High Dimensional Neural Network Potentials Over the past few years, machine learning potentials (MLPs) have been fully developed and can now be applied to a variety of large-scale atomic simulations. MLPs encompass a number of different machine learning algorithms, of which neural network potentials are the most dominant and representative, and have achieved great success. This review focuses on the second generation of neural network potentials, high-dimensional neural network potentials (HDNNPs). While HDNNP has accomplished noteworthy results in the implementation of ion diffusion, nuclear magnetic resonance parameters, defect formation, and thermal conductivity, it still encounters hurdles in the compilation of training datasets, transferability, accuracy constraints, and the management of multiple chemical species. In spite of this, HDNNP remains a highly promising methodology. Looking forward, we anticipate the development of more specialized software for HDNNP to augment research efficiency and lower the barrier to usage. Artificial neural network ; Transferability ; Computer science ; Artificial intelligence ; Machine learning ; Variety (cybernetics) ; Software ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Shuting Xiang
Source:  (preprint, 2024-09-23)
Field: Materials Science — Materials Chemistry

Utilizing machine learning - assisted XANES analysis to resolve the configuration of “single-atom” catalysts XANES ; Catalysis ; Atom (system on chip) ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Physics ; Embedded system ; Spectroscopy ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Aiwei Zhao | Dines Chandra Santra | Kenji Nagata | Junya Sakurai | Masahiko Demura | Masayoshi Higuchi
Source: Proceedings of the International Display Workshops (article, 2024-12-05)
Field: Materials Science — Polymers and Plastics

Synthesis of Electrochromic Supramolecular Polymers Driven by Data Science Materials informatics has recently garnered significant attention as a potent tool for the development of a wide array of functional materials. The successful integration of materials informatics into polymer design holds the promise of streamlining the synthesis of polymers with tailored properties, thereby enhancing efficiency and specificity in material engineering. Machine learning is a subset of artificial intelligence (AI), which involves algorithms learning from data to make predictions or decisions. The machine learning, especially Bayesian optimization method is widely used in materials science. In this paper, we report our recent approach on the search of electrochromic (EC) metallo-supramolecular polymers (MSPs) with the help of materials informatics. Four components were selected among many components of MSPs. Among all the combination of the variations, the selected number of the corresponding MSPs according to an orthogonal table were synthesized. A coloration efficiency (CE) of 1281 cm2/C was obtained compared to our previous work. We found that this method with statistics was useful to find the polymers with better EC properties quickly. Electrochromism ; Supramolecular chemistry ; Polymer ; Supramolecular polymers ; Materials science ; Nanotechnology ; Polymer science ; Chemistry ; Organic chemistry ; Electrode ; Molecule ; Physical chemistry ; Composite material Transition Metal Oxide Nanomaterials
Authors: Patrizia Mazzeo | Edoardo Cignoni | Amanda Arcidiacono | Lorenzo Cupellini | Benedetta Mennucci
Source: ChemRxiv (preprint, 2024-10-03)
Field: Materials Science — Materials Chemistry

Electrostatic Embedding Machine Learning for Ground and Excited State Molecular Dynamics of Solvated Molecules Excited state ; Embedding ; Ground state ; Molecular dynamics ; Molecule ; Dynamics (music) ; State (computer science) ; Chemical physics ; Electrostatics ; Computer science ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Atomic physics ; Artificial intelligence ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Shuting Xiang
Source:  (preprint, 2024-09-23)
Field: Materials Science — Materials Chemistry

Utilizing machine learning - assisted XANES analysis to resolve the configuration of “single-atom” catalysts XANES ; Catalysis ; Atom (system on chip) ; Computer science ; Chemistry ; Physics ; Embedded system ; Spectroscopy ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jiale Han | Chunhua Ying | Yue Cao | Wen Li | Yuan Feng | Masood Mortazavi | Pingfan Wu | Liang Peng | Jiechen Wang
Source: Next Materials (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Harnessing machine learning for high-throughput screening of high thermal conductivity polyimides: A multiscale feature engineering approach Polyimides are known for their exceptional thermal stability and are widely utilized in high-temperature and electronic applications. To further explore and broaden their application in electronic packaging and thermal management, developing polyimide with high thermal conductivity remains a significant challenge. In this study, we present a machine learning technique with novel multiscale feature engineering approach to predict and identify high thermal conductivity polyimide efficiently. Our workflow involves digitally representing chemical structures using physical insights and refining these representations through the Statistical – Tree – Recursive feature engineering pipeline, which includes three steps--statistical selection, tree-based cascading feature selection, and recursive feature elimination. This process results in the creation of a comprehensive combinational feature set. Multiple machine learning models were trained and validated, demonstrating high predictive accuracy and generalizability. High-throughput screening identified polyimide candidates with thermal conductivity values exceeding 0.4 W/(m⋅K), and these predictions were validated using non-equilibrium molecular dynamics simulation. This workflow provides valuable insights into structure-property relationships, offering a robust framework for designing polymer materials with improved thermal properties for applications in electronics packaging, flexible sensors, and other high-performance devices. • Machine learning identifies polymeric materials with high thermal conductivity. • Multiscale feature engineering improves learning model accuracy for material screening. • Integrating physical properties refines machine learning predictions in polymeric materials. • Predicted thermal conductivity is validated with non-equilibrium molecular dynamic simulation. Throughput ; High-throughput screening ; Thermal conductivity ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Materials science ; Conductivity ; Feature engineering ; Machine learning ; Process engineering ; Pattern recognition (psychology) ; Deep learning ; Composite material ; Engineering ; Physics ; Bioinformatics ; Telecommunications ; Biology Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Hao Tang | Brian Xiao | Wenhao He | Pero Subasic | Avetik R. Harutyunyan | Wang Yao | Fang Liu | Haowei Xu | Ju Li
Source: arXiv (Cornell University) (preprint, 2024-05-09)
Field: Materials Science — Materials Chemistry

Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work, we developed a unified ML method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties. As case studies, we apply the model to aromatic compounds and semiconducting polymers on both ground state and excited state properties, demonstrating its accuracy and generalization capability to complex systems that are hard to calculate using CCSD(T)-level methods. Cluster (spacecraft) ; Task (project management) ; Computer science ; Electronic structure ; Materials science ; Chemistry ; Computational chemistry ; Systems engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Adil Khokhar | David M. Broussard | Christoph W. Borst
Source:  (article, 2024-09-13)
Field: Chemistry — Physical and Theoretical Chemistry

Hands-on VR Approach to Teach 3D Spatial Concepts of Molecular Reactions Molecules require a certain relative orientation and proximity to react with each other, a concept that is often difficult for beginners to grasp. We present a virtual reality system designed to help users learn about chemistry reactions with hands-on manipulation and interaction with molecules in a simulated lab environment. Our system emphasizes intuitive molecular interactions over a full molecular dynamics simulation, minimizing erratic molecule motion and sensitivity, in order to help users better understand spatial requirements underlying chemistry reactions. The system uses a novel approach to mapping reagent and product molecules to facilitate reaction detection and animation. We propose to evaluate our system by training a machine learning classifier on motion features (head, hand, and eye) to estimate user understanding of chemistry reactions, thereby enabling adaptive system feedback, such as providing visual hints. Computer science ; Virtual reality ; Human–computer interaction ; Computer graphics (images) ; Multimedia Various Chemistry Research Topics
Authors: Vansh Ramani | Tarak Karmakar
Source: arXiv (Cornell University) (preprint, 2024-02-17)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for Predicting Solubility in Diverse Solvents using MolMerger incorporating Solute-solvent Interactions Prediction of solubility has been a complex and challenging physiochemical problem that has tremendous implications in the chemical and pharmaceutical industry. Recent advancements in machine learning methods have provided great scope for predicting the reliable solubility of a large number of molecular systems. However, most of these methods rely on using physical properties obtained from experiments and or expensive quantum chemical calculations. Here, we developed a method that utilizes a graphical representation of solute-solvent interactions using `MolMerger', which captures the strongest polar interactions between molecules using Gasteiger charges and creates a graph incorporating the true nature of the system. Using these graphs as input, a neural network learns the correlation between the structural properties of a molecule in the form of node embedding and its physiochemical properties as output. This approach has been used to calculate molecular solubility by predicting the Log solubility values of various organic molecules and pharmaceuticals in diverse sets of solvents. Solubility ; Solvent ; Artificial neural network ; Graph ; Chemistry ; Organic chemistry ; Biological system ; Computer science ; Artificial intelligence ; Theoretical computer science ; Biology Crystallization and Solubility Studies ; Various Chemistry Research Topics ; Chemistry and Chemical Engineering
Authors: Xiao-Kuan Wu | Xinying Teng | Hong Zhao | Zhigang Lei | Jie Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A First-Principles Study into the Development of a Bifunctional, Single-Atom Catalyst for Nickel Loading on Melon Carbon Nitride Bifunctional ; Nickel ; Catalysis ; Melon ; Nitride ; Atom (system on chip) ; Carbon fibers ; Carbon atom ; Materials science ; Carbon nitride ; Chemical engineering ; Metallurgy ; Nanotechnology ; Chemistry ; Computer science ; Composite material ; Engineering ; Organic chemistry ; Horticulture ; Composite number ; Biology ; Embedded system ; Ring (chemistry) Catalytic Processes in Materials Science
Authors: Y T Li | Xiao-yan Wang | Sheng-Jie GAO | Yu-Ting DI | Bao-ping Wu | Jian-tao Wu | Jun-tao Li
Source:  (article, 2024-07-19)
Field: Materials Science — Materials Chemistry

Optimization of Process Parameters for High Temperature Alloy Investment Casting Based on Machine Learning Investment casting ; Process (computing) ; Casting ; Investment (military) ; Alloy Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Metallurgy and Material Forming