jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kalaipriya Ramajayam | Selvaganapathy Ganesan | Purnimajayasree Ramesh | Maya Beena | Thangavelu Kokulnathan | Arunkumar Palaniappan
Source: Biomimetics (review, 2023-06-08)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymer-Based Biomimetic Systems for Sensing Environmental Contaminants, Biomarkers, and Bioimaging Applications Molecularly imprinted polymers (MIPs), a biomimetic artificial receptor system inspired by the human body's antibody-antigen reactions, have gained significant attraction in the area of sensor development applications, especially in the areas of medical, pharmaceutical, food quality control, and the environment. MIPs are found to enhance the sensitivity and specificity of typical optical and electrochemical sensors severalfold with their precise binding to the analytes of choice. In this review, different polymerization chemistries, strategies used in the synthesis of MIPs, and various factors influencing the imprinting parameters to achieve high-performing MIPs are explained in depth. This review also highlights the recent developments in the field, such as MIP-based nanocomposites through nanoscale imprinting, MIP-based thin layers through surface imprinting, and other latest advancements in the sensor field. Furthermore, the role of MIPs in enhancing the sensitivity and specificity of sensors, especially optical and electrochemical sensors, is elaborated. In the later part of the review, applications of MIP-based optical and electrochemical sensors for the detection of biomarkers, enzymes, bacteria, viruses, and various emerging micropollutants like pharmaceutical drugs, pesticides, and heavy metal ions are discussed in detail. Finally, MIP's role in bioimaging applications is elucidated with a critical assessment of the future research directions for MIP-based biomimetic systems. Molecularly imprinted polymer ; Nanotechnology ; Molecular imprinting ; Materials science ; Biosensor ; Analyte ; Chemistry ; Chromatography ; Selectivity ; Organic chemistry ; Catalysis Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Biosensors and Analytical Detection
Authors: Xiangdong Wang | Ye Sheng | Jinyan Ning | Jinyang Xi | Lili Xi | Di Qiu | Jiong Yang | Xuezhi Ke
Source: The Journal of Physical Chemistry Letters (review, 2023-02-10)
Field: Materials Science — Materials Chemistry

A Critical Review of Machine Learning Techniques on Thermoelectric Materials Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed. Thermoelectric materials ; Limiting ; Thermoelectric effect ; Refrigeration ; Electricity ; Computer science ; Materials science ; Process engineering ; Engineering physics ; Mechanical engineering ; Nanotechnology ; Biochemical engineering ; Thermal conductivity ; Engineering ; Electrical engineering ; Thermodynamics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Weiluo Ren | Weizhong Fu | Xiaojie Wu | Ji Chen
Source: Nature Communications (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Towards the ground state of molecules via diffusion Monte Carlo on neural networks Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed significant developments in the past decades and become one of the go-to methods when accurate ground state energy of molecules and materials is needed. However, the inaccurate nodal structure hinders the application of DMC for more challenging electronic correlation problems. In this work, we apply the neural-network based trial wavefunction in fixed-node DMC, which allows accurate calculations of a broad range of atomic and molecular systems of different electronic characteristics. Our method is superior in both accuracy and efficiency compared to state-of-the-art neural network methods using variational Monte Carlo (VMC). We also introduce an extrapolation scheme based on the empirical linearity between VMC and DMC energies, and significantly improve our binding energy calculation. Overall, this computational framework provides a benchmark for accurate solutions of correlated electronic wavefunction and also sheds light on the chemical understanding of molecules. Diffusion Monte Carlo ; Bottleneck ; Wave function ; Monte Carlo method ; Benchmark (surveying) ; Statistical physics ; Node (physics) ; Computer science ; Artificial neural network ; Electronic structure ; Ground state ; Variational Monte Carlo ; Quantum Monte Carlo ; Monte Carlo molecular modeling ; Physics ; Computational chemistry ; Chemistry ; Machine learning ; Mathematics ; Quantum mechanics ; Markov chain Monte Carlo Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Zijing Zhang | Shuwen Li | João C. A. Oliveira | Yanjun Li | Xinran Chen | Shuo‐Qing Zhang | Li‐Cheng Xu | Torben Rogge | Xin Hong | Lutz Ackermann
Source: Nature Communications (article, 2023-05-31)
Field: Chemistry — Organic Chemistry

Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics. Chirality (physics) ; Moiety ; Carboxylic acid ; Catalysis ; Combinatorial chemistry ; Alkylation ; Chemistry ; Stereochemistry ; Computer science ; Organic chemistry ; Physics ; Chiral anomaly ; Particle physics Catalytic C–H Functionalization Methods ; Axial and Atropisomeric Chirality Synthesis ; Asymmetric Hydrogenation and Catalysis
Authors: Maithreyi Ramakrishnan | Alexander van Teijlingen | Tell Tuttle | Rein V. Ulijn
Source: Angewandte Chemie International Edition (review, 2023-02-02)
Field: Materials Science — Biomaterials

Integrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems Interest in peptide-based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self-assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field. Supramolecular chemistry ; Computation ; Computer science ; Peptide ; Artificial intelligence ; Chemistry ; Programming language ; Biochemistry ; Crystallography Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Polydiacetylene-based materials and applications
Authors: Zipeng Zhong | Jie Song | Zunlei Feng | Tiantao Liu | Lingxiang Jia | Shaolun Yao | Tingjun Hou | Mingli Song
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Recent advances in deep learning for retrosynthesis Abstract Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand‐new molecules. Conventional rule‐based or expert‐based computer‐aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, dramatic breakthroughs driven by deep learning have revolutionized retrosynthesis. Here we aim to present a comprehensive review of recent advances in AI‐based retrosynthesis. For single‐step and multi‐step retrosynthesis both, we first introduce their goal and provide a thorough taxonomy of existing methods. Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets. In the next part, we introduce popular databases and established platforms for retrosynthesis. Finally, this review concludes with a discussion about promising research directions in this field. This article is categorized under: Data Science &gt; Artificial Intelligence/Machine Learning Data Science &gt; Computer Algorithms and Programming Data Science &gt; Chemoinformatics Retrosynthetic analysis ; Cheminformatics ; Computer science ; Artificial intelligence ; Field (mathematics) ; Data science ; Machine learning ; Engineering Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Jerardo E. Salgado | Samuel Lerman | Zhaotong Du | Chenliang Xu | Niaz Abdolrahim
Source: npj Computational Materials (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Automated classification of big X-ray diffraction data using deep learning models Abstract In current in situ X-ray diffraction (XRD) techniques, data generation surpasses human analytical capabilities, potentially leading to the loss of insights. Automated techniques require human intervention, and lack the performance and adaptability required for material exploration. Given the critical need for high-throughput automated XRD pattern analysis, we present a generalized deep learning model to classify a diverse set of materials’ crystal systems and space groups. In our approach, we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties. We also employ an expedited learning technique to refine our model’s expertise to experimental conditions. In addition, we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making. We evaluate our models using experimental data, materials unseen in training, and altered cubic crystals, where we observe state-of-the-art performance and even greater advances in space group classification. Computer science ; Artificial intelligence ; Deep learning ; Adaptability ; Representation (politics) ; Throughput ; Machine learning ; Experimental data ; Big data ; Synthetic data ; Set (abstract data type) ; Data set ; Diffraction ; Data mining ; Pattern recognition (psychology) ; Mathematics ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Xiaoyang Wang | Zhenyu Wang | Pengyue Gao | Chengqian Zhang | Jian Lv | Handong Wang | Haifeng Liu | Yanchao Wang | Yanming Ma
Source: Nature Communications (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Data-driven prediction of complex crystal structures of dense lithium Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine-learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal structures containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine learning potentials. Crystal structure prediction ; Crystal structure ; Phase diagram ; Lithium (medication) ; Crystal (programming language) ; Materials science ; Chemical physics ; Phase (matter) ; Crystallography ; Computer science ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; History and advancements in chemistry
Authors: Anna Ściążko | Yosuke Komatsu | A. Nakamura | Zhufeng Ouyang | Toru Hara | Naoki Shikazono
Source: Chemical Engineering Journal (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

3D microstructures of solid oxide fuel cell Ni-YSZ anodes with carbon deposition Materials science ; Solid oxide fuel cell ; Microstructure ; Carbon fibers ; Anode ; Scanning electron microscope ; Oxide ; Focused ion beam ; Chemical engineering ; Cermet ; Composite material ; Metallurgy ; Electrode ; Ceramic ; Composite number ; Chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: Janghoon Ock | Chakradhar Guntuboina | Amir Barati Farimani
Source: ACS Catalysis (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

Catalyst Energy Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models Efficient catalyst screening necessitates predictive models for adsorption energy, which is a key descriptor of reactivity. Prevailing methods, notably graph neural networks (GNNs), demand precise atomic coordinates for constructing graph representations, while the integration of observable attributes remains challenging. This research introduces CatBERTa, an energy prediction Transformer model that uses textual inputs. Built on a Transformer encoder pretrained for language modeling purposes, CatBERTa processes human-interpretable text, incorporating target features. Attention score analysis reveals CatBERTa’s focus on tokens related to adsorbates, bulk composition, and their interacting atoms. Moreover, interacting atoms emerge as effective descriptors for adsorption configurations, while factors such as the bond length and atomic properties of these atoms offer limited predictive contributions. In predicting the adsorption energy from textual representations of initial structures, CatBERTa exhibits a precision comparable to that of conventional GNNs. Notably, in subsets recognized for their high accuracy with GNNs, CatBERTa consistently achieves a mean absolute error of 0.35 eV. Furthermore, the subtraction of the CatBERTa-predicted energies effectively cancels out their systematic errors by as much as 19.3% for chemically similar systems, surpassing the error reduction observed in GNNs. This outcome highlights its potential to enhance the accuracy of the energy difference predictions. This research establishes a fundamental framework for text-based catalyst property prediction without relying on graph representations while also unveiling intricate feature–property relationships. Computer science ; Encoder ; Observable ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Fuel Cells and Related Materials
Authors: Xiaofang Chen
Source: Inorganics (article, 2023-05-16)
Field: Chemistry — Inorganic Chemistry

Periodic Density Functional Theory (PDFT) Simulating Crystal Structures with Microporous CHA Framework: An Accuracy and Efficiency Study The structure property is the fundamental factor in determining the stability, adsorption, catalytic performance, and selectivity of microporous materials. Seven density functional approximations (DFAs) are used to simulate the crystal structure of microporous material for examining the efficiency and accuracy. In comparison with the existing zeolites, microporous materials with CHA framework are selected as the testing model. The calculation results indicate that the least lattice volume deviation is 5.18/2.72 Å3 from PBE_mGGA, and the second least is −5.55/−10.36 Å3 from LDA_PP. Contrary to USPP_LDA, PBE_GW, PAW_PBE, and PAW_GGA overestimate the lattice volume by ~15.00–20.00 Å3. For each method, RMS deviations are less than 0.016 Å for bond length and less than 2.813° for bond angle. To complete the crystal structure calculation, the CPU time reduces in order of USPP_GGA &gt; PBE_GW &gt; PAW_GGA, PBE_mGGA &gt; PAW_PBE &gt; LDA_PP &gt; USPP_LDA. For two testing models, when the calculation time is not important, PBE_mGGA is the best choice, and when the tradeoff between accuracy and efficiency is considered, LDA_PP is preferred. It seems feasible and efficient to simulate the zeolite structure through E-V curve fitting, full optimization, and phonon analysis bythe periodic density functional theory. Microporous material ; Density functional theory ; Crystal structure ; Lattice (music) ; Materials science ; Computational chemistry ; Thermodynamics ; Chemistry ; Crystallography ; Physics ; Composite material Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Battery Materials and Technologies
Authors: Mariya Vorobyova | Fabio Biffoli | Walter Giurlani | Stefano Martinuzzi | M. Linser | Andréa Caneschi | Massimo Innocenti
Source: Materials (review, 2023-07-10)
Field: Materials Science — Biomaterials

PVD for Decorative Applications: A Review Physical Vapor Deposition (PVD) is a widely utilized process in various industrial applications, serving as a protective and hard coating. However, its presence in fields like fashion has only recently emerged, as electroplating processes had previously dominated this reality. The future looks toward the replacement of the most hazardous and toxic electrochemical processes, especially those involving Cr(VI) and cyanide galvanic baths, which have been restricted by the European Union. Unfortunately, a complete substitution with PVD coatings is not feasible. Currently, the combination of both techniques is employed to achieve new aesthetic features, including a broader color range and diverse textures, rendering de facto PVD of primary interest for the decorative field and the fashion industry. This review aims to outline the guidelines for decorative industries regarding PVD processes and emphasize the recent advancements, quality control procedures, and limitations. Physical vapor deposition ; Rendering (computer graphics) ; Coating ; De facto ; Nanotechnology ; Computer science ; Architectural engineering ; Materials science ; Engineering ; Political science ; Computer graphics (images) Electrospun Nanofibers in Biomedical Applications ; Surface Modification and Superhydrophobicity ; ZnO doping and properties
Authors: You Lü | Kakali Sen | Chin W. Yong | David S. D. Gunn | John A. Purton | Jingcheng Guan | Alec Desmoutier | Jamal Abdul Nasir | Xingfan Zhang | Lei Zhu | Qing Hou | Joe Jackson-Masters | Sam Watts | Rowan Hanson | Harry N. Thomas | Omal Jayawardena | Andrew J. Logsdail | Scott M. Woodley | Hans Martin Senn | Paul Sherwood | C. Richard A. Catlow | Alexey A. Sokol | Thomas W. Keal
Source: Physical Chemistry Chemical Physics (review, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Multiscale QM/MM modelling of catalytic systems with ChemShell Hybrid quantum mechanical/molecular mechanical (QM/MM) methods are a powerful computational tool for the investigation of all forms of catalysis, as they allow for an accurate description of reactions occurring at catalytic sites in the context of a complicated electrostatic environment. The scriptable computational chemistry environment ChemShell is a leading software package for QM/MM calculations, providing a flexible, high performance framework for modelling both biomolecular and materials catalysis. We present an overview of recent applications of ChemShell to problems in catalysis and review new functionality introduced into the redeveloped Python-based version of ChemShell to support catalytic modelling. These include a fully guided workflow for biomolecular QM/MM modelling, starting from an experimental structure, a periodic QM/MM embedding scheme to support modelling of metallic materials, and a comprehensive set of tutorials for biomolecular and materials modelling. QM/MM ; Catalysis ; Multiscale modeling ; Nanotechnology ; Materials science ; Chemistry ; Computational chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Surface Chemistry and Catalysis ; Mesoporous Materials and Catalysis