jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zikai Xie | Xenophon Evangelopoulos | Ömer H. Omar | Alessandro Troisi | Andrew I. Cooper | Linjiang Chen
Source: Chemical Science (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce <i>via</i> random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues. Organic molecules ; Biological system ; Molecule ; Focus (optics) ; Noise (video) ; Resilience (materials science) ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology ; Chemical physics ; Machine learning ; Chemistry ; Physics ; Biology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Benjamin W. J. Chen | Xinglong Zhang | Jia Zhang
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials Active learning of machine learning interatomic potentials enables dynamic modelling of adsorption and reactions at explicitly solvated heterogeneous catalyst interfaces with near ab initio accuracy and greatly reduced computational cost. Molecular dynamics ; Catalysis ; Ab initio ; Chemistry ; Solvent ; Ethylene glycol ; Adsorption ; Chemical physics ; Computational chemistry ; Solvent effects ; Interatomic potential ; Materials science ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Catalytic Processes in Materials Science
Authors: Yunjian Chen | Hong Cui | Qi Jiang | Xue Bai | Pengyue Shan | Zepeng Jia | Sen Lu | Pei Song | Rong Feng | Qin Kang | Zhiyong Liang | Hongkuan Yuan
Source: ACS Applied Nano Materials (article, 2023-05-03)
Field: Materials Science — Materials Chemistry

M-N<sub>4</sub>-Gr/MXene Heterojunction Nanosheets as Oxygen Reduction and Evolution Reaction Catalysts: Machine Learning and Density Functional Theory Insights MXene nanosheet materials with fast charge transfer characteristics and superior electrical conductivity are popular candidates for oxygen electrocatalysts. In this paper, the oxygen reduction and evolution reaction (ORR and OER, respectively) catalytic activities of 78 M-N4-Gr/MXene (Ti2C, Nb2C, and V2C) heterostructure nanosheets were investigated based on density functional theory (DFT) calculations and machine learning (ML) predictions. The random forest regression (RFR) algorithm proved to be the most desirable ML model for the prediction of catalytic activity with ORR/OER root mean square errors of 0.10 and 0.23 V, respectively. The obtained results show that Ni-N4-Gr/Nb2C (η*ORR = 0.31 V) and Ni-N4-Gr/V2C (η*ORR = 0.32 V) are effective ORR catalysts (prediction error of 0.03 V only), Ru-N4-Gr/Nb2C (η*OER = 0.40 V) is a promising OER catalyst, and Co-N4-Gr/V2C (η*ORR = 0.45 V/η*OER = 0.30 V) is an excellent bifunctional catalyst. Through further model analysis, the d band center of the metal active site was determined to be the most effective descriptor and a volcano curve relationship between the intrinsic descriptor and the overpotential was established based on the characteristic importance and Pearson correlation. The research approach adopted in this study may be used to screen and design other MXene heterojunction catalysts and remarkably accelerate catalyst design for many significant reactions. Overpotential ; Catalysis ; Density functional theory ; Bifunctional ; Heterojunction ; Nanosheet ; Oxygen evolution ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Electrochemistry ; Physical chemistry ; Computational chemistry ; Optoelectronics ; Electrode MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Marco Ragone | Reza Shahabazian-Yassar | Farzad Mashayek | Vitaliy Yurkiv
Source: Progress in Materials Science (review, 2023-07-20)
Field: Materials Science — Materials Chemistry

Deep learning modeling in microscopy imaging: A review of materials science applications Microscopy ; Materials science ; Computer science ; Artificial intelligence ; Characterization (materials science) ; Nanotechnology ; Optics ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Irea Mosquera‐Lois | Seán R. Kavanagh | Johan Klarbring | Kasper Tolborg | Aron Walsh
Source: Chemical Society Reviews (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Imperfections are not 0 K: free energy of point defects in crystals Defects determine many important properties and applications of materials, ranging from doping in semiconductors, to conductivity in mixed ionic-electronic conductors used in batteries, to active sites in catalysts. The theoretical description of defect formation in crystals has evolved substantially over the past century. Advances in supercomputing hardware, and the integration of new computational techniques such as machine learning, provide an opportunity to model longer length and time-scales than previously possible. In this Tutorial Review, we cover the description of free energies for defect formation at finite temperatures, including configurational (structural, electronic, spin) and vibrational terms. We discuss challenges in accounting for metastable defect configurations, progress such as machine learning force fields and thermodynamic integration to directly access entropic contributions, and bottlenecks in going beyond the dilute limit of defect formation. Such developments are necessary to support a new era of accurate defect predictions in computational materials chemistry. Crystallographic defect ; Point (geometry) ; Energy (signal processing) ; Materials science ; Chemical physics ; Engineering physics ; Condensed matter physics ; Nanotechnology ; Statistical physics ; Physics ; Quantum mechanics ; Geometry ; Mathematics Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Advanced Semiconductor Detectors and Materials
Authors: Rocío Mercado | Steven Kearnes | Connor W. Coley
Source: Journal of Chemical Information and Modeling (review, 2023-07-05)
Field: Materials Science — Materials Chemistry

Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data The past decade has seen a number of impressive developments in predictive chemistry and reaction informatics driven by machine learning applications to computer-aided synthesis planning. While many of these developments have been made even with relatively small, bespoke data sets, in order to advance the role of AI in the field at scale, there must be significant improvements in the reporting of reaction data. Currently, the majority of publicly available data is reported in an unstructured format and heavily imbalanced toward high-yielding reactions, which influences the types of models that can be successfully trained. In this Perspective, we analyze several data curation and sharing initiatives that have seen success in chemistry and molecular biology. We discuss several factors that have contributed to their success and how we can take lessons from these case studies and apply them to reaction data. Finally, we spotlight the Open Reaction Database and summarize key actions the community can take toward making reaction data more findable, accessible, interoperable, and reusable (FAIR), including the use of mandates from funding agencies and publishers. Bespoke ; Computer science ; Data science ; Interoperability ; Data sharing ; Cheminformatics ; Field (mathematics) ; Informatics ; Data curation ; World Wide Web ; Chemistry ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Kamal Choudhary | Mathew L. Kelley
Source: The Journal of Physical Chemistry C (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

ChemNLP: A Natural Language-Processing-Based Library for Materials Chemistry Text Data In this work, we present the ChemNLP library that can be used for (1) curating open access datasets for materials and chemistry literature, developing and comparing traditional machine learning, transformers and graph neural network models for (2) classifying and clustering texts, (3) named entity recognition for large-scale text-mining, (4) abstractive summarization for generating titles of articles from abstracts, (5) text generation for suggesting abstracts from titles, (6) integration with density functional theory dataset for identifying potential candidate materials such as superconductors, and (7) web-interface development for text and reference query. We primarily use the publicly available arXiv and PubChem datasets, but the tools can be used for other datasets as well. Moreover, as new models are developed, they can be easily integrated in the library. ChemNLP is available at the websites: https://github.com/usnistgov/chemnlp and https://jarvis.nist.gov/jarvischemnlp/. Computer science ; Automatic summarization ; PubChem ; Information retrieval ; Cluster analysis ; Natural language processing ; Question answering ; Artificial intelligence ; World Wide Web ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Biomedical Text Mining and Ontologies
Authors: Philipp Schienbein
Source: Journal of Chemical Theory and Computation (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor [Image: see text] Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is, however, often difficult to understand such spectra at a microscopic level. Ab initio molecular dynamics has repeatedly proved to be suitable for this purpose; however, the computational cost can be daunting. Here, the E(3)-equivariant neural network e3nn is used to fit the atomic polar tensor of liquid water a posteriori on top of existing molecular dynamics simulations. Notably, the introduced methodology is general and thus transferable to any other system as well. The target property is most fundamental and gives access to the IR spectrum, and more importantly, it is a highly powerful tool to directly assign IR spectral features to nuclear motion—a connection which has been pursued in the past but only using severe approximations due to the prohibitive computational cost. The herein introduced methodology overcomes this bottleneck. To benchmark the machine learning model, the IR spectrum of liquid water is calculated, indeed showing excellent agreement with the explicit reference calculation. In conclusion, the presented methodology gives a new route to calculate accurate IR spectra from molecular dynamics simulations and will facilitate the understanding of such spectra on a microscopic level. Polar ; Spectroscopy ; Computer science ; Tensor (intrinsic definition) ; Artificial intelligence ; Chemistry ; Materials science ; Chemical physics ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Nuclear Physics and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Xiaochen Du | James K. Damewood | Jaclyn R. Lunger | Reisel Millan | Bilge Yildiz | Lin Li | Rafael Gómez‐Bombarelli
Source: Nature Computational Science (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated simulations to enable automatic surface reconstruction Computer science ; Surface (topology) ; Surface reconstruction ; Artificial intelligence ; Geometry ; Mathematics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced X-ray and CT Imaging
Authors: Kamal Choudhary | Kevin F. Garrity | Steven Hartman | Ghanshyam Pilania | Francesca Tavazza
Source: Physical Review Materials (article, 2023-01-31)
Field: Materials Science — Materials Chemistry

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning We develop a computational database, website applications (web-apps), and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D) heterostructures. Using density functional theory (DFT) based lattice parameters and electronic band energies for 674 nonmetallic exfoliable 2D materials, we generate 226 779 possible bilayer heterostructures. We classify these heterostructures into type-I, -II, and -III systems according to Anderson's rule, which is based on the relative band alignments of the noninteracting monolayers. We find that type II is the most common and type III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the Periodic Table of constituent elements. The band alignment data can also be used for identifying photocatalysts and high-work-function 2D metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems ($\mathrm{Mo}{\mathrm{S}}_{2}/\mathrm{W}{\mathrm{Se}}_{2}, \mathrm{Mo}{\mathrm{S}}_{2}/\text{h-BN}$, and $\mathrm{Mo}{\mathrm{Se}}_{2}/\mathrm{Cr}{\mathrm{I}}_{3}$), to compare the band-alignment description with the predictions from Anderson's rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools, and associated data will be distributed through the jarvis-heterostructure website. Our analysis, results, and the developed web-apps can be applied to the screening and design applications, such as finding alternative photocatalysts, photodetectors, and high-work-function (WF) 2D-metal contacts. Heterojunction ; Electronic band ; Density functional theory ; Lattice (music) ; Type (biology) ; Electronic band structure ; Materials science ; Condensed matter physics ; Machine learning ; Computer science ; Physics ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Jinhua Zhu | Yingce Xia | Lijun Wu | Shufang Xie | Wengang Zhou | Tao Qin | Houqiang Li | Tie‐Yan Liu
Source:  (article, 2023-08-04)
Field: Materials Science — Materials Chemistry

Dual-view Molecular Pre-training Molecular pre-training, which is about to learn an effective representation for molecules on large amount of data, has attracted substantial attention in cheminformatics and bioinformatics. A molecule can be viewed as either a graph (where atoms are connected by bonds) or a SMILES sequence (where depth-first-search is applied to the molecular graph with specific rules). The Transformer and graph neural networks (GNN) are two representative methods to deal with the sequential data and the graphic data, which can globally and locally model the molecules respectively and are supposed to be complementary. In this work, we propose to leverage both representations and design a new pre-training algorithm, dual-view molecule pre-training (briefly, DVMP), that can effectively combine the strengths of both types of molecule representations. DVMP has a Transformer branch and a GNN branch, and the two branches are pre-trained to maintain the semantic consistency of molecules. After pre-training, we can use either the Transformer branch (this one is recommended according to empirical results), the GNN branch, or both for downstream tasks. DVMP is tested on 11 molecular property prediction tasks and outperforms strong baselines. Furthermore, we test DVMP on three retrosynthesis tasks and it achieves state-of-the-art results. Our code is released at https://github.com/microsoft/DVMP. Computer science ; Molecular graph ; Leverage (statistics) ; Cheminformatics ; Transformer ; Theoretical computer science ; Training set ; Artificial intelligence ; Graph ; Data mining ; Machine learning ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ying Li | Yong Shi | Xuehui Wang | Ding Luo | Yuying Yan
Source: Applied Thermal Engineering (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

Thermal and electrical contact resistances of thermoelectric generator: Experimental study and artificial neural network modelling Thermal and electrical contact resistances (TCR and ECR) of thermoelectric generator (TEG) exert essential impacts on its performance. In this article, through a series of experiments these two important properties have been estimated in a wide range of thermal and mechanical conditions, and with different interfacial materials. The magnitudes of the overall TCR were found in the range of (1.12–2.00)×10–3 m2∙K/W with air, (0.82–1.81) ×10–3 m2∙K/W with graphene sheet, and (3.61–8.37)×10–4 m2∙K/W with thermal grease as interfacial materials when the heat-source temperature varied from 348.15 K to 598.15 K and the imposed pressure load from 266 kPa to 1266 kPa. Importantly, the detailed TCRs at different locations across the thermoelectric generator system were also analyzed. The dominant components, which occupy more than 80% of the overall TCR, have been identified at the interfaces of the thermoelectric module contacting the heat source and heat sink. In our experiment, the corresponding ECRs under the same working conditions were (1.03–1.52)×10–9 Ω∙m2, (0.56–9.60)×10–10 Ω∙m2, and (1.05–6.23)×10–10 Ω∙m2, respectively. Moreover, it is revealed that the TEG system delivered better performance at relatively low TCR and ECR when it operated at a high heat-source temperature, a large pressure load and using thermal grease as interfacial material. In addition to these experimental findings, a novel fully-connected feed-forward artificial neural network (ANN) model was also proposed to predict the overall TCR. It is shown that such an ANN model, as a promising approach, can achieve a cost-effective TCR prediction in good accuracy, with a mean square error and the correlation coefficient being 2.36×10–9 and 99.4%, respectively. These numerical and experimental results in this article will be of particular value for future TEG design and optimization. Materials science ; Thermoelectric effect ; Thermoelectric generator ; Thermal ; Atmospheric temperature range ; Composite material ; Heat sink ; Mechanical engineering ; Thermodynamics ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Chuan Li | Ayesha Khan Tareen | Karim Khan | Jianyu Long | Iftikhar Hussain | Muhammad Farooq Khan | Muhammad Waqas Iqbal | Zhongjian Xie | Ye Zhang | Asif Mahmood | Nasir Mahmood | Waqas Ahmad | Han Zhang
Source: Progress in Solid State Chemistry (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

Highly efficient, remarkable sensor activity and energy storage properties of MXenes and borophene nanomaterials MXenes ; Nanotechnology ; Computer science ; Wearable computer ; Wearable technology ; Embedded system ; Materials science MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Hyunwoong Ko | Yan Lu | Zhuo Yang | Ndeye Y. Ndiaye | Paul Witherell
Source: Journal of Manufacturing Systems (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

A framework driven by physics-guided machine learning for process-structure-property causal analytics in additive manufacturing NIST ; Raw data ; Knowledge extraction ; Computer science ; Big data ; Process (computing) ; Analytics ; Artificial intelligence ; Construct (python library) ; Property (philosophy) ; Data science ; Machine learning ; Testbed ; Data mining ; Natural language processing Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Additive Manufacturing and 3D Printing Technologies
Authors: Waqar Ahmad | Nisar Ahmad | Kun Wang | Sumaira Aftab | Yunpeng Hou | Zhengwei Wan | Beibei Yan | Zhao Pan | Huai‐Ling Gao | Chen Peung | Yang Jun-ke | Chengdu Liang | Zhihui Lu | Wenjun Yan | Min Ling
Source: Advanced Science (review, 2023-11-29)
Field: Materials Science — Materials Chemistry

Electron‐Sponge Nature of Polyoxometalates for Next‐Generation Electrocatalytic Water Splitting and Nonvolatile Neuromorphic Devices Designing next-generation molecular devices typically necessitates plentiful oxygen-bearing sites to facilitate multiple-electron transfers. However, the theoretical limits of existing materials for energy conversion and information storage devices make it inevitable to hunt for new competitors. Polyoxometalates (POMs), a unique class of metal-oxide clusters, have been investigated exponentially due to their structural diversity and tunable redox properties. POMs behave as electron-sponges owing to their intrinsic ability of reversible uptake-release of multiple electrons. In this review, numerous POM-frameworks together with desired features of a contender material and inherited properties of POMs are systematically discussed to demonstrate how and why the electron-sponge-like nature of POMs is beneficial to design next-generation water oxidation/reduction electrocatalysts, and neuromorphic nonvolatile resistance-switching random-access memory devices. The aim is to converge the attention of scientists who are working separately on electrocatalysts and memory devices, on a point that, although the application types are different, they all hunt for a material that could exhibit electron-sponge-like feature to realize boosted performances and thus, encouraging the scientists of two completely different fields to explore POMs as imperious contenders to design next-generation nanodevices. Finally, challenges and promising prospects in this research field are also highlighted. Neuromorphic engineering ; Nanotechnology ; Materials science ; Electron ; Oxide ; Computer science ; Physics Polyoxometalates: Synthesis and Applications ; Conducting polymers and applications ; Electrocatalysts for Energy Conversion