jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nakaraboina Venkatesh | N. Pavan Kumar | S.N. Goud | D. Ravinder | P. Veera Somaiah | T. Anil Babu | Narasipuram V. K. Prasad | Krishna Prasad | B Sandeep | Somvanshi | A Swapnil | Jadhav | V Mangesh | Khedkar | B Prashant | S Kharat | K More | R Sanmugapriya | P Chaudhary | E Kumar | A Balamurugan | Ch Srinivas | G Prasad | B Yadav | D Sastry | H Emadi | H Mobarak | K Gokduman | G Bate | X Cao | K Sun | C Sung | L Leng | V Kumar | A Rana | M Yadav | R Pant | D Kumar | S Ahmad | Ch Lincoln | D Ravinder | M Warsi | A Iftikhar | M Yousuf | M Sarwar | S Yousaf | S Haider | M Aboud | I Shakir | S Zulfiqar | D Kim | Lee | K Kim | I Shin | Y Lee | N Abhishek | S Pawar | Structural | S Somvanshi | S Patade | D Andhare | S Jadhav | M Khedkar | P Kharat | P Khirade | K Jadhav | F Zhang | S Kantake | Y Kitamoto | M Abe | . Paulsen | A Ring | C Hlo | J Snyder | D Jiles | S Debnath | A Das | R Das | Mangesh Somvanshi | Prashant Khedkar | B Kharat | N Hamdaoui | Y Azizian-Kalandaragh | M Khlifi | L Beji | E Verwey | P Haayman | F Romeijn | H Takagi | S Uchida | H Eiski | S Tanaka | S Raghuvanshi
Source: Biointerface Research in Applied Chemistry (article, 2021-04-09)
Field: Materials Science — Materials Chemistry

FTIR, Optical, Electrical and Magnetic Properties of Sm3+ Doped Mg Nano Ferrites Synthesized Sm, doped Mg nano ferrites with composition MgSmxFe2-xO4 (x = 0,0.025, 0.050, 0.075, 0.100) using the technique of citrate-gel auto-combustion method were analyzed through various experimental techniques. XRD analysis confirmed single-phase cubic spinel structure, while FTIR spectroscopic analysis displayed two absorption peaks that are characteristic of spinel nano ferrites. UV-visible spectral analysis was carried out to study the optical absorption behavior of the prepared ferrites. Two probe method was used to study the dc electrical resistivities of Sm doped Mg nanoferrites between 200 °C–500 °C. A plot between log (σT) vs. inverse temperature yields a curve that reveals prepared nano ferrites' semiconducting nature. A study of TEP (Thermo Electric Power) for prepared samples was carried out at a high temperature (573K). Seebeck coefficient and Curie Temperature of Sm doped Mg nanoferrites were observed. Measurements of magnetization were done by using VSM (Vibrating Sample Magnetometer). Spinel ; Analytical Chemistry (journal) ; Materials science ; Curie temperature ; Doping ; Seebeck coefficient ; Fourier transform infrared spectroscopy ; Nano- ; Magnetization ; Nuclear magnetic resonance ; Ferromagnetism ; Chemistry ; Condensed matter physics ; Optoelectronics ; Optics ; Metallurgy ; Thermal conductivity ; Magnetic field ; Composite material Magnetic Properties and Synthesis of Ferrites
Authors: Weiyi Gong | Qimin Yan
Source: Computational Materials Science (article, 2021-03-02)
Field: Materials Science — Materials Chemistry

Graph-based deep learning frameworks for molecules and solid-state materials Computer science ; Deep learning ; Graph ; Theoretical computer science ; Data science ; Focus (optics) ; Artificial intelligence ; Nanotechnology ; Materials science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yuquan Li | Pengyong Li | Xing Yang | Chang‐Yu Hsieh | Shengyu Zhang | Xiaorui Wang | Ruiqiang Lu | Huanxiang Liu | Xiaojun Yao
Source: Chemical Engineering Journal (article, 2021-02-06)
Field: Materials Science — Materials Chemistry

Introducing block design in graph neural networks for molecular properties prediction Graph ; Artificial neural network ; Block (permutation group theory) ; Computer science ; Representation (politics) ; Quantum chemical ; Atom (system on chip) ; Theoretical computer science ; Process (computing) ; Artificial intelligence ; Algorithm ; Chemistry ; Mathematics ; Molecule ; Combinatorics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Michiel G. Wessels | Arthi Jayaraman
Source: ACS Polymers Au (article, 2021-07-23)
Field: Chemistry — Organic Chemistry

Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions In this article, we present a machine learning enhancement for our recently developed "Computational Reverse Engineering Analysis for Scattering Experiments" (CREASE) method to accelerate analysis of results from small angle scattering (SAS) experiments on polymer materials. We demonstrate this novel artificial neural network (NN) enhanced CREASE approach for analyzing scattering results from amphiphilic polymer solutions that can be easily extended and applied for scattering experiments on other polymer and soft matter systems. We had originally developed CREASE to analyze SAS results [i.e., intensity profiles, <i>I</i>(<i>q</i>) vs <i>q</i>] of amphiphilic polymer solutions exhibiting unconventional assembled structures and/or novel polymer chemistries for which traditional fits using off-the-shelf analytical models would be too approximate/inapplicable. In this paper, we demonstrate that the NN-enhancement to the genetic algorithm (GA) step in the CREASE approach improves the speed and, in some cases, the accuracy of the GA step in determining the dimensions of the complex assembled structures for a given experimental scattering profile. Amphiphile ; Polymer ; Scattering ; Materials science ; Reverse engineering ; Chemical engineering ; Polymer science ; Computer science ; Composite material ; Biological system ; Polymer chemistry ; Engineering ; Optics ; Copolymer ; Physics ; Biology Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Microfluidic and Capillary Electrophoresis Applications
Authors: Artem K. Pimachev | Sanghamitra Neogi
Source: npj Computational Materials (article, 2021-06-17)
Field: Materials Science — Materials Chemistry

First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning Abstract First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to predict properties of heterostructures incorporating fabrication-dependent variability. Machine-learning (ML) approaches are increasingly being used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited ML techniques to characterize relationships between local atomic structures and global electronic transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of fabricated silicon/germanium heterostructures, matching measured data. We demonstrate application of ML approaches to extract important physics that determines electronic transport in semiconductor heterostructures, and bridge the gap between ab initio accessible models and fabricated systems. We anticipate that ETI framework would have broad applicability to diverse materials classes. Heterojunction ; Ab initio ; Semiconductor ; Electronic structure ; Matching (statistics) ; Bridge (graph theory) ; Electronic systems Machine Learning in Materials Science ; Thermal properties of materials ; Surface and Thin Film Phenomena
Authors: Ao Yang | Yang Su | Zihao Wang | Saimeng Jin | Jingzheng Ren | Xiangping Zhang | Weifeng Shen | James H. Clark
Source: Green Chemistry (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A multi-task deep learning neural network for predicting flammability-related properties from molecular structures It is significant that hazardous properties of chemicals including replacements for banned or restricted products are assessed at an early stage of product and process design. Flammability ; Task (project management) ; Hazardous waste ; Artificial neural network ; Process (computing) ; Product (mathematics) ; Computer science ; Artificial intelligence ; Process engineering ; Biochemical engineering ; Machine learning ; Engineering ; Chemistry ; Systems engineering ; Waste management ; Organic chemistry ; Mathematics Thermal and Kinetic Analysis ; Risk and Safety Analysis ; Computational Drug Discovery Methods
Authors: Johannes Klicpera | Florian Becker | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2021-06-02)
Field: Materials Science — Materials Chemistry

GemNet: Universal Directional Graph Neural Networks for Molecules Effectively predicting molecular interactions has the potential to accelerate molecular dynamics by multiple orders of magnitude and thus revolutionize chemical simulations. Graph neural networks (GNNs) have recently shown great successes for this task, overtaking classical methods based on fixed molecular kernels. However, they still appear very limited from a theoretical perspective, since regular GNNs cannot distinguish certain types of graphs. In this work we close this gap between theory and practice. We show that GNNs with directed edge embeddings and two-hop message passing are indeed universal approximators for predictions that are invariant to global rotation and translation, and equivariant to permutation. We then leverage these insights and multiple structural improvements to propose the geometric message passing neural network (GemNet). We demonstrate the benefits of the proposed changes in multiple ablation studies. GemNet outperforms previous models on the COLL and MD17 molecular dynamics datasets by 34% and 40%, performing especially well on the most challenging molecules. Computer science ; Leverage (statistics) ; Theoretical computer science ; Invariant (physics) ; Artificial neural network ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Grégory Fonseca | Igor Poltavsky | Valentín Vassilev-Galindo | Alexandre Tkatchenko | Cordeiro Fonseca, Gregory | Poltavskyi, Igor | Vassilev Galindo, Valentin | Tkatchenko, Alexandre
Source: Open Repository and Bibliography (University of Luxembourg) (article, 2021-03-22)
Field: Materials Science — Materials Chemistry

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), and thus, choosing the training set randomly or according to the probability distribution of the data leads to models whose accuracy is mainly defined by the most common close-to-equilibrium configurations in the reference data. In this work, we combine unsupervised and supervised ML methods to bypass the inherent bias of the data for common configurations, effectively widening the applicability range of the MLFF to the fullest capabilities of the dataset. To achieve this goal, we first cluster the CS into subregions similar in terms of geometry and energetics. We iteratively test a given MLFF performance on each subregion and fill the training set of the model with the representatives of the most inaccurate parts of the CS. The proposed approach has been applied to a set of small organic molecules and alanine tetrapeptide, demonstrating an up to twofold decrease in the root mean squared errors for force predictions on non-equilibrium geometries of these molecules. Furthermore, our ML models demonstrate superior stability over the default training approaches, allowing reliable study of processes involving highly out-of-equilibrium molecular configurations. These results hold for both kernel-based methods (sGDML and GAP/SOAP models) and deep neural networks (SchNet model). Force field (fiction) ; Computer science ; Artificial intelligence ; Set (abstract data type) ; Kernel (algebra) ; Machine learning ; Unsupervised learning ; Artificial neural network ; Field (mathematics) ; Range (aeronautics) ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pascal Friederich | Mario Krenn | Isaac Tamblyn | Alán Aspuru-Guzik
Source: Machine Learning Science and Technology (article, 2021-01-10)
Field: Materials Science — Materials Chemistry

Scientific intuition inspired by machine learning-generated hypotheses Abstract Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science. Intuition ; Realm ; Rule of thumb ; Boosting (machine learning) ; Scientific discovery ; Scientific modelling ; Big data ; Focus (optics) Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Zhong-Hai Ji | Lili Zhang | Dai‐Ming Tang | Chien‐Ming Chen | Torbjörn E. M. Nordling | Zheng-De Zhang | Cui-Lan Ren | Bo Da | Xin Li | Shu-Yu Guo | Chang Liu | Hui–Ming Cheng
Source: Nano Research (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity (IG/ID) was extracted automatically and mapped to the growth parameters to build a database. 1,280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs. Carbon nanotube ; Wafer ; Materials science ; Raman spectroscopy ; Chemical vapor deposition ; Throughput ; Crystallinity ; Nanoparticle ; Nanotechnology ; Chemical engineering ; Computer science ; Composite material ; Engineering ; Optics Carbon Nanotubes in Composites ; Graphene research and applications ; Nanowire Synthesis and Applications
Authors: May Ali Alsaffar | Mohamed Abdel Rahman Abdel Ghany | Jamal M. Ali | Bamidele Victor Ayodele | Siti Indati Mustapa
Source: Topics in Catalysis (article, 2021-01-02)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Modeling of Thermo-catalytic Methane Decomposition for Hydrogen Production Calcination ; Hydrogen production ; Catalysis ; Artificial neural network ; Hydrogen ; Methane ; Materials science ; Biological system ; Chemistry ; Topology (electrical circuits) ; Thermodynamics ; Mathematics ; Computer science ; Physics ; Organic chemistry ; Artificial intelligence Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Hybrid Renewable Energy Systems
Authors: Haoxin Mai | Tu C. Le | Takashi Hisatomi | Dehong Chen | Kazunari Domen | David A. Winkler | Rachel A. Caruso
Source: iScience (article, 2021-08-30)
Field: Materials Science — Materials Chemistry

Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H<sub>2</sub> evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H<sub>2</sub> evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts. Photocatalysis ; Nanotechnology ; Band gap ; Oxide ; Materials science ; Chemistry ; Optoelectronics ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Vaidish Sumaria | Philippe Sautet
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

CO organization at ambient pressure on stepped Pt surfaces: first principles modeling accelerated by neural networks Step and kink sites at Pt surfaces have crucial importance in catalysis. We employ a high dimensional neural network potential (HDNNP) trained using first-principles calculations to determine the adsorption structure of CO under ambient conditions (<i>T</i> = 300 K and <i>P</i> = 1 atm) on these surfaces. To thoroughly explore the potential energy surface (PES), we use a modified basin hopping method. We utilize the explored PES to identify the adsorbate structures and show that under the considered conditions several low free energy structures exist. Under the considered temperature and pressure conditions, the step edge (or kink) is totally occupied by on-top CO molecules. We show that the step structure and the structure of CO molecules on the step dictate the arrangement of CO molecules on the lower terrace. On surfaces with (111) steps, like Pt(553), CO forms quasi-hexagonal structures on the terrace with the top site preferred, with on average two top site CO for one multiply bonded CO, while in contrast surfaces with (100) steps, like Pt(557), present a majority of multiply bonded CO on their terrace. Short terraced surfaces, like Pt(643), with square (100) steps that are broken by kink sites constrain the CO arrangement parallel to the step edge. Overall, this effort provides detailed analysis on the influence of the step edge structure, kink sites, and terrace width on the organization of CO molecules on non-reconstructed stepped surfaces, yielding initial structures for understanding restructuring events driven by CO at high coverages and ambient pressure. Terrace (agriculture) ; Molecule ; Enhanced Data Rates for GSM Evolution ; Chemical physics ; Hexagonal crystal system ; Materials science ; Adsorption ; Ambient pressure ; Surface (topology) ; Square (algebra) ; Nanotechnology ; Chemistry ; Crystallography ; Geometry ; Physics ; Computer science ; Physical chemistry ; Thermodynamics ; Telecommunications Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xianggen Liu | Pengyong Li | Fandong Meng | Hao Zhou | Huasong Zhong | Jie Zhou | Lili Mou | Sen Song
Source: Neurocomputing (article, 2021-09-08)
Field: Materials Science — Materials Chemistry

Simulated annealing for optimization of graphs and sequences Computer science ; Simulated annealing ; Metaheuristic ; Continuous optimization ; Discrete space ; Discrete optimization ; Optimization problem ; Generative grammar ; Artificial intelligence ; Theoretical computer science ; Generative model ; Graph ; Mathematical optimization ; Algorithm ; Multi-swarm optimization ; Mathematics Machine Learning in Materials Science ; Software Engineering Research ; Protein Structure and Dynamics
Authors: Jürgen Köfinger | Gerhard Hummer
Source: The European Physical Journal B (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Empirical optimization of molecular simulation force fields by Bayesian inference Abstract The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi)automatic manner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum mechanical computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges one faces in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and Köfinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior acting on the ensemble. The latter compensates for the unavoidable over simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor–corrector approach, in which we run simulations, determine the reference ensemble using the weighted histogram analysis method (WHAM), and update the force field according to the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, instead of just reweighting a structural ensemble, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi)automatic machine-learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations, and takes advantage of exascale computing resources. Graphic abstract Force field (fiction) ; Observable ; Inference ; Bayesian inference ; Computer science ; Field (mathematics) ; Approximate Bayesian computation ; Bayesian probability ; Statistical physics ; Computation ; Molecular dynamics ; Algorithm ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods