jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Morgan Thomas | Noel M. O’Boyle | Andreas Bender | Chris de Graaf
Source: ChemRxiv (preprint, 2022-04-14)
Field: Materials Science — Materials Chemistry

Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 10^5 molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-efficiency by addressing the limitations of both currently used strategies. We compare its ability to optimize several docking tasks with REINVENT and benchmark this strategy against other commonly used reinforcement learning strategies including REINFORCE, REINVENT (version 1 & 2), Hill-Climb and best agent reminder. We find that optimization ability is improved ~1.5-fold and sample-efficiency is improved ~45-fold compared to REINVENT while still delivering appealing chemistry as output. Diversity filters were used, and their parameters were tuned to overcome observed failure modes that take advantage of certain diversity filter configurations. Lastly, we find that Augmented Hill-Climb outperforms the other reinforcement learning strategies used on six tasks, especially in the early stages of training or for more difficult objectives. Overall, we hence show that AHC improves sample-efficiency for language-based de novo molecule generation conditioning via reinforcement learning, compared to the current state-of-the-art. This makes more computationally expensive scoring functions, such as docking, more accessible on a relevant timescale. Reinforcement learning ; Computer science ; Climb ; Artificial intelligence ; Reinforcement ; Context (archaeology) ; Benchmark (surveying) ; Machine learning ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Wafaa A. Shatti | Zena Mohammed Ali Abbas | Ziad T. Khodair
Source: Journal of Ovonic Research (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Co-precipitation method for the preparation of Mn-Zn Ferrite and study their Structural and Magnetic properties "Ferrite nanoparticles were synthesized using the co-deposition method with pH = 12. This paper discusses the differences in composition and magnetic properties that contribute to the MnZn ferritic's properties. Samples were prepared, fabricated and annealed at two temperatures (600°C, 900°C). The crystal structure of the prepared samples was obtained using X-ray diffraction spectroscopy, Fourier transform infrared spectroscopy (FTIR) and field emitting electron microscopy (FESEM), respectively. The magnetic properties of the samples prepared by assay (VSM) were obtained and the magnetic and structural properties were discussed based on the changing temperature." Fourier transform infrared spectroscopy ; Materials science ; Ferrite (magnet) ; Analytical Chemistry (journal) ; Diffraction ; Spectroscopy ; Precipitation ; Infrared spectroscopy ; Chemical engineering ; Chemistry ; Composite material ; Chromatography ; Optics Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Yu-Jae Jeon | Jong-Hwan Yun | Min-Soo Kang
Source: Materials (article, 2022-11-12)
Field: Materials Science — Polymers and Plastics

Analysis of Elastic Properties of Al/PET Isotropic Composite Materials Using Finite Element Method This study uses the finite element method and numerical analysis to develop an eco-friendly composite material with shielding capabilities. A preliminary study was performed to predict the mechanical properties of the composite material. Polyethylene terephthalate and aluminum powder (AP) were selected as the matrix and enhancer, respectively. The particles of AP are spherical, with a diameter of 1 μm. Material properties were investigated as the AP volume fraction (VF) increased from 5-70%. The FEM results show that the physical properties for AP VFs improve by up to 40%, but there is no significant change in the elastic modulus, shear modulus, and Poisson's ratio at an AP VF of 50-70%. However, the numerical analysis models show that the elastic properties for AP VFs improve by up to 70%. The mechanical properties improved as the VF increased, and the FEM predicted values were reliable for VFs up to 40%. However, it was confirmed that 40% is the limit of AP VF in the FEM. In addition, the FEM and numerical analysis predictions showed that the most similar numerical analysis model was the Halpin-Tsai model. The predictions of the Halpin-Tsai model allowed prediction of the maximum VF above the FEM limit. If the correction coefficients of the FEM and numerical analysis models are derived based on the predictions of this study and future experimental results, reliable predictions can be obtained for the physical properties of composite materials. Materials science ; Finite element method ; Composite material ; Isotropy ; Numerical analysis ; Composite number ; Elastic modulus ; Modulus ; Material properties ; Poisson's ratio ; Shear modulus ; Structural engineering ; Poisson distribution ; Mathematics Natural Fiber Reinforced Composites ; Material Properties and Applications ; Composite Material Mechanics
Authors: Qilin Yi | Gang Wei | Zhengqin Cao | Xiaoyu Wu | Yuanyuan Gao
Source: Adsorption Science & Technology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Adsorption Properties of NF<sub>3</sub> and N<sub>2</sub>O on Al- and Ga-Doped Graphene Surface: A Density Functional Theory Study SF 6 /N 2 gas mixture decomposition components can reflect the operation status inside GIS, and be used for fault diagnosis and monitoring inside GIS. NF 3 and N 2 O are the characteristic decomposition components of SF 6 /N 2 mixed gas. In order to find a potential gas sensitivity material for the detection of NF 3 and N 2 O. This paper investigated the adsorption properties of NF 3 and N 2 O on Al- and Ga- doped graphene monolayers based on density functional theory. Through the analysis of adsorption distance, charge transfer, adsorption energy, energy band structure, etc., the results indicated that the adsorption effect of Al- and Ga-doped graphene to NF 3 and N 2 O are probably good, and these nanomaterials are potential to apply for the monitoring of GIS internal faults. Adsorption ; Graphene ; Chemistry ; Density functional theory ; Monolayer ; Decomposition ; Doping ; Nanomaterials ; Nanotechnology ; Physical chemistry ; Computational chemistry ; Materials science ; Organic chemistry ; Optoelectronics Graphene research and applications ; MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yik-Ling Chew | Hon-Kent Lee | Mei-Ann Khor | Kai Bin Liew | Bontha Venkata Subrahmanya Lokesh | Gabriel Akyirem Akowuah
Source: Indian Journal of Pharmaceutical Education and Research (article, 2022-01-12)
Field: Chemistry — Analytical Chemistry

Forced Degradation of Flibanserin Bulk Drug: Development and Validation of Stability Indicating RP-HPLC Method Background: Flibanserin had been approved as the first drug by United State Food and Drug Administration (USFDA) for the treatment of female sexual interest/arousal disorder of any severity. However, the stability of this drug has yet to be studied extensively. Forced degradation ; Chemistry ; Degradation (telecommunications) ; High-performance liquid chromatography ; Chromatography ; Kinetics ; Food and drug administration ; Pharmacology ; Computer science Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: Amir Mohammad Entekhabi Nooshabadi | Shakiba Sadeghi | Hamed Hashemi‐Dezaki
Source: International Transactions on Electrical Energy Systems (article, 2022-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software’s capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model’s characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models’ characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library. Emtp ; Particle swarm optimization ; Engineering ; Electric arc furnace ; Genetic algorithm ; Software ; Electric power system ; Power (physics) ; Algorithm ; Mathematical optimization ; Computer science ; Mathematics Magnetic Properties and Applications ; Welding Techniques and Residual Stresses ; Power Quality and Harmonics
Authors: Austin D. Sendek | Ekin D. Cubuk | Brandi Ransom | Jagjit Nanda | Evan J. Reed
Source:  (review, 2022-04-08)
Field: Materials Science — Materials Chemistry

Machine‐Learning and Data‐Intensive Methods for Accelerating the Development of Rechargeable Battery Chemistries: A Review We review the field of machine-learning (ML) and data-intensive methods for rechargeable battery materials development. We highlight advancements in high-throughput density functional theory calculation of materials properties, and discuss how these advancements have enabled the application of ML or data-driven methods to accelerate property prediction. We provide an overview of the fundamentals of machine learning, including a discussion of underfitting vs. overfitting and the importance of model selection. We explore notable examples of ML-based modeling in solid electrolyte, cathode, and anode design, and note the potential for these approaches to accelerate materials discovery in liquid electrolytes and electrochemical capacitors. We conclude with our observations of best practices in applying ML to the space of battery materials. Overfitting ; Battery (electricity) ; Computer science ; Throughput ; Anode ; Artificial intelligence ; Cathode ; Field (mathematics) ; Machine learning ; Nanotechnology ; Materials science ; Artificial neural network ; Engineering ; Electrical engineering ; Wireless ; Chemistry ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; Fuel Cells and Related Materials
Authors: Eduardo Poggio‐Fraccari | Caré Damián | Mariño Fernando
Source: International Journal of Hydrogen Energy (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

A comparison of deep learning models applied to Water Gas Shift catalysts for hydrogen purification Artificial neural network ; Principal component analysis ; Computer science ; Water-gas shift reaction ; Catalysis ; Grid ; Biological system ; Feature (linguistics) ; Artificial intelligence ; Volume (thermodynamics) ; Pattern recognition (psychology) ; Machine learning ; Chemistry ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Santiago Miret | Kin Long Kelvin Lee | Carmelo Gonzales | Marcel Nassar | Matthew Spellings
Source: arXiv (Cornell University) (preprint, 2022-10-31)
Field: Materials Science — Materials Chemistry

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1. A scalable machine learning workflow for materials science leveraging PyTorch Lightning, which enables seamless scaling across different computation capabilities (laptop, server, cluster) and hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for rapid graph neural network prototyping and development. By publishing and sharing this toolkit with the research community via open-source release, we hope to: 1. Lower the entry barrier for new machine learning researchers and practitioners that want to get started with the OpenCatalyst dataset, which presently comprises the largest computational materials science dataset. 2. Enable the scientific community to apply advanced machine learning tools to high-impact scientific challenges, such as modeling of materials behavior for clean energy applications. We demonstrate the capabilities of our framework by enabling three new equivariant neural network models for multiple OpenCatalyst tasks and arrive at promising results for compute scaling and model performance. Computer science ; Workflow ; Scalability ; Python (programming language) ; Deep learning ; Artificial intelligence ; Machine learning ; Artificial neural network ; Laptop ; Computer architecture ; Software engineering ; Operating system ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Luis A. Miccio | Claudia Borredon | Ulises Casado | Anh D. Phan | Gustavo A. Schwartz
Source: Polymers (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

Approaching Polymer Dynamics Combining Artificial Neural Networks and Elastically Collective Nonlinear Langevin Equation The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time-consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer. Nonlinear system ; Langevin equation ; Relaxation (psychology) ; Statistical physics ; Polymer ; Langevin dynamics ; Artificial neural network ; Work (physics) ; Materials science ; Monomer ; Dynamics (music) ; Biological system ; Computer science ; Thermodynamics ; Physics ; Artificial intelligence Material Dynamics and Properties ; Mechanical and Optical Resonators ; Microfluidic and Capillary Electrophoresis Applications
Authors: Joung Sik Suh | Byeong‐Chan Suh | Jun Ho Bae | Young Min Kim
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Biomaterials

Machine Learning-Based Design of Biodegradable Mg Alloys for Load-Bearing Implants Load bearing ; Bearing (navigation) ; Materials science ; Biomedical engineering ; Mechanical engineering ; Computer science ; Engineering ; Composite material ; Artificial intelligence Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Surface Treatment and Coatings
Authors: Yuxing Ma | Shuaihua Lu | Yehui Zhang | Tingbo Zhang | Qionghua Zhou | Jinlan Wang
Source: Applied Physics Letters (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Accurate energy prediction of large-scale defective two-dimensional materials via deep learning Crystal defects play a vital role in physical and chemical properties of two-dimensional (2D) materials. The computational cost for a real defective system with more than thousands of atoms is considerably high. Here, we propose a framework to accurately predict the formation energy of defective 2D materials, graphene and molybdenum disulfide (MoS2), at a large scale, by combining deep learning techniques and density functional calculations. To improve the training performance of deep learning models, a multi-layer structure descriptor using chemical bond parameters is proposed. For the defective graphene (MoS2) over 300 nm2 (650 nm2), the calculated mean absolute error for the formation energy is less than 47 meV (53 meV) per 1000 atoms. This study provides a practical solution for the accurate and rapid description of large-scale defective 2D materials. Graphene ; Molybdenum disulfide ; Scale (ratio) ; Materials science ; Deep learning ; Density functional theory ; Atomic units ; Energy (signal processing) ; Artificial intelligence ; Computer science ; Nanotechnology ; Chemical physics ; Statistical physics ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Shusen Liu | Bhavya Kailkhura | Jize Zhang | Anna M. Hiszpanski | Emily Robertson | Donald Loveland | Xiaoting Zhong | T. Yong-Jin Han
Source: ACS Omega (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis The materials science community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite their effectiveness in building highly predictive models, e.g., predicting material properties from microstructure imaging, due to their opaque nature fundamental challenges exist in extracting meaningful domain knowledge from the deep neural networks. In this work, we propose a technique for interpreting the behavior of deep learning models by injecting domain-specific attributes as tunable "knobs" in the material optimization analysis pipeline. By incorporating the material concepts in a generative modeling framework, we are able to explain what structure-to-property linkages these black-box models have learned, which provides scientists with a tool to leverage the full potential of deep learning for domain discoveries. Deep learning ; Leverage (statistics) ; Artificial intelligence ; Computer science ; Domain (mathematical analysis) ; Artificial neural network ; Pipeline (software) ; Generative grammar ; Machine learning ; Black box ; Deep neural networks ; Domain knowledge ; Data science ; Mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Enhanced Oil Recovery Techniques
Authors: Ian Rouse | Vladimir Lobaskin
Source: Faraday Discussions (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Machine-learning based prediction of small molecule–surface interaction potentials Predicting the adsorption affinity of a small molecule to a target surface is of importance to a range of fields, from catalysis to drug delivery and human safety, but a complex task to perform computationally when taking into account the effects of the surrounding medium. We present a flexible machine-learning approach to predict potentials of mean force (PMFs) and adsorption energies for chemical-surface pairs from the separate interaction potentials of each partner with a set of probe atoms. We use a pre-existing library of PMFs obtained <i>via</i> atomistic molecular dynamics simulations for a variety of inorganic materials and molecules to train the model. We find good agreement between original and predicted PMFs in both training and validation groups, confirming the predictive power of this approach, and demonstrate the flexibility of the model by producing PMFs for molecules and surfaces outside the training set. Flexibility (engineering) ; Molecule ; Adsorption ; Set (abstract data type) ; Molecular dynamics ; Surface (topology) ; Task (project management) ; Range (aeronautics) ; Computer science ; Predictive power ; Chemical physics ; Chemistry ; Biological system ; Computational chemistry ; Materials science ; Physics ; Physical chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Udaykumar Gajera | Loriano Storchi | Danila Amoroso | Francesco Delodovici | Silvia Picozzi
Source: Journal of Applied Physics (article, 2022-06-07)
Field: Materials Science — Materials Chemistry

Toward machine learning for microscopic mechanisms: A formula search for crystal structure stability based on atomic properties Machine-learning techniques are revolutionizing the way to perform efficient materials modeling. We here propose a combinatorial machine-learning approach to obtain physical formulas based on simple and easily accessible ingredients, such as atomic properties. The latter are used to build materials features that are finally employed, through linear regression, to predict the energetic stability of semiconducting binary compounds with respect to zinc blende and rocksalt crystal structures. The adopted models are trained using a dataset built from first-principles calculations. Our results show that already one-dimensional (1D) formulas well describe the energetics; a simple grid-search optimization of the automatically obtained 1D-formulas enhances the prediction performance at a very small computational cost. In addition, our approach allows one to highlight the role of the different atomic properties involved in the formulas. The computed formulas clearly indicate that “spatial” atomic properties (i.e., radii indicating maximum probability densities for s,p,d electronic shells) drive the stabilization of one crystal structure with respect to the other, suggesting the major relevance of the radius associated with the p-shell of the cation species. Stability (learning theory) ; Simple (philosophy) ; Atomic radius ; Relevance (law) ; Binary number ; RADIUS ; Crystal (programming language) ; Computer science ; Statistical physics ; Artificial intelligence ; Crystal structure prediction ; Machine learning ; Algorithm ; Crystal structure ; Physics ; Chemistry ; Mathematics ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods