jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eric McCalla
Source: ACS Engineering Au (article, 2023-11-04)
Field: Materials Science — Materials Chemistry

Semiautomated Experiments to Accelerate the Design of Advanced Battery Materials: Combining Speed, Low Cost, and Adaptability A number of methodologies are currently being exploited in order to dramatically increase the composition space explored in the design of new battery materials. This is proving necessary as commercial Li-ion battery materials have become increasingly high-performing and complex. For example, commercial cathode materials have quinary compositions with a sixth element in the coating, while a very large number of contenders are still being considered for solid electrolytes, with most of the periodic table being at play. Furthermore, the promise of accelerated design by computation and machine learning (ML) are encouraging, but they both ultimately require large amounts of quality experimental data either to fill in holes left by the computations or to be used to improve the ML models. All of this leads researchers to increase experimental throughputs. This perspective focuses on semiautomated experimental approaches where automation is only utilized in key steps where absolutely necessary in order to overcome bottlenecks while minimizing costs. Such workflows are more widely accessible to research groups as compared to fully automated systems, such that the current perspective may be useful to a wide community. The most essential steps in automation are related to characterization, with X-ray diffraction being a key bottleneck. By analyzing published workflows of both semi- and fully automated workflows, it is found herein that steps handled by researchers during the synthesis are not prohibitive in terms of overall throughput and may lead to greater flexibility, making more synthesis routes possible. Examples will be provided in this perspective of workflows that have been optimized for anodes, cathodes, and electrolytes in Li batteries, the vast majority of which are also suitable for battery technologies beyond Li. Workflow ; Computer science ; Flexibility (engineering) ; Automation ; Bottleneck ; Throughput ; Battery (electricity) ; Computation ; Adaptability ; Systems engineering ; Computer engineering ; Embedded system ; Mechanical engineering ; Engineering ; Wireless ; Algorithm Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Aparna Loecher | Michael Bruyns‐Haylett | Pedro J. Ballester | Salvador Borrós | Nuria Oliva
Source: Biomaterials Science (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

A machine learning approach to predict cellular uptake of pBAE polyplexes The delivery of genetic material (DNA and RNA) to cells can cure a wide range of diseases but is limited by the delivery efficiency of the carrier system. Poly β-amino esters (pBAEs) are promising polymer-based vectors that form polyplexes with negatively charged oligonucleotides, enabling cell membrane uptake and gene delivery. pBAE backbone polymer chemistry, as well as terminal oligopeptide modifications, define cellular uptake and transfection efficiency in a given cell line, along with nanoparticle size and polydispersity. Moreover, uptake and transfection efficiency of a given polyplex formulation also vary from cell type to cell type. Therefore, finding the optimal formulation leading to high uptake in a new cell line is dictated by trial and error, and requires time and resources. Machine learning (ML) is an ideal <i>in silico</i> screening tool to learn the non-linearities of complex data sets, like the one presented herein, with the aim of predicting cellular internalisation of pBAE polyplexes. A library of pBAE nanoparticles was fabricated and the uptake studied in 4 different cell lines, on which various ML models were successfully trained. The best performing models were found to be gradient-boosted trees and neural networks. The gradient-boosted trees model was then analysed using SHapley Additive exPlanations, to interpret the model and gain an understanding into the important features and their impact on the predicted outcome. Chemistry ; Computer science Advanced Polymer Synthesis and Characterization ; RNA Interference and Gene Delivery ; Click Chemistry and Applications
Authors: Zaixi Zhang | Qi Liu | Chee‐Kong Lee | Chang‐Yu Hsieh | Enhong Chen
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

An equivariant generative framework for molecular graph-structure Co-design Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for <i>de novo</i> molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including <i>de novo</i> molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% validity) and diverse (98.75% uniqueness) molecular graphs/structures with desirable properties, but also generates drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation. Equivariant map ; Generative grammar ; Graph ; Computer science ; Mathematics ; Theoretical computer science ; Pure mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Yeonghun Kang | Hyunsoo Park | Berend Smit | Jihan Kim
Source: ChemRxiv (preprint, 2023-01-18)
Field: Chemistry — Inorganic Chemistry

A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-Organic Frameworks Metal-organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a vast chemical space due to their tunable molecular building blocks with diverse topologies. Given that an unlimited number of MOFs can, in principle, be synthesized, constructing structure-property relationships through a machine learning approach allows for efficient exploration of this vast chemical space, resulting in identifying optimal candidates with desired properties. In this work, we introduce MOFTransformer, a multi-model Transformer encoder pre-trained with 1 million hypothetical MOFs. This multi-modal model utilizes integrated atom-based graph and energy-grid embeddings to capture both local and global features of MOFs, respectively. By fine-tuning the pre-trained model with small datasets ranging from 5,000 to 20,000 MOFs, our model achieves state-of-the-art results for predicting across various properties including gas adsorption, diffusion, electronic properties, and even text-mined data. Beyond its universal transfer learning capabilities, MOFTransformer generates chemical insights by analyzing feature importance through attention scores within the self-attention layers. As such, this model can serve as a bedrock platform for other MOF researchers that seek to develop new machine learning models for their work. Computer science ; Chemical space ; Transformer ; Encoder ; Transfer of learning ; Autoencoder ; Modal ; Grid ; Ranging ; Artificial intelligence ; Deep learning ; Machine learning ; Theoretical computer science ; Materials science ; Drug discovery ; Engineering ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zachary J. L. Bare | Ryan J. Morelock | Charles B. Musgrave
Source: Scientific Data (article, 2023-04-28)
Field: Materials Science — Materials Chemistry

Dataset of theoretical multinary perovskite oxides Perovskite oxides (ternary chemical formula ABO<sub>3</sub>) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula [Formula: see text]) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed [Formula: see text] compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 [Formula: see text] compositions were generated in the a<sup>-</sup>b<sup>+</sup>a<sup>-</sup> Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔH<sub>f</sub>) and decomposition enthalpies (ΔH<sub>d</sub>) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated [Formula: see text] perovskite structures using MP-compatible DFT calculations. Materials science ; Algorithm ; Computer science Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Hai-Xu Zou | Xiang Bai | Xing Fan | Minhua Wang | Yang-Yang Xu | Feng‐Yun Ma | Xian‐Yong Wei | П. Н. Кузнецов
Source: Fuel (article, 2023-07-13)
Field: Chemistry — Analytical Chemistry

Infrared spectroscopic evaluation for catalytic hydrogenation of biomass and coal using unsupervised and supervised algorithms Principal component analysis ; Coal ; Biomass (ecology) ; Chemistry ; Algorithm ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Mathematics ; Organic chemistry ; Geology Spectroscopy and Chemometric Analyses ; Thermochemical Biomass Conversion Processes ; Industrial Technology and Control Systems
Authors: Luis M. Antunes | Keith T. Butler | Ricardo Grau‐Crespo
Source: Machine Learning Science and Technology (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Predicting thermoelectric transport properties from composition with attention-based deep learning Abstract Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deployment on global scales, new classes of effective thermoelectrics are thus required. Ab initio models of transport properties can help in the design of new thermoelectrics, but they are still too computationally expensive to be solely relied upon for high-throughput screening in the vast chemical space of all possible candidates. Here, we use models constructed with modern machine learning techniques to scan very large areas of inorganic materials space for novel thermoelectrics, using composition as an input. We employ an attention-based deep learning model, trained on data derived from ab initio calculations, to predict a material’s Seebeck coefficient, electrical conductivity, and power factor over a range of temperatures and n - or p -type doping levels, with surprisingly good performance given the simplicity of the input, and with significantly lower computational cost. The results of applying the model to a space of known and hypothetical binary and ternary selenides reveal several materials that may represent promising thermoelectrics. Our study establishes a protocol for composition-based prediction of thermoelectric behaviour that can be easily enhanced as more accurate theoretical or experimental databases become available. Thermoelectric materials ; Thermoelectric effect ; Ternary operation ; Computer science ; Seebeck coefficient ; Ab initio ; Thermoelectric generator ; Materials science ; Engineering physics ; Thermodynamics ; Chemistry ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Stanley Lo | Martin Seifrid | Théophile Gaudin | Alán Aspuru‐Guzik
Source: Journal of Chemical Information and Modeling (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Augmenting Polymer Datasets by Iterative Rearrangement One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the success of data augmentation in computer vision and natural language processing, we explore augmenting polymer data by iteratively rearranging the molecular representation while preserving the correct connectivity, revealing additional substructural information that is not present in a single representation. We evaluate the effects of this technique on the performance of machine learning models trained on three polymer datasets and compare them to common molecular representations. Data augmentation does not yield significant improvements in machine learning property prediction performance compared to equivalent (non-augmented) representations. In datasets where the target property is primarily influenced by the polymer sequence rather than experimental parameters, this data augmentation technique provides molecular embedding with more information to improve property prediction accuracy. Representation (politics) ; Property (philosophy) ; Computer science ; Embedding ; Sequence (biology) ; Artificial intelligence ; Machine learning ; Polymer ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Stefan Riemelmoser | Carla Verdi | Merzuk Kaltak | Georg Kresse
Source: Journal of Chemical Theory and Computation (article, 2023-10-06)
Field: Materials Science — Materials Chemistry

Machine Learning Density Functionals from the Random-Phase Approximation Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we use machine learning to map the RPA to a pure Kohn-Sham density functional. The machine learned RPA model (ML-RPA) is a nonlocal extension of the standard gradient approximation. The density descriptors used as ingredients for the enhancement factor are nonlocal counterparts of the local density and its gradient. Rather than fitting only RPA exchange-correlation energies, we also include derivative information in the form of RPA optimized effective potentials. We train a single ML-RPA functional for diamond, its surfaces, and liquid water. The accuracy of ML-RPA for the formation energies of 28 diamond surfaces reaches that of state-of-the-art van der Waals functionals. For liquid water, however, ML-RPA cannot yet improve upon the standard gradient approximation. Overall, our work demonstrates how machine learning can extend the applicability of the RPA to larger system sizes, time scales, and chemical spaces. Random phase approximation ; van der Waals force ; Density functional theory ; Diamond ; Local-density approximation ; Work (physics) ; Statistical physics ; Computer science ; Physics ; Materials science ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Jingjin He | Changxin Wang | Junjie Li | Chuanbao Liu | Dezhen Xue | Jiangli Cao | Yanjing Su | Lijie Qiao | Turab Lookman | Yang Bai
Source: Journal of Advanced Ceramics (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics In materials science and engineering, obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult. In this work, we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system, <em>i.e.</em> (Ba<sub>1</sub><sub>−<em>x</em>−<em>y</em></sub>Ca<em><sub>x</sub></em>Sr<em><sub>y</sub></em>)(Ti<sub>1</sub><sub>−<em>u</em>−<em>v</em>-<em>w</em></sub>Zr<em><sub>u</sub></em>Sn<em><sub>v</sub></em>Hf<em><sub>w</sub></em>)O<sub>3</sub>. The deep neural network model uses physical features as inputs and directly outputs the full spectrum, in addition to yielding the octahedral factor, Matyonov-Batsanov electronegativity, ratio of valence electron to nuclear charge and core electron distance (Schubert) as four key descriptors. Owing to the physically meaningful features, our model exhibits better performance and generalization ability in the broader composition space of BaTiO<sub>3</sub> based solid solutions. And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature. Furthermore, the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information. The prediction are also experimentally validated by typical samples of (Ba<sub>0.85</sub>Ca<sub>0.15</sub>)(Ti<sub>0.98–<em>x</em></sub>Zr<em><sub>x</sub></em>Hf<sub>0.02</sub>)O<sub>3</sub>. The work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information. Dielectric ; Ferroelectricity ; Electronegativity ; Artificial neural network ; Materials science ; Valence (chemistry) ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Computer science ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Multiferroics and related materials
Authors: Yang Liu | Xiaojia Yang | Bingqin Wang | Zifan Wang | Xuequn Cheng | Xiaogang Li
Source: Journal of Materials Research and Technology (article, 2023-06-19)
Field: Materials Science — Metals and Alloys

Corrosion resistance optimization of Sn-additional low-alloy high strength steel by data-driven identification and field exposure verification Corrosion resistance is a critical consideration in the selection of materials for various applications. In this study, we employed a data-driven approach using machine learning techniques and a large dataset of corrosion data to design and test four different low-alloy steels with varying amounts of tin (Sn) microalloying (0.1 wt.%, 0.2 wt.%, 0.3 wt.% and Sn-free) for improved corrosion resistance in Beijing outdoor atmosphere. Using experimental methods such as corrosion morphology and rust layer analysis, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and potentiodynamic polarization measurements, we verified that the 0.2 wt.% Sn microalloying steel exhibited the best corrosion resistance. Our findings demonstrate the potential of data-driven approaches and machine learning techniques, such as the use of corrosion big data, in the identification and optimization of optimal alloy compositions of corrosion-resistant materials for outdoor environments. Corrosion ; Materials science ; Alloy ; Metallurgy ; X-ray photoelectron spectroscopy ; Polarization (electrochemistry) ; Alloy steel ; Tin ; Chemical engineering Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Liang Gao | Liquan Wang | Jiaping Lin | Lei Du
Source: Engineering (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

An Intelligent Manufacturing Platform of Polymers: Polymeric Material Genome Engineering Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing. Polymeric material genome engineering (PMGE) is becoming a vital platform for the intelligent manufacturing of polymeric materials. However, the development of PMGE is still in its infancy, and many issues remain to be addressed. In this perspective, we elaborate on the PMGE concepts, summarize the state-of-the-art research and achievements, and highlight the challenges and prospects in this field. In particular, we focus on property estimation approaches, including property proxy prediction and machine learning prediction of polymer properties. The potential engineering applications of PMGE are discussed, including the fields of advanced composites, polymeric materials for communications, and integrated circuits. Computer science ; Field (mathematics) ; Systems engineering ; Nanotechnology ; Engineering ; Manufacturing engineering ; Materials science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Nayem Ahmed | Mohamed Aldaw | Ramadan Ahmed | Cătălin Teodoriu
Source: Engineering Failure Analysis (article, 2023-12-09)
Field: Materials Science — Metals and Alloys

Modeling of necking area reduction of carbon steel in hydrogen environment using machine learning approach Necking ; Reduction (mathematics) ; Carbon fibers ; Materials science ; Hydrogen ; Carbon steel ; Composite material ; Chemistry ; Composite number ; Mathematics ; Corrosion ; Geometry Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming