jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prashant Singh | Tyler Del Rose | Andriy Palasyuk | Yaroslav Mudryk
Source: Chemistry of Materials (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials High-performance permanent magnets with a high Curie temperature, containing less critical materials, are integral to zero-carbon energy solutions. We built a machine-learning model trained over available experimentally measured Curie temperature values to predict the TC of multicomponent magnetic materials. We chose two compositions from a pseudo-binary (Zr1–xCex)Fe2 system, namely, (Zr0.16Ce0.84)Fe2 and (Zr0.94Ce0.06)Fe2, to experimentally validate the ability of our model to predict the Curie temperature of novel compounds. We also provided a detailed discussion on the correlation of the Curie temperature with the de Gennes scaling factor in rare-earth intermetallic compounds and its breakdown below a certain rare-earth content. The electronic structure calculations (density of states and Fermi surface) were performed using the density functional theory on selected compounds (Zr0.16Ce0.84)Fe2 and (Zr0.94Ce0.06)Fe2 to understand the electronic origin of a strong magnetic exchange. We found that the change in the electronic density of states and electron/hole fillings at the Fermi level directly correlate with the Curie temperature. Notably, our model was able to capture these key electronic structure trends, which show that physics-informed machine learning can play a crucial role in designing new high-performance magnets with improved properties for environmentally sustainable applications. Curie temperature ; Condensed matter physics ; Curie ; Density functional theory ; Magnet ; Marie curie ; Materials science ; Fermi level ; Local-density approximation ; Fermi surface ; Electronic structure ; Physics ; Electron ; Ferromagnetism ; Nuclear physics ; Quantum mechanics ; Superconductivity Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Volker Eyert | J.L. Wormald | W.A. Curtin | E. Wimmer
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potentials: Recent developments and prospective applications High-throughput generation of large and consistent ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting materials research: (i) while classical interatomic potentials have become indispensable in atomistic simulations, such potentials are typically restricted to certain classes of materials. Machine-learned potentials (MLPs) are applicable to all classes of materials individually and, importantly, to any combinations of them; (ii) MLPs are by design reactive force fields. This Focus Issue provides an overview of the state of the art of MLPs by presenting a range of impressive applications including metallurgy, photovoltaics, proton transport, nanoparticles for catalysis, ionic conductors for solid state batteries, and crystal structure predictions. These investigations provide insight into the current challenges, and they present pathways for their solutions, thus setting the stage for exciting perspectives in computational materials research. Ab initio ; Interatomic potential ; Materials science ; Photovoltaics ; Nanotechnology ; Ionic bonding ; Computer science ; Photovoltaic system ; Computational chemistry ; Molecular dynamics ; Ion ; Physics ; Chemistry ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Salvador Gutiérrez | Juan Fernández‐Novales | Teresa Garde‐Cerdán | Sandra Marín‐San Román | Javier Tardáguila | María P. Diago
Source: Information Fusion (article, 2023-06-02)
Field: Chemistry — Analytical Chemistry

Multi-sensor spectral fusion to model grape composition using deep learning Spectral instruments can be useful for the rapid assessment of chemical compounds in different targets, and their use have been already reported for the modeling of grape composition comparing two spectral ranges. Still, with the increased easiness of acquiring data with several sensors, it would be valuable to explore spectral fusion techniques for the modeling with deep learning, seeking to obtain improved performance. Therefore, the objective of this work was to develop multi-sensor spectral fusion approaches for the deep learning modeling of grape composition. From 128 grape samples, two spectra per sample were acquired from two different ranges using two sensors (visible and shortwave near infrared, 570-1000 nm; and wider NIR 1100-2100 nm). From each sample, 15 grape nitrogen compounds were analyzed by wet chemistry. Three different data fusion approaches are defined using neural networks and deep learning, testing several ways of structuring and merging the input spectra. Statistical analyses supported that i) the proposed deep learning fusion architectures performed better than single spectral range models, and ii) neural networks have better modeling capabilities than partial least squares in spectral fusion. The results demonstrate the potential of deep learning for spectral data fusion in grape nitrogen composition regression, and potentially other traits in food and agriculture spectroscopy. Deep learning ; Fusion ; Sensor fusion ; Partial least squares regression ; Computer science ; Artificial intelligence ; Artificial neural network ; Convolutional neural network ; Range (aeronautics) ; Near-infrared spectroscopy ; Spectral signature ; Pattern recognition (psychology) ; Biological system ; Machine learning ; Remote sensing ; Materials science ; Physics ; Optics ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Jad Mahmoud Halabi | Marieh B. Al‐Handawi | Rodrigo Ceballos | Pancě Naumov
Source: Journal of the American Chemical Society (article, 2023-05-26)
Field: Chemistry — Organic Chemistry

Intersectional Effects of Crystal Features on the Actuation Performance of Dynamic Molecular Crystals Despite being researched for decades, shape-shifting molecular crystals have yet to claim their spot as an actuating materials class among the primary functional materials. While the process for developing and commercializing materials can be lengthy, it inevitably starts with building an extensive knowledge base, which for molecular crystal actuators remains scattered and disjointed. Using machine learning for the first time, we identify inherent features and structure-function relationships that fundamentally impact the mechanical response of molecular crystal actuators. Our model can factor in different crystal properties in tandem and decipher their intersectional and combined effects on each actuation performance. This analysis is an open invitation to utilize interdisciplinary expertise in translating the current basic research on molecular crystal actuators into technology-based development that promotes large-scale experimentation and prototyping. Actuator ; Crystal (programming language) ; Chemistry ; Molecular machine ; Nanotechnology ; Tandem ; Process (computing) ; DECIPHER ; Molecular dynamics ; Mechanical engineering ; Computer science ; Artificial intelligence ; Materials science ; Engineering ; Composite material ; Bioinformatics Supramolecular Chemistry and Complexes ; Silicone and Siloxane Chemistry ; Crystallography and molecular interactions
Authors: Andreas Leitherer | Byung Chul Yeo | Christian H. Liebscher | Luca M. Ghiringhelli
Source: npj Computational Materials (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy Abstract Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials. Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision. Here, we present AI-STEM, an automatic, artificial-intelligence based method, for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy (STEM) images of polycrystalline materials. The method is based on a Bayesian convolutional neural network (BNN) that is trained only on simulated images. AI-STEM automatically and accurately identifies crystal structure, lattice orientation, and location of interface regions in synthetic and experimental images. The model is trained on cubic and hexagonal crystal structures, yielding classifications and uncertainty estimates, while no explicit information on structural patterns at the interfaces is included during training. This work combines principles from probabilistic modeling, deep learning, and information theory, enabling automatic analysis of experimental, atomic-resolution images. Artificial intelligence ; Convolutional neural network ; Computer science ; Scanning transmission electron microscopy ; Resolution (logic) ; Artificial neural network ; High-resolution transmission electron microscopy ; Materials science ; Pattern recognition (psychology) ; Transmission electron microscopy ; Machine learning ; Nanotechnology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuhan Yang | Zhenzi Yu | David S. Sholl
Source: Chemistry of Materials (article, 2023-11-22)
Field: Chemistry — Inorganic Chemistry

Machine Learning Models for Predicting Molecular Diffusion in Metal–Organic Frameworks Accounting for the Impact of Framework Flexibility Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations. Flexibility (engineering) ; Molecular dynamics ; Diffusion ; Molecule ; Metal-organic framework ; Chemical physics ; Statistical physics ; Chemistry ; Computational chemistry ; Nanotechnology ; Materials science ; Adsorption ; Thermodynamics ; Physics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Yibin Zhang | Miaozhuang Fan | Zhourui Xu | Yihang Jiang | Huijun Ding | Zhengzheng Li | Kaixin Shu | Mingyan Zhao | Gang Feng | Ken‐Tye Yong | Biqin Dong | Wei Zhu | Gaixia Xu
Source: Journal of Nanobiotechnology (article, 2023-03-25)
Field: Materials Science — Materials Chemistry

Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging Due to the excellent biocompatible physicochemical performance, luminogens with aggregation-induced emission (AIEgens) characteristics have played a significant role in biomedical fluorescence imaging recently. However, screening AIEgens for special applications takes a lot of time and efforts by using conventional chemical synthesis route. Fortunately, artificial intelligence techniques that could predict the properties of AIEgen molecules would be helpful and valuable for novel AIEgens design and synthesis. In this work, we applied machine learning (ML) techniques to screen AIEgens with expected excitation and emission wavelength for biomedical deep fluorescence imaging. First, a database of various AIEgens collected from the literature was established. Then, by extracting key features using molecular descriptors and training various state-of-the-art ML models, a multi-modal molecular descriptors strategy has been proposed to extract the structure-property relationships of AIEgens and predict molecular absorption and emission wavelength peaks. Compared to the first principles calculations, the proposed strategy provided greater accuracy at a lower computational cost. Finally, three newly predicted AIEgens with desired absorption and emission wavelength peaks were synthesized successfully and applied for cellular fluorescence imaging and deep penetration imaging. All the results were consistent successfully with our expectations, which demonstrated the above ML has a great potential for screening AIEgens with suitable wavelengths, which could boost the design and development of novel organic fluorescent materials. Fluorescence ; Aggregation-induced emission ; Fluorescence-lifetime imaging microscopy ; Nanotechnology ; Chemistry ; Biophysics ; Computational biology ; Materials science ; Physics ; Biology ; Optics Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Sakshi Yadav | Kacper J. Lachowski | Huat Thart Chiang | Lilo D. Pozzo | Jim De Yoreo | Shuai Zhang
Source: Angewandte Chemie International Edition (review, 2023-09-13)
Field: Materials Science — Biomaterials

Mechanisms of Biomolecular Self‐Assembly Investigated Through <i>In Situ</i> Observations of Structures and Dynamics Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This review will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials. Characterization (materials science) ; Nanotechnology ; Computer science ; Mechanism (biology) ; Biochemical engineering ; Materials science ; Engineering ; Physics Supramolecular Self-Assembly in Materials ; Diatoms and Algae Research ; Polydiacetylene-based materials and applications
Authors: Evan R. Antoniuk | Gowoon Cheon | George Wang | Daniel Bernstein | William Cai | Evan J. Reed
Source: npj Computational Materials (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials. Chemical space ; Workflow ; Computer science ; Task (project management) ; Reliability (semiconductor) ; Characterization (materials science) ; Artificial intelligence ; Machine learning ; Nanotechnology ; Materials science ; Drug discovery ; Chemistry ; Database ; Systems engineering ; Engineering ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yu Liu | Jinlin Qian | Yuxin Shi | Xu Yu | Yongjie Mao | Rongguan Lv | Bing Huang | Yuzhen Sun | Zhiyuan Zhao | Yingna Chang | Rong Xing | Huan Pang
Source: Sustainable materials and technologies (article, 2023-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Latest advances of metal-organic frameworks-based materials for supercapacitors Supercapacitor ; Nanotechnology ; Metal-organic framework ; Economic shortage ; Renewable energy ; Materials science ; Energy storage ; Electrochemical energy storage ; Energy density ; Capacitor ; Process engineering ; Electrode ; Electrochemistry ; Engineering physics ; Voltage ; Power (physics) ; Electrical engineering ; Engineering ; Chemistry Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications
Authors: Sanggyu Chong | Federico Grasselli | Chiheb Ben Mahmoud | Joe D. Morrow | Volker L. Deringer | Michele Ceriotti
Source: Journal of Chemical Theory and Computation (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Robustness of Local Predictions in Atomistic Machine Learning Models Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost and also allows for the identification and posthoc interpretation of contributions from individual chemical environments and motifs to complicated macroscopic properties. However, even though practical justifications exist for the local decomposition, only the global quantity is rigorously defined. Thus, when the atom-centered contributions are used, their sensitivity to the training strategy or the model architecture should be carefully considered. To this end, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), that allows one to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of the LPR on the aspects of model training, particularly the composition of training data set, for a range of different problems from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of atomistic ML models. Interpretability ; Robustness (evolution) ; Computer science ; Transferability ; Machine learning ; Artificial intelligence ; Scaling ; Metric (unit) ; Training set ; Test set ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xiaoyu Xue | Min Cheng | Shihui Wang | Shaochen Chen | Li Zhou | Chong Liu | Xu Ji
Source: Industrial & Engineering Chemistry Research (article, 2023-01-06)
Field: Chemistry — Inorganic Chemistry

High-Throughput Screening of Metal–Organic Frameworks Assisted by Machine Learning: Propane/Propylene Separation The separation of a propane (C3H8)/propylene(C3H6) mixture is of paramount importance in the petrochemical industry. Metal–organic frameworks (MOFs), as a class of promising alternative to the traditional adsorbents, have garnered extensive interest. This study proposes a machine learning-assisted high-throughput screening strategy for the identification of suitable MOFs for C3H8/C3H6 separation, striving to accelerate the discovery of high-performance MOF candidates for this particular application. First, a chemical/geometric analysis-based prescreening is applied to a data set of 146 203 MOFs composed of an experimentally synthesized MOF database and a hypothetical MOF database, and MOFs with undesirable chemical/geometric features were excluded. Six structural and nine chemical descriptors were calculated for the remaining MOFs. Random Forest regression algorithm was applied to “learn” the relationship correlations between the features (chemical and/or structural) of MOFs and their C3H8/C3H6 separation capacity. Grand Canonical Monte Carlo (GCMC) simulations were applied to evaluate the C3H8/C3H6 separation performances of the randomly selected training and testing MOF samples. A performance prediction model based on chemical and structural descriptors was obtained with R2 equal to 0.96, which was employed for a separation performance prediction of the remaining MOFs. 2500 MOFs with potential to possess high C3H8/C3H6 separation performance were shortlisted by the prediction model. GCMC simulations were applied to calibrate the prediction results and validate of the machine learning model. MOFs with competitively high C3H8/C3H6 separation potential and regenerability were identified, and a comparison with MOFs reported in the literature was made, indicating that the proposed machine learning-assisted high-throughput screening approach is efficient and effective. Furthermore, structure–property correlation analysis was conducted. Propane ; Petrochemical ; Separation (statistics) ; Metal-organic framework ; Throughput ; Chromatographic separation ; Adsorption ; Computer science ; Materials science ; Process engineering ; Artificial intelligence ; Machine learning ; Chemistry ; Engineering ; Chromatography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science