jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinyoung Seo | Jason D. Braun | Vidhya M. Dev | Jarad A. Mason
Source: Journal of the American Chemical Society (article, 2022-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Driving Barocaloric Effects in a Molecular Spin-Crossover Complex at Low Pressures Barocaloric effects─thermal changes in a material induced by applied hydrostatic pressure─offer promise for creating solid-state refrigerants as alternatives to conventional volatile refrigerants. To enable efficient and scalable barocaloric cooling, materials that undergo high-entropy, reversible phase transitions in the solid state in response to a small change in pressure are needed. Here, we report that pressure-induced spin-crossover (SCO) transitions in the molecular iron(II) complex Fe[HB(tz)<sub>3</sub>]<sub>2</sub> (HB(tz)<sub>3</sub><sup>-</sup> = bis[hydrotris(1,2,4-triazol-1-yl)borate]) drive giant and reversible barocaloric effects at easily accessible pressures. Specifically, high-pressure calorimetry and powder X-ray diffraction studies reveal that pressure shifts as low as 10 bar reversibly induce nonzero isothermal entropy changes, and a pressure shift of 150 bar reversibly induces a large isothermal entropy change (>90 J kg<sup>-1</sup> K<sup>-1</sup>) and adiabatic temperature change (>2 K). Moreover, we demonstrate that the thermodynamics of the SCO transition can be fine-tuned through systematic deuteration of the tris(triazolyl)borate ligand. These results provide new insights into pressure-induced SCO transitions and further establish SCO materials as promising barocaloric materials. Chemistry ; Spin crossover ; Crossover ; Spin (aerodynamics) ; Condensed matter physics ; Chemical physics ; Thermodynamics ; Crystallography ; Physics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Youkun Zheng | Min Wei | Haibin Wu | Fangyuan Li | Daishun Ling
Source: Journal of Nanobiotechnology (review, 2022-07-16)
Field: Materials Science — Materials Chemistry

Antibacterial metal nanoclusters Combating bacterial infections is one of the most important applications of nanomedicine. In the past two decades, significant efforts have been committed to tune physicochemical properties of nanomaterials for the development of various novel nanoantibiotics. Among which, metal nanoclusters (NCs) with well-defined ultrasmall size and adjustable surface chemistry are emerging as the next-generation high performance nanoantibiotics. Metal NCs can penetrate bacterial cell envelope more easily than conventional nanomaterials due to their ultrasmall size. Meanwhile, the abundant active sites of the metal NCs help to catalyze the bacterial intracellular biochemical processes, resulting in enhanced antibacterial properties. In this review, we discuss the recent developments in metal NCs as a new generation of antimicrobial agents. Based on a brief introduction to the characteristics of metal NCs, we highlight the general working mechanisms by which metal NCs combating the bacterial infections. We also emphasize central roles of core size, element composition, oxidation state, and surface chemistry of metal NCs in their antimicrobial efficacy. Finally, we present a perspective on the remaining challenges and future developments of metal NCs for antibacterial therapeutics. Nanoclusters ; Nanotechnology ; Nanomaterials ; Nanomedicine ; Metal ; Antimicrobial ; Chemistry ; Materials science ; Nanoparticle ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties
Authors: Sanket Kadulkar | Zachary M. Sherman | Venkat Ganesan | Thomas M. Truskett
Source: Annual Review of Chemical and Biomolecular Engineering (review, 2022-03-18)
Field: Materials Science — Materials Chemistry

Machine Learning–Assisted Design of Material Properties Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties. Bottleneck ; Computer science ; Leverage (statistics) ; Machine learning ; Curse of dimensionality ; Artificial intelligence ; Exploit ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Adeesh Kolluru | Muhammed Shuaibi | Aini Palizhati | Nima Shoghi | Abhishek Das | Brandon M. Wood | C. Lawrence Zitnick | John R. Kitchin | Zachary W. Ulissi
Source: ACS Catalysis (article, 2022-07-05)
Field: Materials Science — Materials Chemistry

Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery The development of machine-learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While they are effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst data sets has offered the opportunity to build a universal machine-learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO2 reduction, NH3 production, etc.) without the additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 Data set (OC20) has begun just that, pushing the heterogeneous catalysis and machine-learning communities toward building more accurate and robust models. In this Perspective, we discuss some of the challenges and findings of recent developments on OC20. We examine the performance of current models across different materials and adsorbates to identify notably underperforming subsets. We then discuss some of the modeling efforts surrounding energy conservation, approaches to finding and evaluating the local minima, and augmentation of off-equilibrium data. To complement the community’s ongoing developments, we end with an outlook to some of the important challenges that have yet to be thoroughly explored for large-scale catalyst discovery. Chemical space ; Data science ; Scale (ratio) ; Computer science ; Process (computing) ; Variety (cybernetics) ; Biochemical engineering ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Nanotechnology ; Drug discovery ; Engineering ; Biology ; Bioinformatics ; Materials science ; Physics Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Man Li | Lingyun Dai | Yongjie Hu
Source: ACS Energy Letters (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research. Field (mathematics) ; Computer science ; Thermal conductivity ; Complex system ; Boltzmann equation ; Thermoelectric materials ; Thermal ; Thermoelectric effect ; Data science ; Emissivity ; Transient (computer programming) ; Work (physics) ; Nanotechnology ; Engineering physics ; Mechanical engineering ; Materials science ; Artificial intelligence ; Engineering ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: James M. Thompson | W. Patrick Walters | Jianwen A. Feng | Nicolas A Pabon | Hongcheng Xu | Michael Maser | Brian B Goldman | Demetri Moustakas | Molly Schmidt | Forrest York
Source: Artificial Intelligence in the Life Sciences (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Optimizing active learning for free energy calculations While Relative Binding Free Energy (RBFE) calculations have become a mainstay in lead optimization programs, the computational expense of performing these calculations has limited their broader application. Active learning (AL), a machine learning method used to direct a search iteratively, has explored larger chemical libraries using RBFE calculations. While AL has been successfully applied, there has not been a systematic study of the impact of parameter settings on the performance of AL. To address this gap, we have generated an exhaustive dataset of RBFE calculations on 10,000 congeneric molecules. We used this dataset to explore the impact of several AL design choices, including the number of molecules sampled at each iteration, the method used to select an initial sample, the method used to build a machine learning model, and the acquisition function that defines the balance between exploration and exploitation in the search. Our studies demonstrated that the performance of AL is largely insensitive to the specific machine learning method and acquisition functions used. In our studies, the most significant factor impacting performance was the number of molecules sampled at each iteration where selecting too few molecules hurts performance. Under the best conditions, we were able to identify 75% of the 100 top scoring molecules by sampling only 6% of the dataset. We hope that the dataset of 10K molecules will provide the basis for future studies exploring additional AL strategies. The source code and supporting data for the work are available at https://github.com/google-research/google-research/tree/master/al_for_fep. Computer science ; Machine learning ; Source code ; Tree (set theory) ; Function (biology) ; Sampling (signal processing) ; Code (set theory) ; Artificial intelligence ; Energy (signal processing) ; Data mining ; Sample (material) ; Set (abstract data type) ; Statistics ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Samet Ozturk | Alexander L. Bowler | Ahmed Rady | Nicholas J. Watson
Source: Journal of Food Engineering (article, 2022-10-27)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process In food production environments, the wrong powder material is occasionally loaded onto a production line which impacts food safety, product quality, and production economics. The aim of this study was to assess the potential of using Near Infrared (NIR) spectroscopy combined with Machine Learning to classify food powders under motion conditions. Two NIR sensors with different wavelength ranges were compared and the ML models were tasked with classifying between 25 food powder materials. Eleven different spectra pre-processing methods, three feature selection methods, and five algorithms were investigated to find the optimal ML pipeline. It was found that pre-processing the spectra using autoencoders followed by using support vector machines with the all spectral wavelengths from both sensors was most accurate. The results were improved further using under-sampling and boosting. Overall, this method achieved 99.52, 97.12, 94.08, and 91.68% accuracy for the static, 0.017, 0.036 and 0.068 m s-1 sample speeds. The models were also validated using an independent test sets. Artificial intelligence ; Support vector machine ; Near-infrared spectroscopy ; Feature selection ; Spectroscopy ; Boosting (machine learning) ; Food processing ; Pattern recognition (psychology) ; Process engineering ; Biological system ; Wavelength ; Machine learning ; Computer science ; Materials science ; Chemistry ; Engineering ; Optics ; Food science ; Physics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Qiang Zhu | Shinnosuke Hattori
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

Organic crystal structure prediction and its application to materials design Abstract In recent years, substantial progress has been made in the modeling of organic solids. Computer simulation has been increasingly shaping the area of new organic materials by design. It is possible to discover new organic crystals by computational structure prediction, based on the combination of powerful exploratory algorithms and accurate energy modeling. In this review, we begin with several key early concepts in describing crystal packing, and then introduce the recent state-of-the-art computational techniques for organic crystal structure prediction. Perspectives on the remaining technical challenges, functional materials screening and software development are also discussed in the end. It is reasonable to expect that, in the near future, accurate predictive computational modeling can be accomplished within a time frame that is appreciably shorter than that needed for the laboratory synthesis and characterization. Graphical abstract Frame (networking) ; Characterization (materials science) ; Materials science ; Computer science ; Key (lock) ; Crystal structure prediction ; Software ; Systems engineering ; Crystal structure ; Computational science ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Jesús Sanmartín‐Matalobos | Pilar Bermejo–Barrera | Manuel Aboal‐Somoza | Matilde Fondo | Ana M. Garcı́a-Deibe | Julio Corredoira‐Vázquez | Yeneva Alves-Iglesias
Source: Nanomaterials (review, 2022-07-21)
Field: Materials Science — Materials Chemistry

Semiconductor Quantum Dots as Target Analytes: Properties, Surface Chemistry and Detection Since the discovery of Quantum Dots (QDs) by Alexey I. Ekimov in 1981, the interest of researchers in that particular type of nanomaterials (NMs) with unique optical and electrical properties has been increasing year by year. Thus, since 2009, the number of scientific articles published on this topic has not been less than a thousand a year. The increasing use of QDs due to their biomedical, pharmaceutical, biological, photovoltaics or computing applications, as well as many other high-tech uses such as for displays and solid-state lighting (SSL), has given rise to a considerable number of studies about its potential toxicity. However, there are a really low number of reported studies on the detection and quantification of QDs, and these include ICP-MS and electrochemical analysis, which are the most common quantification techniques employed for this purpose. The knowledge of chemical phenomena occurring on the surface of QDs is crucial for understanding the interactions of QDs with species dissolved in the dispersion medium, while it paves the way for a widespread use of chemosensors to facilitate its detection. Keeping in mind both human health and environmental risks of QDs as well as the scarcity of analytical techniques and methodological approaches for their detection, the adaptation of existing techniques and methods used with other NMs appears necessary. In order to provide a multidisciplinary perspective on QD detection, this review focused on three interrelated key aspects of QDs: properties, surface chemistry and detection. Nanotechnology ; Quantum dot ; Human health ; Nanomaterials ; Computer science ; Materials science Quantum Dots Synthesis And Properties ; Advanced biosensing and bioanalysis techniques ; Carbon and Quantum Dots Applications
Authors: Swee Leong Sing
Source: International Journal of Bioprinting (article, 2022-01-12)
Field: Materials Science — Materials Chemistry

Perspectives on Additive Manufacturing Enabled BetaTitanium Alloys for Biomedical Applications "Stress shielding" caused by the mismatch of modulus between the implant and natural bones, is one of the major problems faced by current commercially used biomedical materials. Beta-titanium (β-Ti) alloys are a class of materials that have received increased interest in the biomedical field due to their relatively low elastic modulus and excellent biocompatibility. Due to their lower modulus, β-Ti alloys have the potential to reduce "stress shielding." Powder bed fusion (PBF), a category of additive manufacturing, or more commonly known as 3D printing techniques, has been used to process β-Ti alloys. In this perspective article, the emerging research of PBF of β-Ti alloys is covered. The potential and limitations of using PBF for these materials in biomedical applications are also elucidated with focus on the perspectives from processes, materials, and designs. Finally, future trends and potential research topics are highlighted. Materials science ; Biocompatibility ; Stress shielding ; Elastic modulus ; Titanium alloy ; Modulus ; Electromagnetic shielding ; 3D printing ; Nanotechnology ; Composite material ; Metallurgy ; Implant ; Alloy ; Medicine Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Orthopaedic implants and arthroplasty
Authors: Jingru Lu | Sofia Donnecke | Irina Paci | David C. Leitch
Source: Chemical Science (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

A reactivity model for oxidative addition to palladium enables quantitative predictions for catalytic cross-coupling reactions Making accurate, quantitative predictions of chemical reactivity based on molecular structure is an unsolved problem in chemical synthesis, particularly for complex molecules. We report an approach to reactivity prediction for catalytic reactions based on quantitative structure-reactivity models for a key step common to many catalytic mechanisms. We demonstrate this approach with a mechanistically based model for the oxidative addition of (hetero)aryl electrophiles to palladium(0), which is a key step in myriad catalytic processes. This model links simple molecular descriptors to relative rates of oxidative addition for 79 substrates, including chloride, bromide and triflate leaving groups. Because oxidative addition often controls the rate and/or selectivity of palladium-catalyzed reactions, this model can be used to make quantitative predictions about catalytic reaction outcomes. Demonstrated applications include a multivariate linear model for the initial rate of Sonogashira coupling reactions, and successful site-selectivity predictions for Suzuki, Buchwald-Hartwig, and Stille reactions of multihalogenated substrates relevant to the synthesis of pharmaceuticals and natural products. Chemistry ; Sonogashira coupling ; Reactivity (psychology) ; Stille reaction ; Oxidative addition ; Palladium ; Catalysis ; Combinatorial chemistry ; Aryl ; Trifluoromethanesulfonate ; Electrophile ; Coupling reaction ; Reductive elimination ; Selectivity ; Computational chemistry ; Organic chemistry Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions
Authors: Wenhui Du | Rui Peng | Zhonglin He | Ying Dai | Baibiao Huang | Yandong Ma
Source: npj 2D Materials and Applications (article, 2022-02-25)
Field: Materials Science — Materials Chemistry

Anomalous valley Hall effect in antiferromagnetic monolayers Abstract Anomalous valley Hall (AVH) effect is a fundamental transport phenomenon in the field of condensed-matter physics. Usually, the research on AVH effect is mainly focused on 2D lattices with ferromagnetic order. Here, by means of model analysis, we present a general design principle for realizing AVH effect in antiferromagnetic monolayers, which involves the introduction of nonuniform potentials to break of PT symmetry. Using first-principles calculations, we further demonstrate this design principle by stacking antiferromagnetic monolayer MnPSe 3 on ferroelectric monolayer Sc 2 CO 2 and achieve the AVH effect. The AVH effect can be well controlled by modulating the stacking pattern. In addition, by reversing the ferroelectric polarization of Sc 2 CO 2 via electric field, the AVH effect in monolayer MnPSe 3 can be readily switched on or off. The underlying physics are revealed in detail. Our findings open up a new direction of research on exploring AVH effect. Monolayer ; Antiferromagnetism ; Condensed matter physics ; Stacking ; Ferroelectricity ; Ferromagnetism ; Polarization (electrochemistry) ; Hall effect ; Point reflection ; Electric field ; Symmetry (geometry) ; Materials science ; Physics ; Chemistry ; Nanotechnology ; Magnetic field ; Optoelectronics ; Nuclear magnetic resonance ; Mathematics ; Quantum mechanics ; Dielectric ; Geometry 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena