jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Caroline McCue | Henri-Louis Girard | Kripa K. Varanasi
Source: ACS Applied Materials & Interfaces (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Enhancing Protein Crystal Nucleation Using In Situ Templating on Bioconjugate-Functionalized Nanoparticles and Machine Learning Although protein crystallization offers a promising alternative to chromatography for lower-cost protein purification, slow nucleation kinetics and high protein concentration requirements are major barriers for using crystallization as a viable strategy in downstream protein purification. Here, we demonstrate that nanoparticles functionalized with bioconjugates can result in an in situ template for inducing rapid crystallization of proteins at low protein concentration conditions. We use a microbatch crystallization setup to show that the range of successful crystallization conditions is expanded by the presence of functionalized nanoparticles. Furthermore, we use a custom machine learning-enabled emulsion crystallization setup to rigorously quantify nucleation parameters. We show that bioconjugate-functionalized nanoparticles can result in up to a 7-fold decrease in the induction time and a 3-fold increase in the nucleation rate of model proteins compared to those in control environments. We thus provide foundational insight that could enable crystallization to be used in protein manufacturing by reducing both the protein concentration and the time required to nucleate protein crystals. Crystallization ; Nucleation ; Protein crystallization ; Bioconjugation ; Materials science ; Nanoparticle ; Chemical engineering ; In situ ; Emulsion ; Nanotechnology ; Chemistry ; Organic chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Protein purification and stability
Authors: Martina Crippa | Annalisa Cardellini | Matteo Cioni | Gábor Cśanyi | Giovanni M. Pavan
Source: Machine Learning Science and Technology (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Machine learning of microscopic structure-dynamics relationships in complex molecular systems Abstract In many complex molecular systems, the macroscopic ensemble’s properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic-level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g. Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g. Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g. LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales. Dynamical systems theory ; Statistical physics ; Complex system ; Computer science ; Molecular machine ; Biological system ; Molecular dynamics ; Artificial intelligence ; Complex dynamics ; Local structure ; Physics ; Nanotechnology ; Chemical physics ; Mathematics ; Materials science ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Elliot Chang | Mavrik Zavarin | Linda Beverly | Haruko Wainwright
Source: Applied Geochemistry (article, 2023-06-26)
Field: Chemistry — Inorganic Chemistry

A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces Historically, surface complexation model (SCM) constants and distribution coefficients (Kd) have been employed to quantify mineral-based retardation effects controlling the fate of metals in subsurface geologic systems. Our recent SCM development workflow, based on the Lawrence Livermore National Laboratory Surface Complexation/Ion Exchange (L-SCIE) database, illustrated a community FAIR data approach to SCM development by predicting uranium(VI)-quartz adsorption for a large number of literature-mined data. Here, we present an alternative hybrid machine learning (ML) approach that shows promise in achieving equivalent high-quality predictions compared to traditional surface complexation models. At its core, the hybrid random forest (RF) ML approach is motivated by the proliferation of incongruent SCMs in the literature that limit their applicability in reactive transport models. Our hybrid ML approach implements PHREEQC-based aqueous speciation calculations; values from these simulations are automatically used as input features for a random forest (RF) algorithm to quantify adsorption and avoid SCM modeling constraints entirely. Named the LLNL Speciation Updated Random Forest (L-SURF) model, this hybrid approach is shown to have applicability to U(VI) sorption cases driven by both ion-exchange and surface complexation, as is shown for quartz and montmorillonite cases. The approach can be applied to reactive transport modeling and may provide an alternative to the costly development of self-consistent SCM reaction databases. Adsorption ; Sorption ; Chemistry ; Workflow ; Ion exchange ; Computer science ; Process engineering ; Ion ; Database ; Physical chemistry ; Engineering Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping ; Groundwater flow and contamination studies
Authors: Xiao Yang | Qiuhong Huang | Lulu Zhang | Lifeng Li | Yu Chen | Wei Wang | Hong Liang | Yufang Wu | Zheng He | Yue Zhao | Zhiwei Qiao
Source: Applied Materials Today (article, 2023-01-28)
Field: Chemistry — Inorganic Chemistry

Computational screening and machine learning of hydrophobic metal-organic frameworks for removal of chemical warfare agents from air Chemical Warfare Agents ; Adsorption ; Metal-organic framework ; Chemical warfare ; Computer science ; Chemistry ; Artificial intelligence ; Engineering ; Biochemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Sulfur Compounds in Biology ; Energetic Materials and Combustion
Authors: Jiajun Zhou | Austin M. Mroz | Kim E. Jelfs
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Deep generative design of porous organic cages <i>via</i> a variational autoencoder Porous organic cages (POCs) are a class of porous molecular materials characterised by their tunable, intrinsic porosity; this functional property makes them candidates for applications including guest storage and separation. Typically formed <i>via</i> dynamic covalent chemistry reactions from multifunctionalised molecular precursors, there is an enormous potential chemical space for POCs due to the fact they can be formed by combining two relatively small organic molecules, which themselves have an enormous chemical space. However, identifying suitable molecular precursors for POC formation is challenging, as POCs often lack shape persistence (the cage collapses upon solvent removal with loss of its cavity), thus losing a key functional property (porosity). Generative machine learning models have potential for targeted computational design of large functional molecular systems such as POCs. Here, we present a deep-learning-enabled generative model, Cage-VAE, for the targeted generation of shape-persistent POCs. We demonstrate the capacity of Cage-VAE to propose novel, shape-persistent POCs, <i>via</i> integration with multiple efficient sampling methods, including Bayesian optimisation and spherical linear interpolation. Autoencoder ; Porosity ; Porous medium ; Materials science ; Property (philosophy) ; Class (philosophy) ; Chemical engineering ; Nanotechnology ; Computer science ; Deep learning ; Artificial intelligence ; Composite material ; Engineering Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Elizaveta Kalika | N. V. Bondarev | Konstantin P. Katin | A. I. Kochaev | Anastasiya A. Grekova | Savaş Kaya | Yusupbek A. Bauetdinov | Mikhail M. Maslov
Source: Journal of Molecular Liquids (article, 2023-03-05)
Field: Chemistry — Organic Chemistry

Adsorption of 40 low molecular weight drugs on pristine and fluorinated C60 fullerenes: Ab initio, statistical and neural networks analysis Fullerene ; Adsorption ; Binding energy ; Computational chemistry ; Density functional theory ; Molecule ; Molecular orbital ; Ab initio ; Materials science ; Dipole ; Ab initio quantum chemistry methods ; Chemistry ; Physical chemistry ; Chemical physics ; Atomic physics ; Organic chemistry ; Physics Fullerene Chemistry and Applications ; Boron and Carbon Nanomaterials Research ; Advanced Chemical Physics Studies
Authors: Sourav Mal | Prasenjit Sen
Source: Journal of Magnetism and Magnetic Materials (article, 2023-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Leveraging available data for efficient exploration of materials space using Machine Learning: A case study for identifying rare earth-free permanent magnets Magnet ; Anisotropy ; Materials science ; Computer science ; Magnetization ; Artificial neural network ; Saturation (graph theory) ; Space (punctuation) ; Magnetic anisotropy ; Rare earth ; Stability (learning theory) ; Statistical physics ; Machine learning ; Biological system ; Mechanical engineering ; Magnetic field ; Physics ; Optics Magnetic Properties of Alloys ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Gang Xiao | Xiaolei Zhang | Guisheng Yang | Yanlong Jia | Gen Yan | Renhua Wu
Source: NMR in Biomedicine (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Deep learning to reconstruct quasi‐steady‐state chemical exchange saturation transfer from a non‐steady‐state experiment The insufficiently long RF saturation duration and relaxation delay in chemical exchange saturation transfer (CEST)-MRI experiments may result in underestimation of CEST measurements. To maintain the CEST effect without prolonging the saturation duration and reach quasi-steady state (QUASS), a deep learning method was developed to reconstruct a QUASS CEST image pixel by pixel from non-steady-state CEST acquired in experiments. In this work, we established a tumor-bearing rat model on a 7 T horizontal bore small-animal MRI scanner, allowing ground-truth generation, after which a bidirectional long short-term memory network was formulated and trained on simulated CEST Z-spectra to reconstruct the QUASS CEST; finally, the ground truth yielded by experiments was used to evaluate the performance of the reconstruction model by comparing the estimates with the ground truth. For quantitation evaluation, linear regression analysis, structural similarity index (SSIM) and peak signal-to-noise ratio (peak SNR) were used to assess the proposed model in the QUASS CEST reconstruction. In the linear regression analysis of in vivo data, the coefficient of determination for six different representative frequency offsets was at least R<sup>2</sup> = 0.9521. Using the SSIM and peak SNR as evaluation metrics, the reconstruction accuracies of in vivo QUASS CEST were found to be 0.9991 and 46.7076, respectively. Experimental results demonstrate that the proposed model provides a robust and accurate solution for QUASS CEST reconstruction using a deep learning mechanism. Steady state (chemistry) ; Saturation (graph theory) ; Steady State theory ; Transfer of learning ; State (computer science) ; Thermodynamics ; Nuclear magnetic resonance ; Chemistry ; Physics ; Computer science ; Artificial intelligence ; Mathematics ; Physical chemistry ; Algorithm Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Ana P. Arcanjo | Denisson O. Liborio | Santiago Arias | Florival Rodrigues de Carvalho | Josivan Pedro da Silva | Bernardo Dias Ribeiro | Marcos L. Dias | Aline Machado de Castro | R. Fréty | C. M. B. M. Barbosa | José Geraldo A. Pacheco
Source: Polymers (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Chemical Recycling of PET Using Catalysts from Layered Double Hydroxides: Effect of Synthesis Method and Mg-Fe Biocompatible Metals The chemical recycling of poly(ethylene terephthalate) (PET) residues was performed via glycolysis with ethylene glycol (EG) over Mg-Fe and Mg-Al oxide catalysts derived from layered double hydroxides. Catalysts prepared using the high supersaturation method (h.s.c.) presented a higher surface area and larger particles, but this represented less PET conversion than those prepared by the low supersaturation method (l.s.c.). This difference was attributed to the smaller mass transfer limitations inside the (l.s.c.) catalysts. An artificial neural network model well fitted the PET conversion and bis(2-hydroxyethyl) terephthalate (BHET) yield. The influence of Fe in place of Al resulted in a higher PET conversion of the Mg-Fe-h.s.c. catalyst (~95.8%) than of Mg-Al-h.s.c. (~63%). Mg-Fe catalysts could be reused four to five times with final conversions of up to 97% with reaction conditions of EG: PET = 5:1 and catalyst: PET = 0.5%. These results confirm the Mg-Fe oxides as a biocompatible novel catalyst for the chemical recycling of PET residues to obtain non-toxic BHET for further polymerization, and use in food and beverage packaging. Catalysis ; Ethylene glycol ; Yield (engineering) ; Materials science ; Layered double hydroxides ; Polymerization ; Oxide ; Supersaturation ; Ethylene oxide ; Ethylene ; Nuclear chemistry ; Chemistry ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Polymer ; Composite material ; Metallurgy ; Copolymer Layered Double Hydroxides Synthesis and Applications ; biodegradable polymer synthesis and properties ; Supercapacitor Materials and Fabrication
Authors: Seong-Min Kim | Juhwan Noh | Geun Ho Gu | Shuan Chen | Yousung Jung
Source: Chemical Science (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation While advances in computational techniques have accelerated virtual materials design, the actual synthesis of predicted candidate materials is still an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the priority of predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for the top-<i>k</i> exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict synthetic precursors for the materials synthesized after 2016. The high correlation between the probability score and prediction accuracy suggests that the probability score can be interpreted as a measure of confidence levels, which can offer the priority of the predictions. Materials science ; Computer science Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; X-ray Diffraction in Crystallography
Authors: Lin Wang | Zhengda He | Bin Ouyang
Source: Computational Materials Science (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

Data driven design of compositionally complex energy materials Computer science ; Curse of dimensionality ; Electrochemical energy storage ; Field (mathematics) ; Range (aeronautics) ; Energy transformation ; Nanotechnology ; Artificial intelligence ; Materials science ; Supercapacitor ; Physics ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Antti Pihlajamäki | Sami Malola | Tommi Kärkkäinen | Hannu Häkkinen
Source: The Journal of Physical Chemistry C (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts Understanding hydrogen adsorption on metal nanoparticles is a key prerequisite for designing efficient electrocatalysts for water splitting and the hydrogen evolution reaction. However, this seemingly simple elementary reaction step is affected by several factors arising from the chemical environment at the catalyst, and deciphering the most important contributions to optimal interactions requires numerically heavy electronic structure calculations. Here, we combine graph-based representations of the local atomic environment of hydrogen in copper- and palladium-doped 25-atom gold nanoparticles with several kernel-based machine learning (ML) methods to predict the interaction energy between hydrogen and the nanoparticle catalyst. We demonstrate that simple distance-based kernel models are able to predict the interaction energy within 0.1 eV when trained by reference data from state-of-the-art density functional theory calculations. Analyzing the model performance with respect to attributes of the hydrogen node highlights the locality of hydrogen adsorption. This implies the viability of combining graphs with kernel-based ML models for studying hydrogen chemisorption in complex environment data efficiently. Nanoparticle ; Hydrogen ; Catalysis ; Chemisorption ; Kernel (algebra) ; Adsorption ; Density functional theory ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanocluster Synthesis and Applications
Authors: Biswajyoti Das | P. S. Robi | Pinakeswar Mahanta
Source: Fuel (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Experimental Investigation and Modelling by Machine Learning Techniques for Hydrogen Generation by Reacting Aluminium with Aqueous NaOH Solution Hydrogen ; Combustion ; Artificial neural network ; Correlation coefficient ; Approximation error ; Aqueous solution ; Linear regression ; Chemistry ; Hydrogen production ; Aluminium ; Thermodynamics ; Biological system ; Computer science ; Machine learning ; Algorithm ; Physical chemistry ; Organic chemistry ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Industrial Gas Emission Control
Authors: Joshua Hinz | Valentin V. Karasiev | S. X. Hu | Deyan Mihaylov
Source: Physical Review Materials (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter In this paper $\mathrm{\ensuremath{\Delta}}$ learning is used to map orbital-free density functional theory (DFT) ionic forces to the corresponding Kohn-Sham (KS) DFT ionic forces. The development of the approximate force difference in terms of the ion positions is constructed and serves as a stand-in for the ground truth force difference. Descriptor vectors for ion configurations are constructed using all distances between ions in conjunction with an indexing based on a nearest neighbor ranking. It is demonstrated that such a scheme of descriptors can uniquely describe an ionic configuration up to a rotation and reflection when no ambiguity in the nearest neighbor ranking exists. How to handle the case when an ambiguity exists in the nearest neighbor ranking is discussed. As a proof of principle, the model is trained and tested on warm dense hydrogen at temperatures between 1 and 15 eV. Once tested, the model was used to perform molecular dynamic simulations of warm dense hydrogen. The resulting energies and pressures are within 1 and 2% of their respective target KS values. Ranking (information retrieval) ; k-nearest neighbors algorithm ; Ion ; Ambiguity ; Ionic bonding ; Density functional theory ; Molecular dynamics ; Materials science ; Statistical physics ; Physics ; Computer science ; Algorithm ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Chang Liu | Koichi Kitahara | Asuka Ishikawa | Takanobu Hiroto | Alok Singh | Erina Fujita | Yukari Katsura | Yuki Inada | Ryuji Tamura | Kaoru Kimura | Ryo Yoshida
Source: Physical Review Materials (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Quasicrystals predicted and discovered by machine learning Quasicrystals represent a class of ordered materials that have diffraction symmetry forbidden in periodic crystals. Since the first discovery of quasicrystals in 1984, approximately 100 thermodynamically stable quasicrystals have been synthesized. The discovery of new quasicrystals has led to the observation of novel physical phenomena, such as robust quantum criticality, fractal superconductivity, and peculiar long-range magnetic ordering. However, the pace of discovery of new quasicrystals has significantly slowed down, which is attributed to the lack of design principles for exploring new quasicrystals. Here, we demonstrate that machine learning can greatly accelerate the process of material discovery. Our model can predict stable quasicrystalline phases with high accuracy. With this model, we discovered three stable decagonal quasicrystals through an exhaustive screening of more than 1000 ternary aluminum alloy systems. Quasicrystal ; Materials science ; Condensed matter physics ; Superconductivity ; Ternary operation ; Diffraction ; Physics ; Computer science ; Quantum mechanics Quasicrystal Structures and Properties ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography