jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruimin Ma | Hanfeng Zhang | Tengfei Luo
Source: ACS Applied Materials & Interfaces (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.400 W/m·K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all generated polymers with thermal conductivity ≥0.400 W/m·K, we have evaluated their synthesizability by calculating the synthetic accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has an MD-calculated thermal conductivity of 0.693 W/m·K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other material development tasks for different applications. Thermal conductivity ; Materials science ; Polymer ; Amorphous solid ; Conductivity ; Reinforcement learning ; Leverage (statistics) ; Thermal ; Composite material ; Computer science ; Machine learning ; Thermodynamics ; Organic chemistry ; Physics ; Physical chemistry ; Chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Yuanqing Wang | Josh Fass | Benjamin Kaminow | John E. Herr | Dominic A. Rufa | Ivy Zhang | Iván Pulido | Michael M. Henry | Hannah E. Bruce Macdonald | Kenichiro Takaba | John D. Chodera
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

End-to-end differentiable construction of molecular mechanics force fields Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy <i>vis-á-vis</i> experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma. Differentiable function ; Embedding ; Molecular mechanics ; Force field (fiction) ; Scheme (mathematics) ; Computer science ; Classical mechanics ; Physics ; Mathematics ; Molecular dynamics ; Pure mathematics ; Artificial intelligence ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Tanja Bien | Krischan Koerfer | Jan Schwenzfeier | Klaus Dreisewerd | Jens Soltwisch
Source: Proceedings of the National Academy of Sciences (article, 2022-07-11)
Field: Chemistry — Spectroscopy

Mass spectrometry imaging to explore molecular heterogeneity in cell culture Molecular analysis on the single-cell level represents a rapidly growing field in the life sciences. While bulk analysis from a pool of cells provides a general molecular profile, it is blind to heterogeneities between individual cells. This heterogeneity, however, is an inherent property of every cell population. Its analysis is fundamental to understanding the development, function, and role of specific cells of the same genotype that display different phenotypical properties. Single-cell mass spectrometry (MS) aims to provide broad molecular information for a significantly large number of cells to help decipher cellular heterogeneity using statistical analysis. Here, we present a sensitive approach to single-cell MS based on high-resolution MALDI-2-MS imaging in combination with MALDI-compatible staining and use of optical microscopy. Our approach allowed analyzing large amounts of unperturbed cells directly from the growth chamber. Confident coregistration of both modalities enabled a reliable compilation of single-cell mass spectra and a straightforward inclusion of optical as well as mass spectrometric features in the interpretation of data. The resulting multimodal datasets permit the use of various statistical methods like machine learning-driven classification and multivariate analysis based on molecular profile and establish a direct connection of MS data with microscopy information of individual cells. Displaying data in the form of histograms for individual signal intensities helps to investigate heterogeneous expression of specific lipids within the cell culture and to identify subpopulations intuitively. Ultimately, t-MALDI-2-MSI measurements at 2-µm pixel sizes deliver a glimpse of intracellular lipid distributions and reveal molecular profiles for subcellular domains. Biological system ; Population ; Mass spectrometry imaging ; Computational biology ; Single-cell analysis ; Mass spectrometry ; MALDI imaging ; Chemistry ; Cell ; Computer science ; Biology ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics ; Cell Image Analysis Techniques
Authors: Francesco Lavini | Martin Rejhon | Elisa Riedo
Source: Nature Reviews Materials (review, 2022-06-27)
Field: Materials Science — Materials Chemistry

Two-dimensional diamonds from sp2-to-sp3 phase transitions Phosphorene ; Diamond ; Graphene ; Materials science ; Phase transition ; Nanotechnology ; MXenes ; Boron nitride ; Chemical physics ; Phase (matter) ; Hexagonal boron nitride ; Surface modification ; Condensed matter physics ; Chemistry ; Physics ; Physical chemistry ; Composite material MXene and MAX Phase Materials ; Graphene research and applications ; 2D Materials and Applications
Authors: Riley J. Hickman | Matteo Aldeghi | Florian Häse | Alán Aspuru-Guzik
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Bayesian optimization with known experimental and design constraints for chemistry applications A Bayesian optimization algorithm that satisfies known constraints has been developed. The usefulness of considering experimental and design constraints are shown in two simulated chemistry applications. Computer science ; Chemical space ; Bayesian optimization ; Robustness (evolution) ; Flexibility (engineering) ; A priori and a posteriori ; Mathematical optimization ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Michael Tuck | Florent Grélard | Landry Blanc | Nicolas Desbenoît
Source: Frontiers in Chemistry (review, 2022-05-09)
Field: Chemistry — Spectroscopy

MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI. Mass spectrometry imaging ; Focus (optics) ; Computer science ; MALDI imaging ; Modality (human–computer interaction) ; Modalities ; Artificial intelligence ; Mass spectrometry ; Human–computer interaction ; Matrix-assisted laser desorption/ionization ; Chemistry ; Desorption ; Physics Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Thomas E. Gartner | Kelly M. Hunter | Eleftherios Lambros | Alessandro Caruso | Marc Riera | Gregory R. Medders | Athanassios Z. Panagiotopoulos | Pablo G. Debenedetti | Francesco Paesani
Source: The Journal of Physical Chemistry Letters (article, 2022-04-18)
Field: Materials Science — Materials Chemistry

Anomalies and Local Structure of Liquid Water from Boiling to the Supercooled Regime as Predicted by the Many-Body MB-pol Model For the past 50 years, researchers have sought molecular models that can accurately reproduce water's microscopic structure and thermophysical properties across broad ranges of its complex phase diagram. Herein, molecular dynamics simulations with the many-body MB-pol model are performed to monitor the thermodynamic response functions and local structure of liquid water from the boiling point down to deeply supercooled temperatures at ambient pressure. The isothermal compressibility and isobaric heat capacity show maxima near 223 K, in excellent agreement with recent experiments, and the liquid density exhibits a minimum at ∼208 K. A local tetrahedral arrangement, where each water molecule accepts and donates two hydrogen bonds, is found to be the most probable hydrogen-bonding topology at all temperatures. This work suggests that MB-pol may provide predictive capability for studies of liquid water's physical properties across broad ranges of thermodynamic states, including the so-called water's "no man's land" which is difficult to probe experimentally. Supercooling ; Thermodynamics ; Heat capacity ; Phase diagram ; Water model ; Isobaric process ; Hydrogen bond ; Liquid water ; Compressibility ; Molecular dynamics ; Work (physics) ; Boiling ; Isothermal process ; Chemistry ; Properties of water ; Materials science ; Molecule ; Phase (matter) ; Computational chemistry ; Physics ; Organic chemistry Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Irum Shahzadi | Ameer Fawad Zahoor | Burak Tüzün | Asim Mansha | Muhammad Naveed Anjum | Azhar Rasul | Ali Irfan | Katarzyna Kotwica-Mojzych | Mariusz Mojzych
Source: PLoS ONE (article, 2022-12-15)
Field: Chemistry — Organic Chemistry

Repositioning of acefylline as anti-cancer drug: Synthesis, anticancer and computational studies of azomethines derived from acefylline tethered 4-amino-3-mercapto-1,2,4-triazole Novel azomethines derived from acefylline tethered triazole hybrids (7a-k) have been synthesized and evaluated against human liver cancer cell line (Hep G2) using MTT assay. The synthesized series of azomethines exhibited promising efficacy against liver cancer cell line. Screening of the synthesized series identified compound 7d with the least cell viability value (11.71 ± 0.39%) as the most potent anticancer agent in contrast to the reference drug acefylline (cell viability = 80 ± 3.87%). In this study, the potentials of the novel agents (7a-k) to inhibit liver cancer proteins were assessed. Subsequently, the structure-activity relationship of the potential drug candidates was assessed via ADME/T molecular screening. The cytotoxic potential of these derivatives was also investigated by hemolysis and thrombolysis. Their hemolytic and thrombolytic studies showed that all of these drugs had very low cytotoxicity and moderate clot lysis activity. Compound 7g (0.26% hemolysis) and 7k (52.1% clot lysis) were the least toxic and moderate thrombolytic agents respectively. Hemolysis ; Cytotoxicity ; ADME ; Viability assay ; MTT assay ; Pharmacology ; Drug ; Chemistry ; Cancer ; Cytolysis ; Cancer cell ; Biochemistry ; Cell ; In vitro ; Biology ; Medicine ; Immunology ; Internal medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Enzyme function and inhibition
Authors: Thomas R. Roose | Daniël S. Verdoorn | Pieter Mampuys | Eelco Ruijter | Bert U. W. Maes | Romano V. A. Orrù
Source: Chemical Society Reviews (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Transition metal-catalysed carbene- and nitrene transfer to carbon monoxide and isocyanides Transition metal-catalysed carbene- and nitrene transfer to the C1-building blocks carbon monoxide and isocyanides provides heteroallenes (<i>i.e.</i> ketenes, isocyanates, ketenimines and carbodiimides). These are versatile and reactive compounds allowing <i>in situ</i> transformation towards numerous functional groups and organic compounds, including heterocycles. Both one-pot and tandem processes have been developed providing valuable synthetic methods for the organic chemistry toolbox. This review discusses all known transition metal-catalysed carbene- and nitrene transfer reactions towards carbon monoxide and isocyanides and <i>in situ</i> transformation of the heteroallenes hereby obtained, with a special focus on the general mechanistic considerations. Nitrene ; Carbene ; Chemistry ; Carbon monoxide ; Transition metal ; Combinatorial chemistry ; Photochemistry ; Organic chemistry ; Catalysis Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Xiaolan Tian | Siwei Song | Fang Chen | Xiujuan Qi | Yi Wang | Qinghua Zhang
Source: Energetic Materials Frontiers (article, 2022-08-18)
Field: Materials Science — Materials Chemistry

Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives Predicting chemical properties is one of the most important applications of machine learning. In recent years, the prediction of the properties of energetic materials using machine learning has been receiving more attention. This review summarized recent advances in predicting energetic compounds’ properties (e.g., density, detonation velocity, enthalpy of formation, sensitivity, the heat of the explosion, and decomposition temperature) using machine learning. Moreover, it presented general steps for applying machine learning to the prediction of practical chemical properties from the aspects of data, molecular representation, algorithms, and general accuracy. Additionally, it raised some controversies specific to machine learning in energetic materials and its possible development directions. Machine learning is expected to become a new power for driving the development of energetic materials soon. Property (philosophy) ; Computer science ; Artificial intelligence ; Machine learning ; Epistemology ; Philosophy Machine Learning in Materials Science ; Energetic Materials and Combustion ; Thermal and Kinetic Analysis
Authors: Meng Xu | Ming Xu | Xiangshui Miao
Source: InfoMat (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration Abstract The recent development of three‐dimensional semiconductor integration technology demands a key component—the ovonic threshold switching (OTS) selector to suppress the current leakage in the high‐density memory chips. Yet, the unsatisfactory performance of existing OTS materials becomes the bottleneck of the industrial advancement. The sluggish development of OTS materials, which are usually made from chalcogenide glass, should be largely attributed to the insufficient understanding of the electronic structure in these materials, despite of intensive research in the past decade. Due to the heavy first‐principles computation on disordered systems, a universal theory to explain the origin of mid‐gap states (MGS), which are the key feature leading to the OTS behavior, is still lacking. To avoid the formidable computational tasks, we adopt machine learning method to understand and predict MGS in typical OTS materials. We build hundreds of chalcogenide glass models and collect major structural features from both short‐range order (SRO) and medium‐range order (MRO) of the amorphous cells. After training the artificial neural network using these features, the accuracy has reached ~95% when it recognizes MGS in new glass. By analyzing the synaptic weights of the input structural features, we discover that the bonding and coordination environments from SRO and particularly MRO are closely related to MGS. The trained model could be used in many other OTS chalcogenides after minor modification. The intelligent machine learning allows us to understand the OTS mechanism from vast amount of structural data without heavy computational tasks, providing a new strategy to design functional amorphous materials from first principles. image Bottleneck ; Chalcogenide ; Computer science ; Artificial intelligence ; Amorphous solid ; Deep learning ; Artificial neural network ; Nanotechnology ; Materials science ; Optoelectronics ; Embedded system ; Chemistry Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Advanced Memory and Neural Computing
Authors: Florence H. Vermeire | Yunsie Chung | William H. Green
Source: Journal of the American Chemical Society (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures The solubility of organic molecules is crucial in organic synthesis and industrial chemistry; it is important in the design of many phase separation and purification units, and it controls the migration of many species into the environment. To decide which solvents and temperatures can be used in the design of new processes, trial and error is often used, as the choice is restricted by unknown solid solubility limits. Here, we present a fast and convenient computational method for estimating the solubility of solid neutral organic molecules in water and many organic solvents for a broad range of temperatures. The model is developed by combining fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solid solubility at 298 K. We provide free open-source and online tools for the prediction of solid solubility limits and a curated data collection (SolProp) that includes more than 5000 experimental solid solubility values for validation of the model. The model predictions are accurate for aqueous systems and for a huge range of organic solvents up to 550 K or higher. Methods to further improve solid solubility predictions by providing experimental data on the solute of interest in another solvent, or on the solute's sublimation enthalpy, are also presented. Solubility ; Solvation ; Chemistry ; Enthalpy ; Thermodynamics ; Solvent ; Aqueous solution ; Sublimation (psychology) ; Molecule ; Hildebrand solubility parameter ; Enthalpy change of solution ; Organic chemistry Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Process Optimization and Integration
Authors: Md. Shamim Hussain | Mohammed J. Zaki | Dharmashankar Subramanian
Source: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

Global Self-Attention as a Replacement for Graph Convolution We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called edge channels. The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data. Our model exclusively uses global self-attention as an aggregation mechanism rather than static localized convolutional aggregation. This allows for unconstrained long-range dynamic interactions between nodes. Moreover, the edge channels allow the structural information to evolve from layer to layer, and prediction tasks on edges/links can be performed directly from the output embeddings of these channels. We verify the performance of EGT in a wide range of graph-learning experiments on benchmark datasets, in which it outperforms Convolutional/Message-Passing Graph Neural Networks. EGT sets a new state-of-the-art for the quantum-chemical regression task on the OGB-LSC PCQM4Mv2 dataset containing 3.8 million molecular graphs. Our findings indicate that global self-attention based aggregation can serve as a flexible, adaptive and effective replacement of graph convolution for general-purpose graph learning. Therefore, convolutional local neighborhood aggregation is not an essential inductive bias. Computer science ; Theoretical computer science ; Graph ; Inductive bias ; Pairwise comparison ; Convolutional neural network ; Feature learning ; Deep learning ; Artificial intelligence ; Multi-task learning ; Task (project management) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sepideh Khaki Firooz | Daniel W. Armstrong
Source: Analytica Chimica Acta (review, 2022-08-05)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks in separations: A review Chemistry ; Metal-organic framework ; Adsorption ; Membrane ; Solvent ; Gas separation ; Nanotechnology ; Organic chemistry ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Covalent Organic Framework Applications