jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dimitris Gkoumas
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

ALMol: Aligned Language-Molecule Translation LLMs through Offline Preference Contrastive Optimisation The field of chemistry and Artificial Intelligence (AI) intersection is an area of active research that aims to accelerate scientific discovery.The integration of large language models (LLMs) with scientific modalities has shown significant promise in this endeavour.However, challenges persist in effectively addressing training efficacy and the out-of-distribution problem, particularly as existing approaches rely on larger models and datasets.In this context, we focus on machine language-molecule translation and deploy a novel training approach called contrastive preference optimisation, which avoids generating translations that are merely adequate but not perfect.To ensure generalisability and mitigate memorisation effects, we conduct experiments using only 10% of the data.Our results demonstrate that our models achieve up to a 32% improvement compared to counterpart models.Finally, we introduce a fine-grained, domain-agnostic evaluation method to assess hallucination in LLMs and promote responsible use. Preference ; Translation (biology) ; Computer science ; Natural language processing ; Machine translation ; Linguistics ; Artificial intelligence ; Chemistry ; Messenger RNA ; Gene ; Mathematics ; Philosophy ; Biochemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Natural Language Processing Techniques
Authors: Zhuangyu Zhao | Nathan A. Rudman | Ivan J. Dmochowski
Source: ACS Omega (article, 2024-06-28)
Field: Chemistry — Organic Chemistry

A Site-Specific Cross-Linker for Visible-Light Control of Proteins There is a need for photochemical tools that allow precise control of protein structure and function with visible light. We focus here on the <i>s</i>-tetrazine moiety, which can be installed at a specific protein site via the reaction between dichlorotetrazine and two adjacent sulfhydryl groups. Tetrazine's compact size enables structural mimicry of native amino acid linkages, such as an intramolecular salt bridge or disulfide bond. In this study, we investigated tetrazine installation in three different proteins, where it was confirmed that the cross-linking reaction is highly efficient in aqueous conditions and site-specific when two cysteines are located proximally: the S-S distance was 4-10 Å. As shown in maltose binding protein, the tetrazine cross-linker can replace an interdomain salt bridge crucial for xenon binding and serve as a visible-light photoswitch to modulate <sup>129</sup>Xe NMR contrast. This work highlights the ease of aqueous tetrazine bioconjugation and its applications for protein photoregulation. Tetrazine ; Moiety ; Bioconjugation ; Chemistry ; Intramolecular force ; Salt bridge ; Photoswitch ; Biophysics ; Combinatorial chemistry ; Photochemistry ; Stereochemistry ; Biochemistry ; Organic chemistry ; Biology Click Chemistry and Applications ; Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research
Authors: Saleem Abdul Fattah Ahmed Al Dajani | David E. Keyes
Source: arXiv (Cornell University) (preprint, 2024-07-29)
Field: Materials Science — Materials Chemistry

Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNN Deep AndersoNN accelerates AI by exploiting the continuum limit as the number of explicit layers in a neural network approaches infinity and can be taken as a single implicit layer, known as a deep equilibrium model. Solving for deep equilibrium model parameters reduces to a nonlinear fixed point iteration problem, enabling the use of vector-to-vector iterative solvers and windowing techniques, such as Anderson extrapolation, for accelerating convergence to the fixed point deep equilibrium. Here we show that Deep AndersoNN achieves up to an order of magnitude of speed-up in training and inference. The method is demonstrated on density functional theory results for industrial applications by constructing artificial life and materials `scientists' capable of classifying drugs as strongly or weakly polar, metal-organic frameworks by pore size, and crystalline materials as metals, semiconductors, and insulators, using graph images of node-neighbor representations transformed from atom-bond networks. Results exhibit accuracy up to 98\% and showcase synergy between Deep AndersoNN and machine learning capabilities of modern computing architectures, such as GPUs, for accelerated computational life and materials science by quickly identifying structure-property relationships. This paves the way for saving up to 90\% of compute required for AI, reducing its carbon footprint by up to 60 gigatons per year by 2030, and scaling above memory limits of explicit neural networks in life and materials science, and beyond. Artificial intelligence ; Computer science Machine Learning in Materials Science
Authors: Daniil A. Boiko | Valentine P. Ananikov
Source: arXiv (Cornell University) (preprint, 2024-06-27)
Field: Chemistry — Spectroscopy

Machine learning meets mass spectrometry: a focused perspective Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation. Perspective (graphical) ; Mass spectrometry ; Computer science ; Data science ; Artificial intelligence ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Jane Catherine Ngila | Philiswa N. Nomngongo
Source: South African Journal of Science (article, 2024-11-06)
Field: Chemistry — Physical and Theoretical Chemistry

Growth of the chemistry discipline over the last 120 years, as documented by the SAJS This article demonstrates how SAJS has played a significant role in validating and documenting scientific breakthroughs in chemistry, among other disciplines.As a central discipline, chemistry has contributed to economic and industrial growth in manufacturing, mining, health, food, water, energy and other industry sectors.The discipline supports the implementation of nearly 12 of the 17 United Nation's Sustainable Development Goals 2030.Chemistry, through research and innovation, will continue to play a significant role in responding to emerging technologies that drive industrial revolution, with artificial intelligence as one of the enablers. Chemistry History and advancements in chemistry
Authors: Eran Agmon
Source: arXiv (Cornell University) (preprint, 2024-08-01)
Field: Chemistry — Physical and Theoretical Chemistry

Foundations of a Compositional Systems Biology Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes to enable integrative multiscale simulations. Whereas traditional models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect these models, asking critical questions about the space between models: What variables should a submodel expose through its interface? How do coupled models connect and translate across scales? How do domain-specific models connect across biological and physical disciplines to drive the synthesis of new knowledge? This approach requires robust software to integrate diverse datasets and submodels, providing researchers with tools to flexibly recombine, iteratively refine, and collaboratively expand their models. This article offers a comprehensive framework to support this vision, including: a conceptual and graphical framework to define interfaces and composition patterns; standardized schemas that facilitate modular data and model assembly; biological templates that integrate detailed submodels that connect molecular processes to the emergence of the cellular interface; and user-friendly software interfaces that empower research communities to construct and improve multiscale models of cellular systems. By addressing these needs, compositional systems biology will foster a unified and scalable approach to understanding complex cellular systems. Cognitive science ; Computer science ; Biology ; Psychology thermodynamics and calorimetric analyses
Authors: M. Hanai | Ryo Ishikawa | Mitsuaki Kawamura | Masato Ohnishi | Norio Takenaka | Keizo NAKAMURA | Daiju Matsumura | Satoshi Fujikawa | Hiroki Sakamoto | Yukinori Ochiai | Tetsuo Okane | Shinichiro Kuroki | Atsuo Yamada | Toyotaro Suzumura | Junichiro Shiomi | Kenjiro Taura | Yoshio Mita | N. Shibata | Yuichi Ikuhara
Source: arXiv (Cornell University) (preprint, 2024-09-08)
Field: Materials Science — Materials Chemistry

ARIM-mdx Data System: Towards a Nationwide Data Platform for Materials Science In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge research. However, existing integrated systems that handle data from these resources have primarily focused just on smaller-scale cross-institutional or single-domain operations. As a result, they often lack the scalability, efficiency, agility, and interdisciplinarity, needed for handling substantial volumes of data from various researchers. In this paper, we introduce ARIM-mdx data system, aiming at a nationwide data platform for materials science in Japan. Currently in its trial phase, the platform has been involving 11 universities and institutes all over Japan, and it is utilized by over 800 researchers from around 140 organizations in academia and industry, being intended to gradually expand its reach. The ARIM-mdx data system, as a pioneering nationwide data platform, has the potential to contribute to the creation of new research communities and accelerate innovations. Data science ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Ankush Biswas | Asmita Nag Sarkar
Source: International Journal of Pharmaceutical Sciences Review and Research (article, 2024-02-01)
Field: Chemistry — Organic Chemistry

Heparin: A Conventional Drug with A New Prototype Heparin, a long-established anticoagulant, has evolved into a versatile therapeutic agent with diverse applications beyond thrombosis management. This review explores the expanding repertoire of heparin's pharmacological activities, delving into its interactions with various proteins. As a highly sulfated glycosaminoglycan, heparin demonstrates efficacy in antineoplastic activities, offering new avenues for cancer treatment. Additionally, recent research highlights its potential in addressing COVID-19, inflammation, infertility, malaria, Alzheimer's disease, and infectious diseases. The article provides insights into the discovery, structure, and bioengineered preparation methods of heparin derivatives, showcasing both established and emerging applications. Challenges in current clinical treatments, including dosage optimization and adverse effects, are discussed. The dynamic landscape of heparin-based therapies emphasizes the need for continued exploration, underscoring exciting opportunities for innovative therapeutic strategies. In conclusion, this concise review illuminates the expanding horizons of heparin's therapeutic potential, emphasizing the ongoing quest for refined applications. As research progresses, heparin and its derivatives stand poised to revolutionize treatment paradigms across a spectrum of medical conditions, promising improved patient outcomes and paving the way for novel therapeutic interventions. Heparin ; Drug ; Computer science ; Business ; Medicine ; Pharmacology ; Internal medicine Synthesis and biological activity
Authors: Z. T. Azamatov | Mira Ruzimovna Bekchanova | Abror B. Bakhromov
Source:  (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

HOLOGRAPHIC PROPERTIES OF CHALCOGENIDE GLASSY SEMICONDUCTOR (CGS) FILMS The article presents the results of correlation analysis of budget and actual cost data reflecting fleet operational expenses groups and fleet performance quality data.All data is converted to uniform arrays where each element indicates the yearly value of vessel's performance.The initial data comes from corporate accounting and management software of world's leading tanker company having a fleet of more than 100 vessels for the period of 5 years.The correlation analysis concludes with unpredicted expenses impact to the quality of management processes and further necessity to allocate such expenses in OPEX to improve on cost analysis logic, provide justified methods of cost optimization and form rational approach toward asset insurance policy. Chalcogenide ; Holography ; Materials science ; Semiconductor ; Chalcogenide glass ; Optoelectronics ; Optics ; Physics Phase-change materials and chalcogenides ; Glass properties and applications ; Solid-state spectroscopy and crystallography
Authors: D.S. Gladkov | Е. С. Лукин | Н. А. Попова
Source:  (article, 2024-12-11)
Field: Materials Science — General Materials Science

RESULTS OF SILICON CARBIDE AND ZIRCONIUM DIBORIDE JOINT MECHANICAL ACTIVATION Silicon carbide ; Zirconium diboride ; Zirconium ; Materials science ; Joint (building) ; Carbide ; Silicon ; Metallurgy ; Structural engineering ; Engineering Material Properties and Applications ; Advanced ceramic materials synthesis ; Advanced Surface Polishing Techniques
Authors: Christophe Bonneville | Nathan Bieberdorf | Arun Hegde | Mark Asta | Habib N. Najm | Laurent Capolungo | Cosmin Safta
Source: arXiv (Cornell University) (preprint, 2024-06-24)
Field: Materials Science — Materials Chemistry

Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For such liquid-metal dealloying (LMD) process, phase field models have been developed. However, the governing equations often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, stiffness in the PDEs requires an extremely small time steps (e.g. $10^{-12}$ or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose U-Shaped Adaptive Fourier Neural Operators (U-AFNO), a machine learning (ML) model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g. the deformation of the liquid-metal interface) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in-spite of the chaotic nature of LMD. Our model reproduces the key micro-structure statistics and QoIs with a level of accuracy on-par with the high-fidelity numerical solver. We also investigate the opportunity of using hybrid simulations, in which we alternate forward leap in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes. Operator (biology) ; Net (polyhedron) ; Fourier transform ; Phase (matter) ; Field (mathematics) ; Artificial neural network ; Computer science ; Physics ; Mathematics ; Artificial intelligence ; Mathematical analysis ; Chemistry ; Pure mathematics Solidification and crystal growth phenomena ; Magnetic Properties and Applications ; Magnetic confinement fusion research
Authors: Ian Jasper Retiro | Crusty Tinio | Marilyn Quimado
Source: Ecosystems and Development Journal (article, 2024-12-12)
Field: Chemistry — Analytical Chemistry

A systematic review of medicinal nickel hyperaccumulator plants in Southeast Asia: A phytochemical perspective Nickel (Ni) hyperaccumulators are unique plants that can accumulate high levels of Ni in their tissues without suffering from toxicity and garner attention due to their phytoremediation and ethnomedicine potentials. This study aimed to provide a list of Ni hyperaccumulators found in Southeast Asia, their geographic distribution, conservation status, phytochemistry, and medicinal properties based on research findings from various literatures and propose conservation measures for potential medical hallmarks. A systematic approach was employed by adhering to the Preferred Reporting Items for Systematic Items and Meta-Analyses (PRISMA) guidelines to gather data from various scientific sources, which resulted in the analysis of 55 Ni hyperaccumulators. The principal component analysis (PCA) was used to identify the correlation between the plant orders and the three components in which the positive and negative correlations exhibit the relationship of plant orders to the presence of medicinal plants, phytochemicals, and biological activities. The PCA revealed that Ericales (0.98), Malpighiales (0.95), and Myrtales (0.92) showed strong associations with medicinal properties and phytochemical diversity. Furthermore, the results indicated that 16 species (29%) possess documented medicinal properties, with the family Phyllanthaceae being particularly well-represented. Key phytochemicals such as alkaloids, flavonoids, and phenolics, known for their antimicrobial, antioxidant, and anti-diabetic properties, were identified. However, 39 species (71%) remained unexamined for medicinal efficacy, highlighting a critical research gap. This highlights the need for further exploration of the pharmaco-medical properties of unstudied species. To safeguard their ecological and medicinal value, conservation efforts need to be prioritized in ultramafic ecosystems, where these plants predominantly thrive. Ni hyperaccumulators hold significant therapeutic potential; therefore, further research is essential to uncover their medicinal applications and broaden their contributions to ethnomedicine and phytochemistry. Hyperaccumulator ; Medicinal plants ; Phytochemical ; Ethnomedicine ; Biodiversity ; Ethnobotany Heavy Metals in Plants ; Ginger and Zingiberaceae research ; Nanoparticles: synthesis and applications
Authors: S.E. Khalchitsky | М.В. Иванов
Source:  (article, 2024-12-11)
Field: Chemistry — Organic Chemistry

FEATURES AND BENEFITS OF THE CARIPRAZINE USE AS A THIRD-GENERATION ANTIPSYCHOTIC IN VARIOUS MENTAL DISORDERS Antipsychotic ; Computer science ; Schizophrenia (object-oriented programming) ; Psychology ; Psychotherapist ; Psychiatry Chemical synthesis and pharmacological studies
Authors: Kevin Linka | Ellen Kuhl
Source: arXiv (Cornell University) (preprint, 2024-04-10)
Field: Materials Science — Materials Chemistry

Best-in-class modeling: A novel strategy to discover constitutive models for soft matter systems The ability to automatically discover interpretable mathematical models from data could forever change how we model soft matter systems. For convex discovery problems with a unique global minimum, model discovery is well-established. It uses a classical top-down approach that first calculates a dense parameter vector, and then sparsifies the vector by gradually removing terms. For non-convex discovery problems with multiple local minima, this strategy is infeasible since the initial parameter vector is generally non-unique. Here we propose a novel bottom-up approach that starts with a sparse single-term vector, and then densifies the vector by systematically adding terms. Along the way, we discover models of gradually increasing complexity, a strategy that we call best-in-class modeling. To identify successful candidate terms, we reverse-engineer a library of sixteen functional building blocks that integrate a century of knowledge in material modeling with recent trends in machine learning and artificial intelligence. Yet, instead of solving the discrete combinatorial problem with 65,536 possible combinations of terms, best-in-class modeling starts with the best one-term model and iteratively repeats adding terms, until the objective function meets a user-defined convergence criterion. Strikingly, we achieve good convergence with only one or two terms. We illustrate the best-in-class one- and two-term models for a variety of soft matter systems including rubber, brain, artificial meat, skin, and arteries. Our discovered models display distinct and unexpected features for each family of materials, and suggest that best-in-class modeling is an efficient, robust, and easy-to-use strategy to discover the mechanical signatures of traditional and unconventional soft materials. We anticipate that our technology will generalize naturally to other classes of natural and man-made soft matter. Computer science ; Class (philosophy) ; Maxima and minima ; Convergence (economics) ; Term (time) ; Function (biology) ; Artificial intelligence ; Regular polygon ; Theoretical computer science ; Mathematical optimization ; Mathematics Machine Learning in Materials Science