jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pedro da Fonseca Soares Rodrigues | Paulo Fernando Guimarães Morando Marzocchi Tierno | Gustavo Augusto Couto Carvalho | Beneficência Portuguesa São Paulo | João Marcos Moreira Guimarães Santos
Source: Path of Science (article, 2023-09-30)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymer-Based Electrochemical Sensing in Protein Detection Protein detection is paramount across various scientific, clinical, and industrial domains. Accurate and sensitive detection of proteins is pivotal for understanding biological processes, diagnosing diseases, drug development, environmental monitoring, and ensuring food safety. Traditional protein detection methods encounter sensitivity, specificity, and ease of use challenges. Molecularly imprinted polymers (MIPs), with their tailored molecular recognition sites, offer a novel approach to address these limitations. When combined with electrochemical techniques, MIP-based electrochemical methods have emerged as a revolutionary technology, showcasing enhanced sensitivity and selectivity. This article provides a comprehensive overview of MIP-based electrochemical methods for protein detection, including the principles, engineering aspects, advantages, and potential applications. The aim is to elucidate the potential of this cutting-edge technology in reshaping protein detection and its promising role in advancing biosensing technologies. Molecularly imprinted polymer ; Nanotechnology ; Computer science ; Biosensor ; Materials science ; Biochemical engineering ; Chemistry ; Selectivity ; Engineering Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Girmaw Abebe Tadesse | Jannis Born | Célia Cintas | William Ogallo | Dmitry Yu. Zubarev | Matteo Manica | Komminist Weldemariam
Source: ArXiv.org (preprint, 2023-01-20)
Field: Materials Science — Materials Chemistry

Domain-agnostic and Multi-level Evaluation of Generative Models While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplified quantities or manual inspection with limited practicality. To this end, we propose a framework for Multi-level Performance Evaluation of Generative mOdels (MPEGO), which could be employed across different domains. MPEGO aims to quantify generation performance hierarchically, starting from a sub-feature-based low-level evaluation to a global features-based high-level evaluation. MPEGO offers great customizability as the employed features are entirely user-driven and can thus be highly domain/problem-specific while being arbitrarily complex (e.g., outcomes of experimental procedures). We validate MPEGO using multiple generative models across several datasets from the material discovery domain. An ablation study is conducted to study the plausibility of intermediate steps in MPEGO. Results demonstrate that MPEGO provides a flexible, user-driven, and multi-level evaluation framework, with practical insights on the generation quality. The framework, source code, and experiments will be available at https://github.com/GT4SD/mpego. Computer science ; Generative grammar ; Domain (mathematical analysis) ; Feature (linguistics) ; Code (set theory) ; Source code ; Quality (philosophy) ; Data mining ; Generative model ; Machine learning ; Artificial intelligence ; Programming language ; Set (abstract data type) ; Mathematics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Elizabeth S. Thrall | Fernando Martinez Lopez | Thomas Egg | Seung Eun Lee | Joshua Schrier | Yijun Zhao
Source: ChemRxiv (preprint, 2023-10-03)
Field: Chemistry — Physical and Theoretical Chemistry

Rediscovering the particle in a box: Machine learning regression analysis for hypothesis generation in physical chemistry lab Given the growing prevalence of computational methods in chemistry, it is essential that undergraduate curricula introduce students to these approaches. One such area is the application of machine learning (ML) techniques to chemistry. Here we describe a new activity that applies ML regression analysis to the common physical chemistry laboratory experiment on the electronic absorption spectra of cyanine dyes. In the classic version of this experiment, students collect experimental spectra and interpret them using the Kuhn free electron model, based on the quantum mechanical particle-in-a-box (PIB). Our new computational activity has students train regression models of increasing complexity to predict the wavelength of maximum absorption for different cyanine dyes using a set of 13 molecular features. In addition, the activity introduces methods for evaluating and interpreting regression models. Ultimately, students are prompted to use their regression analysis results to generate hypotheses for what molecular properties underlie cyanine dye absorption, leading them naturally to the PIB model. In this report, we provide a dataset, reference code implementations in Mathematica and Python notebooks, and an example lab protocol with an introduction to cyanine dyes and the ML techniques. This activity can be completed in a single 3-hour lab period by upper-level undergraduate students with relatively little prior programming experience. Although intended to complement the experimental measurement of cyanine dye spectra, this activity can also be performed on its own; alternatively, it can form the basis of more involved projects in a computational chemistry or machine learning course. Cyanine ; Chemistry ; Python (programming language) ; Regression analysis ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Programming language ; Optics Various Chemistry Research Topics
Authors: Matteo Zanda
Source: Synlett (article, 2023-12-08)
Field: Chemistry — Organic Chemistry

SYNFORM ISSUE 2023/12 the group of S. G. Newman (Canada), who devel oped a novel deoxygenative Suzuki-Miyaura arylation of tertiary alcohols using silyl ethers and an unusual bisimidazolium Nheterocyclic carbene (NHC) ligand.A second Literature Coverage article stems from the work of R. Giri's group (USA) on a new approach to the intermolecular alkene cyclopropanation by using active methylene compounds -such as malonates -and photosensitized molecular oxygen.The issue is closed by a third Literature Coverage article, this one covering a fascinating subject, namely the synthesis and proper ties of cyclic sandwich compounds, recently published in Nature by the group of P. W. Roesky (Germany). Chemistry ; Earth science ; Geology N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Cyclopropane Reaction Mechanisms ; Catalytic Cross-Coupling Reactions
Authors: Tianfan Jin | Brett M. Savoie
Source: ChemRxiv (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

Deductive Machine Learning Challenges and Opportunities in Chemical Applications Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive, reasoning is required and still depends on manual intervention by an expert. This perspective describes the characteristics of deductive reasoning that are helpful for understanding the role played by expert intervention in problem-solving and why such interventions are often relatively resistant to disruption by typical machine learning strategies. The perspective then covers what factors contribute to creating a deductive bottleneck, how deductive bottlenecks are currently addressed in several application areas, and how machine learning models capable of deduction can be designed. The perspective concludes with a tutorial case-study that illustrates the challenges of deduction problems and a notebook for readers to experiment with on their own. Perspective (graphical) ; Bottleneck ; Deductive reasoning ; Computer science ; Artificial intelligence ; Intervention (counseling) ; Deductive method ; Multitude ; Inductive reasoning ; Management science ; Machine learning ; Data science ; Epistemology ; Engineering ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Taicheng Guo | Changsheng Ma | Xiuying Chen | Bozhao Nan | Kehan Guo | Shichao Pei | Nitesh V. Chawla | Olaf Wiest | Xiangliang Zhang
Source: arXiv (Cornell University) (preprint, 2023-10-07)
Field: Materials Science — Materials Chemistry

ReactionTeam: Teaming Experts for Divergent Thinking Beyond Typical Reaction Patterns Reaction prediction, a critical task in synthetic chemistry, is to predict the outcome of a reaction based on given reactants. Generative models like Transformer have typically been employed to predict the reaction product. However, these likelihood-maximization models overlooked the inherent stochastic nature of chemical reactions, such as the multiple ways electrons can be redistributed among atoms during the reaction process. In scenarios where similar reactants could follow different electron redistribution patterns, these models typically predict the most common outcomes, neglecting less frequent but potentially crucial reaction patterns. These overlooked patterns, though rare, can lead to innovative methods for designing synthetic routes and significantly advance synthesis techniques. To address these limitations, we build a team of expert models to capture diverse plausible reaction outcomes for the same reactants, mimicking the divergent thinking of chemists. The proposed framework, ReactionTeam, is composed of specialized expert models, each trained to capture a distinct type of electron redistribution pattern in reaction, and a ranking expert that evaluates and orders the generated predictions. Experimental results across two widely used datasets and different data settings demonstrate that our proposed method achieves significantly better performance compared to existing state-of-the-art approaches. Randomness ; Boosting (machine learning) ; Computer science ; Predictability ; Autoencoder ; Benchmark (surveying) ; Generative model ; Machine learning ; Artificial intelligence ; Generative grammar ; Mathematics ; Artificial neural network ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Daniel Cunnington | Flaviu Cipcigan | Rodrigo Neumann Barros Ferreira | Jonathan Booth
Source: arXiv (Cornell University) (preprint, 2023-11-30)
Field: Materials Science — Materials Chemistry

Symbolic Learning for Material Discovery Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare. A typical task in materials discovery is to search for a material in a database which maximises the value of a function. That function is often expensive to evaluate, and can rely upon a simulation or an experiment. Here, we introduce SyMDis, a sample efficient optimisation method based on symbolic learning, that discovers near-optimal materials in a large database. SyMDis performs comparably to a state-of-the-art optimiser, whilst learning interpretable rules to aid physical and chemical verification. Furthermore, the rules learned by SyMDis generalise to unseen datasets and return high performing candidates in a zero-shot evaluation, which is difficult to achieve with other approaches. Computer science ; Function (biology) ; Task (project management) ; Artificial intelligence ; Sample (material) ; Machine learning ; Data mining ; Engineering Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Topic Modeling
Authors: A.O. Glazachev | O.V. Ivanova | Д.А. Синицин | R.M. Akhmetshin
Source: Nanotechnologies in Construction A Scientific Internet-Journal (article, 2023-10-30)
Field: Materials Science — General Materials Science

Complementary improvement of technological characteristics of asphalt concrete road surfaces using macromolecular nanocomposites ABSTRACT: Introduction. The issue of enhancing the long-term performance of asphalt concrete pavement on highways is not entirely addressed by conventional solutions, such as improving the choice of material composition and refining the production process for bitumen binders and mineral filler compounds. One of the most promising ways to improve the performance and durability of asphalt concrete is the complementary modification of bitumen and road-concrete nanocompositions with thermoplastic and elastic polymers. Methods and materials. Bitumen binders are assessed employing suitable methodologies to determine the following technological parameters: extensibility, softening temperature, brittleness, elasticity, and so forth. Asphaltenes of bitumen binders are the most important structure-forming component and form associated nanoclusters with sizes of 15–200 nm. Thermoplastic block copolymers are introduced into the composition of bitumen binders in granular form (chips) or melted form. Additionally, compatibilizers are used to improve the compatibility of complementary copolymers in bitumen binders. The quality of the finished asphalt concrete composition is significantly affected by the characteristics of the main components: mineral powder, sand, crushed stone, bitumen binder and macromolecular nano-additives, as well as by optimal process modes: mixing temperature, etc. Results. The complementary interaction of supramolecular associations of asphaltenes and fragments of macromolecules of polymer nanocomposites ensures the formation of Van der Waals bonds due to spatial mutual correspondence. The introduction of polymer nanomodifiers leads to an increase in the softening temperature and a decrease in the brittleness temperature of bitumen binders due to the formation of sufficiently stable supramolecular complexes. Discussion. Synergistic nanoadditives of complementary macromolecules into the bitumen binder provides a fewfold increase in the adhesion of the bitumen composition and the mineral components, as well as a noticeable increase of the temperature range for plasticity and deformability. Asphalt concrete coatings with polymer-bitumen binders increase the operability of the roadway and resistance to the formation of plastic flow deformations (shifts, ruts) at high and low temperatures. Conclusion. Bitumen binders of asphalt road concrete, which have been modified with polymer nanocomposites, have higher adhesion, an extended range of thermoplasticity and water resistance Asphalt ; Nanocomposite ; Materials science ; Asphalt concrete ; Composite material ; Asphalt pavement ; Geotechnical engineering ; Forensic engineering ; Engineering Material Properties and Applications ; Polymer Synthesis and Characterization ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Changliang Zhu | Chenchao Fang | Zhipeng Jin | Baowen Li | Xiangying Shen | Lei Xu
Source: arXiv (Cornell University) (preprint, 2023-12-06)
Field: Materials Science — Materials Chemistry

A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks "AI for science" is widely recognized as a future trend in the development of scientific research. Currently, although machine learning algorithms have played a crucial role in scientific research with numerous successful cases, relatively few instances exist where AI assists researchers in uncovering the underlying physical mechanisms behind a certain phenomenon and subsequently using that mechanism to improve machine learning algorithms' efficiency. This article uses the investigation into the relationship between extreme Poisson's ratio values and the structure of amorphous networks as a case study to illustrate how machine learning methods can assist in revealing underlying physical mechanisms. Upon recognizing that the Poisson's ratio relies on the low-frequency vibrational modes of dynamical matrix, we can then employ a convolutional neural network, trained on the dynamical matrix instead of traditional image recognition, to predict the Poisson's ratio of amorphous networks with a much higher efficiency. Through this example, we aim to showcase the role that artificial intelligence can play in revealing fundamental physical mechanisms, which subsequently improves the machine learning algorithms significantly. Artificial intelligence ; Computer science ; Poisson distribution ; Mechanism (biology) ; Machine learning ; Convolutional neural network ; Artificial neural network ; Matrix (chemical analysis) ; Amorphous solid ; Algorithm ; Mathematics ; Physics ; Materials science Material Dynamics and Properties ; Topological and Geometric Data Analysis ; Nonlinear Optical Materials Studies
Authors: Matthias Germann | Adrian Hjältén | Jonathan Tennyson | S. N. Yurchenko | Iouli E. Gordon | Christian Pett | Isak Silander | Karol Krzempek | Arkadiusz Hudzikowski | Aleksander Głuszek | Grzegorz Soboń | Aleksandra Foltynowicz
Source: arXiv (Cornell University) (preprint, 2023-06-21)
Field: Chemistry — Spectroscopy

Optical frequency comb Fourier transform spectroscopy of formaldehyde in the 1250 to 1390 cm$^{-1}$ range: experimental line list and improved MARVEL analysis We use optical frequency comb Fourier transform spectroscopy to record high-resolution, low-pressure, room-temperature spectra of formaldehyde (H$_2$$^{12}$C$^{16}$O) in the range of 1250 to 1390 cm$^{-1}$. Through line-by-line fitting, we retrieve line positions and intensities of 747 rovibrational transitions: 558 from the $ν_6$ band, 129 from the $ν_4$ band, and 14 from the $ν_3$ band, as well as 46 from four different hot bands. We incorporate the accurate and precise line positions (0.4 MHz median uncertainty) into the MARVEL (measured active vibration-rotation energy levels) analysis of the H$_2$CO spectrum. This increases the number of MARVEL-predicted energy levels by 82 and of rovibrational transitions by 5382, and substantially reduces uncertainties of MARVEL-derived H$_2$CO energy levels over a large range: from pure rotational levels below 200 cm$^{-1}$ up to multiply excited vibrational levels at 6000 cm$^{-1}$. This work is an important step toward filling the gaps in formaldehyde data in the HITRAN database. Rotational–vibrational spectroscopy ; HITRAN ; Spectroscopy ; Excited state ; Line (geometry) ; Fourier transform spectroscopy ; Fourier transform ; Fourier transform infrared spectroscopy ; Range (aeronautics) ; Spectral line ; Formaldehyde ; Optical frequency comb ; Energy (signal processing) ; Chemistry ; Atomic physics ; Optics ; Analytical Chemistry (journal) ; Physics ; Materials science ; Laser ; Mathematics ; Geometry Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Laura Rieger | Max Wilson | Tejs Vegge | Eibar Flores
Source: ChemRxiv (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Understanding the patterns that neural networks learn from chemical spectra Analysing spectra from experimental characterization of materials is often time consuming, susceptible to distortions in data, requires specific domain knowledge, and may be susceptible to biases in general heuristics under human analysis. Recent work has shown the potential of using machine learning methods to solve this task, and provide automated and unbiased spectral analysis on- the-fly . We present a simple 1D neural network model to classify infrared spectra from small organic molecules, according to their functional groups. Our model is within range of state-of-the-art performance while being significantly less complex than previous works. A smaller network reduces the risk of overfitting and enables exploring what the model has learned about the underlying physics behind the spectra. Using explainability techniques, we show that our model learns the characteristic group frequencies of functional groups, and additionally uses non-intuitive patterns to classify spectra. We argue for the development of smaller yet accurate models to carry out tasks where model transparency is as important as prediction accuracy Overfitting ; Heuristics ; Computer science ; Machine learning ; Artificial intelligence ; Artificial neural network ; Task (project management) ; Interpretability ; Range (aeronautics) ; Pattern recognition (psychology) ; Data mining Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Javier Orera-Echeverría | Jacobo Ayensa-Jiménez | M. Doblaré
Source: arXiv (Cornell University) (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables Nonlinear materials are often difficult to model with classical state model theory because they have a complex and sometimes inaccurate physical and mathematical description or we simply do not know how to describe such materials in terms of relations between external and internal variables. In many disciplines, Neural Network methods have arisen as powerful tools to identify very complex and non-linear correlations. In this work, we use the very recently developed concept of Physically Guided Neural Networks with Internal Variables (PGNNIV) to discover constitutive laws using a model-free approach and training solely with measured force-displacement data. PGNNIVs make a particular use of the physics of the problem to enforce constraints on specific hidden layers and are able to make predictions without internal variable data. We demonstrate that PGNNIVs are capable of predicting both internal and external variables under unseen load scenarios, regardless of the nature of the material considered (linear, with hardening or softening behavior and hyperelastic), unravelling the constitutive law of the material hence explaining its nature altogether, placing the method in what is known as eXplainable Artificial Intelligence (XAI). Artificial neural network ; Nonlinear system ; State variable ; Computer science ; Constitutive equation ; Hyperelastic material ; Internal model ; Artificial intelligence ; Variable (mathematics) ; Physical law ; Statistical physics ; Mathematics ; Physics ; Engineering ; Mathematical analysis ; Structural engineering ; Control (management) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: Xiaokai Qin | Mingda Song | Yangguan Chen | Zhehong Ai | Jing Jiang
Source: arXiv (Cornell University) (preprint, 2023-09-15)
Field: Materials Science — Materials Chemistry

GPT-Lab: Next Generation Of Optimal Chemistry Discovery By GPT Driven Robotic Lab The integration of robots in chemical experiments has enhanced experimental efficiency, but lacking the human intelligence to comprehend literature, they seldom provide assistance in experimental design. Therefore, achieving full-process autonomy from experiment design to validation in self-driven laboratories (SDL) remains a challenge. The introduction of Generative Pre-trained Transformers (GPT), particularly GPT-4, into robotic experimentation offers a solution. We introduce GPT-Lab, a paradigm that employs GPT models to give robots human-like intelligence. With our robotic experimentation platform, GPT-Lab mines literature for materials and methods and validates findings through high-throughput synthesis. As a demonstration, GPT-Lab analyzed 500 articles, identified 18 potential reagents, and successfully produced an accurate humidity colorimetric sensor with a root mean square error (RMSE) of 2.68%. This showcases the rapid materials discovery and validation potential of our system. Computer science ; Robot ; Transformer ; Artificial intelligence ; Systems engineering ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Innovative Microfluidic and Catalytic Techniques Innovation