jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fabio J. Cortés Rodríguez | Lucien F. Krapp | Matteo Dal Peraro | Luciano A. Abriata
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-11-27)
Field: Materials Science — Materials Chemistry

HandMol: Coupling WebXR, AI and HCI technologies for Immersive, Natural, Collaborative and Inclusive Molecular Modeling Abstract Except for isolated developments and specific software extensions, molecular graphics and modeling have historically been stuck at flat screens for visualization, mouse operations for molecular manipulation, menus and command line interfaces for controls, and single-user interfaces that only allow collaboration by streaming video hence limited to just sharing the view of the user operating the software. We demonstrate here how various technologies are ripe enough to enable much more fluent, immersive and natural human-computer interactions that in turn facilitate collaboration between human users, using affordable hardware through the internet and without even installing any specialized programs. For this, we introduce HandMol, a web app that exploits (i) WebXR for molecular visualization and manipulation in virtual reality, (ii) speech recognition coupled to a large language model to pass commands orally, (iii) speech synthesis for auditory feedback, (iv) WebRTC to communicate multiple instances of the tool without even requiring a server, and (v) external APIs to flexibly account for molecular mechanics, exemplified here with an endpoint running an AMBER forcefield for protein and nucleic acids and another running a DFT-trained neural network, ANI-2x, to allow exploration of conformation and some simple reactivity at high speed and accuracy. We show example applications to situations from daily work and education in chemistry and structural biology where HandMol can provide an advantage over traditional software: exploring and explaining molecular conformations and reactivity, docking and undocking small molecules into/out of protein pockets, threading molecules through nanopores, preparing systems for molecular simulations and for protein design, etc. We also present a brief study showing how users, even with limited or even no experience in VR, can significantly benefit from these kinds of technologies. As a draft prototype for the moment, HandMol is made available free of charge and without registration at https://go.epfl.ch/handmol , in (optional but greatly appreciated) exchange for feedback on usability and on features expected for this kind of tools. Computer science ; Software ; Visualization ; Human–computer interaction ; User interface ; Molecular graphics ; OpenGL ; Graphics ; Exploit ; Graphical user interface ; Workstation ; Virtual reality ; Computer graphics ; Computer graphics (images) ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Ganesh Kamath | Alexey Illarionov | Serzhan Sakipov | Leonid Pereyaslavets | Igor V. Kurnikov | Oleg Butin | Е. Н. Воронина | Ilya Ivahnenko | Igor Leontyev | Grzegorz Nawrocki | Mikhail Darkovskiy | Michael Olevanov | Yevhan Cherniavskyi | Christopher Lock | Sean Greenslade | Hulda Chen | Roger D. Kornberg | Michael Levitt | Boris Fain
Source: ChemRxiv (preprint, 2023-11-29)
Field: Materials Science — Materials Chemistry

Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions We present a formalism of a neural network encoding of bonded interactions in molecules. This intramolecular encoding is consistent with models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a corresponding neural network may be used to economically improve representation of torsional degrees of freedom in any force field. We test the accuracy of the reproduction of the ab initio potential energy surface on a set of conformations of two dipeptides - methyl-capped ALA and ASP - in several scenarios. The encoding, either alone or in conjunction with an analytical potential, improves agreement with ab initio energies on par with that of other neural network-based potentials. Using the encoding and neural nets in tandem with an analytical model places the agreements firmly within ‘chemical accuracy’ of ± 0.5 kcal/mol. Artificial neural network ; Intramolecular force ; Force field (fiction) ; Ab initio ; Formalism (music) ; Encoding (memory) ; Molecule ; Representation (politics) ; Intermolecular force ; Computer science ; Computational chemistry ; Biological system ; Chemistry ; Physics ; Artificial intelligence ; Stereochemistry ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Y. Liu | Milena Jovanović | Krishnanand Mallayya | Wesley J. Maddox | Andrew Gordon Wilson | Sebastian Klemenz | Leslie M. Schoop | Eun-Ah Kim
Source: arXiv (Cornell University) (preprint, 2023-12-05)
Field: Materials Science — Materials Chemistry

Materials Expert-Artificial Intelligence for Materials Discovery The advent of material databases provides an unprecedented opportunity to uncover predictive descriptors for emergent material properties from vast data space. However, common reliance on high-throughput ab initio data necessarily inherits limitations of such data: mismatch with experiments. On the other hand, experimental decisions are often guided by an expert's intuition honed from experiences that are rarely articulated. We propose using machine learning to "bottle" such operational intuition into quantifiable descriptors using expertly curated measurement-based data. We introduce "Materials Expert-Artificial Intelligence" (ME-AI) to encapsulate and articulate this human intuition. As a first step towards such a program, we focus on the topological semimetal (TSM) among square-net materials as the property inspired by the expert-identified descriptor based on structural information: the tolerance factor. We start by curating a dataset encompassing 12 primary features of 879 square-net materials, using experimental data whenever possible. We then use Dirichlet-based Gaussian process regression using a specialized kernel to reveal composite descriptors for square-net topological semimetals. The ME-AI learned descriptors independently reproduce expert intuition and expand upon it. Specifically, new descriptors point to hypervalency as a critical chemical feature predicting TSM within square-net compounds. Our success with a carefully defined problem points to the "machine bottling human insight" approach as promising for machine learning-aided material discovery. Machine learning ; Artificial intelligence ; Intuition ; Computer science Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Geochemistry and Geologic Mapping
Authors: Manjeet Bhatia
Source: ChemRxiv (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

A Gaussian process regression (GPR) quest to predict HOMO-LUMO energy Machine learning methods employ statistical algorithms and pattern recognition techniques to learn patterns and make predictions based on statistical patterns. Global reactivity descriptors, such as HOMO-LUMO energy, chemical potential (µ), chemical hardness (η), softness (σ) and electrophilic index (ω) are predicted using Gaussian process regression (GPR) machine learning method. GPR predicted values are in close agreement with the values obtained via ab initio methods. Over 85% prediction accuracy in HOMO energies and reactivity parameters is observed, while LUMO energies were in good range with the DFT evaluations. An appropriate kernel combination with proper tuning of parameters and the selection of quality correlated data can make the GPR model robust and powerful. Machine learning models like GPR could play a pivotal role in assisting and accelerating ab initio calculations and providing insights for highly complex molecular systems. Kriging ; HOMO/LUMO ; Ground-penetrating radar ; Ab initio ; Artificial intelligence ; Gaussian ; Gaussian process ; Kernel (algebra) ; Machine learning ; Computer science ; Computational chemistry ; Mathematics ; Chemistry ; Physics ; Quantum mechanics ; Radar ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Antonio Sanna | Tiago F. T. Cerqueira | Yue‐Wen Fang | Ion Errea | Alfred Ludwig | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

Prediction of Ambient Pressure Conventional Superconductivity above 80K in Thermodynamically Stable Hydride Compounds The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far. Here, we propose a family of compounds, of composition Mg$_2$XH$_6$ with X$=$Rh, Ir, Pd, or Pt, that achieves this goal. These materials were identified by scrutinizing more than a million compounds using a machine-learning accelerated high-throughput workflow. They are thermodynamically stable, indicating that they are serious candidates for experimental synthesis. We predict that their superconducting transition temperatures are in the range of 45-80K, or even above 100K with appropriate electron doping of the Pt compound. These results indicate that, although very rare, high-temperature superconductivity in thermodynamically stable hydrides is achievable at room pressure. Superconductivity ; Ambient pressure ; Hydride ; Materials science ; Doping ; Thermodynamics ; Condensed matter physics ; Physics ; Metallurgy ; Metal ; Optoelectronics Machine Learning in Materials Science ; Rare-earth and actinide compounds ; Inorganic Chemistry and Materials
Authors: Saman ALVANDI | Seyed Saeid Mohtasebi | Mahmoud Omid | Mohammad Hosseinpour-Zarnaq
Source: Spanish Journal of Agricultural Research (article, 2023-05-08)
Field: Chemistry — Analytical Chemistry

Development of an online Nigella sativa inspection system equipped with machine vision technology and artificial neural networks Aim of study: Nigella sativa L. seeds usually are mixed with impurities, which affect its quality and influences consumer acceptance in both raw seeds and the oil market. In this study, an intelligent system based on the combination of machine vision (MV) and artificial neural networks (ANN) was developed to classify and clean N. sativa seeds and its impurities. Area of study: Iran, Kurdistan province. Material and methods: For accurate detections we developed a robust image processing algorithm including image acquisition, image enhancement, segmentation, and feature extraction steps. Correlation-based Feature Selection method was used to select the superior features. Three methods of linear discriminant analysis, support vector machines, and ANN were used to classify the data. Main results: The statistical indices of sensitivity, specificity, and accuracy for N. sativa in the online phase were 90%, 98.93%, and 97.04%, respectively. The average of these measurements for the impurities class was 95.57%, 96.89%, and 96.58%, respectively. Research highlights: The results demonstrated the feasibility of suggested machine learning and image processing approaches in the real-time cleaning of N. sativa. The image acquisition and processing process, including selection of the best lighting methods to reduce the shadows, noise elimination and segmentation, provided precise results. The final results indicated the effectiveness of proposed machine learning algorithm in feature extraction, feature dimensionality reduction, and classification approaches. This methodology can be recommended for detection, classification and automatic cleaning of other similar seeds. Artificial intelligence ; Computer science ; Support vector machine ; Linear discriminant analysis ; Artificial neural network ; Machine vision ; Feature extraction ; Feature selection ; Pattern recognition (psychology) ; Image processing ; Machine learning ; Image segmentation ; Segmentation ; Computer vision ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Nigella sativa pharmacological applications
Authors: Ihor Neporozhnii | Zhibo Wang | Rochan Bajpai | Camilo Ariel Jaime Gomez | Nirvik Chakraborty | Isaac Tamblyn | Oleksandr Voznyy
Source: ChemRxiv (preprint, 2023-09-13)
Field: Materials Science — Materials Chemistry

Navigating Materials Space with ML-Generated Electronic Fingerprints In the field of materials science, finding materials with specific properties is a major challenge due to the vastness of the search space, which makes random exploration prohibitively expensive. A more practical approach is to search for new materials within the proximity of known compounds that possess the desired property. In such an approach, fingerprinting methods are often used to measure the similarity of materials and group them into clusters. Such methods rely exclusively on the material’s structure to generate the fingerprint, which often does not correspond to clustering by desired property. To address this issue, electronic structure fingerprints that use properties such as the density of states (DOS) and band structure were proposed as an alternative. However, the computational cost of electronic structure calculations for tens of thousands of materials remains too high for rapid exploration. In this work, we developed a Graph Neural Network (GNN) ProDosNet which is trained on orbital Projected Density of States (PDOS) data and capable of predicting the electronic structure of materials at extremely low computational cost. With this model, we were able to generate PDOS fingerprints for all compounds present in the Materials Projects Database and cluster them by the similarity of their orbital PDOS, and therefore electronic properties. We demonstrate that using PDOS fingerprints allows finding materials that have similar electronic properties but drastically different structures. Electronic structure ; Computer science ; Similarity (geometry) ; Fingerprint (computing) ; Property (philosophy) ; Cluster analysis ; Cluster (spacecraft) ; Space (punctuation) ; Field (mathematics) ; Data mining ; Materials science ; Artificial intelligence ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Kiho Matsubara | Kei Takahashi | Takeshi Matsuda | Yuji Ueki | Noriaki Seko | Ryohei Kakuchi
Source: ChemPlusChem (article, 2023-10-31)
Field: Materials Science — Biomaterials

GFN‐xTB‐Based Computations Provide Comprehensive Insights into Emulsion Radiation‐Induced Graft Polymerization** In this article, a deep insight into emulsion radiation-induced graft polymerization (RIGP) was obtained by computing explicit solvation free energies, conformational entropy, monomer radius and dipole moments with the state-of-the-art Conformer-Rotamer Ensemble Sampling Tool (CREST) package primarily at semiempirical GFN-xTB level. By leveraging the robustness of the CREST package, above parameters provided dynamic nature of methacrylate monomers with the consideration of realistic emulsion conditions. With the chemical and physical importance of the above results, CREST-determined explanatory variables sufficiently led to the building of the prediction models for the RIGP of methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion. Polymerization ; Emulsion polymerization ; Emulsion ; Materials science ; Computation ; Nanotechnology ; Polymer chemistry ; Polymer science ; Computer science ; Chemistry ; Polymer ; Organic chemistry ; Composite material Electrospun Nanofibers in Biomedical Applications ; Polymer Surface Interaction Studies ; Luminescence and Fluorescent Materials
Authors: Wei Lai | Qin Li | Sadman Sadeed Omee | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2023-07-12)
Field: Materials Science — Materials Chemistry

Towards Quantitative Evaluation of Crystal Structure Prediction Performance Crystal structure prediction (CSP) is now increasingly used in the discovery of novel materials with applications in diverse industries. However, despite decades of developments, the problem is far from being solved. With the progress of deep learning, search algorithms, and surrogate energy models, there is a great opportunity for breakthroughs in this area. However, the evaluation of CSP algorithms primarily relies on manual structural and formation energy comparisons. The lack of a set of well-defined quantitative performance metrics for CSP algorithms make it difficult to evaluate the status of the field and identify the strengths and weaknesses of different CSP algorithms. Here, we analyze the quality evaluation issue in CSP and propose a set of quantitative structure similarity metrics, which when combined can be used to automatically determine the quality of the predicted crystal structures compared to the ground truths. Our CSP performance metrics can be then utilized to evaluate the large set of existing and emerging CSP algorithms, thereby alleviating the burden of manual inspection on a case-by-case basis. The related open-source code can be accessed freely at https://github.com/usccolumbia/CSPBenchMetrics Computer science ; Set (abstract data type) ; Strengths and weaknesses ; Field (mathematics) ; Quality (philosophy) ; Data mining ; Machine learning ; Similarity (geometry) ; Code (set theory) ; Source code ; Artificial intelligence ; Energy (signal processing) ; Algorithm ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Calcium Carbonate Crystallization and Inhibition
Authors: Jingrui Liu
Source: Highlights in Science Engineering and Technology (article, 2023-07-11)
Field: Chemistry — Analytical Chemistry

Review Of Near Infrared Spectrum Detection Based on Deep Learning Near infrared spectroscopy technology has the characteristics of accurate analysis of material composition and characteristics, quantitative testing, non-destructive testing, and so on. With the progress of artificial intelligence and the development of deep learning technology, near-infrared spectroscopy detection systems based on deep learning algorithms have emerged, which include various typical methods and have achieved good application results in food safety monitoring, pollutant detection, and drug analysis. This article provides a systematic review of domestic and foreign literature, introducing the principles, characteristics, and development history of deep learning and near-infrared spectroscopy technology. It discusses the research significance of near-infrared spectroscopy detection based on deep learning. It also reviews the latest progress in deep learning based near-infrared spectroscopy related technologies, elaborates on the advantages, disadvantages, and applicable fields of the methods, and makes prospects and predictions for the future development trends in this field. Deep learning ; Artificial intelligence ; Computer science ; Spectroscopy ; Near-infrared spectroscopy ; Infrared ; Data science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Maria Alexandra Theodorescu | Jacob Furst | Daniela Raicu | Roselyne Tchoua
Source:  (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Leveraging Interpretable Features and Clustering Agreements for Assistive Materials Discovery Data science is intrinsically interdisciplinary; however, end-users of machine learning models are not always trained data scientists. For this reason, it is crucial that these models be infused with domain knowledge to increase explainability and trust in their output. Our goal is to assign domain-aware confidence scores to models' outputs to help experts make informed decisions. Our hypothesis is that given consensus-based confidence scores, end-users will be more willing to trust and thus adopt machine learning models. We test this hypothesis in materials informatics, a field that has the potential to greatly reduce time-to-market and development costs for new materials as it leverages machine learning and large datasets for targeted materials design. Automated phase-mapping seeks to discover the relationship between materials synthesis and materials phases, as materials with similar structure are likely to have similar properties and vice versa. A particular challenge is identifying composition-phase regions, i.e., regions of composition space where materials are composed of similar lattice-structure phases. This is challenging because structure measurements per sample are high dimensional and suffer from the curse of dimensionality, making results difficult to interpret and generalize. Towards our goal, we quantify agreements (or disagreements) across carefully chosen clustering methods used to discover composition-phase regions by defining confidence (or uncertainty) scores to identify the correct number of phase regions present in X-Ray Diffraction (XRD) datasets. Building a series of clustering experiments, we show that we can narrow in on the correct number of phase regions in two ternary composition spreads. Cluster analysis ; Computer science ; Curse of dimensionality ; Machine learning ; Artificial intelligence ; Data science ; Data mining ; Domain (mathematical analysis) ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Geochemistry and Geologic Mapping
Authors: E. A. Huerta | Hyun Park | Xiaoli Yan | Ruijie Zhu | Santanu Chaudhuri | Donald E. Cooper | Ian Foster | Emad Tajkhorshid
Source: Research Square (preprint, 2023-06-30)
Field: Chemistry — Inorganic Chemistry

GHP-MOFassemble: Diffusion modeling, high throughput screening, and molecular dynamics for rational discovery of novel metal-organic frameworks for carbon capture at scale Scale (ratio) ; Throughput ; Diffusion ; Carbon fibers ; Metal-organic framework ; Nanotechnology ; Dynamics (music) ; Materials science ; Chemistry ; Computer science ; Physics ; Physical chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography