jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wanqiu Zhang | Nathan Heath Patterson | Nico Verbeeck | Jessica L. Moore | Alice Ly | Richard M. Caprioli | Bart De Moor | Jeremy L. Norris | Marc Claesen
Source: medRxiv (preprint, 2022-11-29)
Field: Chemistry — Spectroscopy

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma Abstract Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&amp;E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments. Computer science ; Artificial intelligence ; Pipeline (software) ; Pattern recognition (psychology) ; Microscopy ; Deep learning ; MALDI imaging ; Mass spectrometry imaging ; Artificial neural network ; Feature extraction ; Contextual image classification ; Pathology ; Mass spectrometry ; Image (mathematics) ; Matrix-assisted laser desorption/ionization ; Chemistry ; Medicine Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Sabina-Adriana Floria | Marius Gavrilescu | Florin Leon | Silvia Curteanu
Source: Applied Sciences (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Ensembles of Biologically Inspired Optimization Algorithms for Training Multilayer Perceptron Neural Networks Artificial neural networks have proven to be effective in a wide range of fields, providing solutions to various problems. Training artificial neural networks using evolutionary algorithms is known as neuroevolution. The idea of finding not only the optimal weights and biases of a neural network but also its architecture has drawn the attention of many researchers. In this paper, we use different biologically inspired optimization algorithms to train multilayer perceptron neural networks for generating regression models. Specifically, our contribution involves analyzing and finding a strategy for combining several algorithms into a hybrid ensemble optimizer, which we apply for the optimization of a fully connected neural network. The goal is to obtain good regression models for studying and making predictions for the process of free radical polymerization of methyl methacrylate (MMA). In the first step, we use a search procedure to find the best parameter values for seven biologically inspired optimization algorithms. In the second step, we use a subset of the best-performing algorithms and improve the search capability by combining the chosen algorithms into an ensemble of optimizers. We propose three ensemble strategies that do not involve changes in the logic of optimization algorithms: hybrid cascade, hybrid single elite solution, and hybrid multiple elite solutions. The proposed strategies inherit the advantages of each individual optimizer and have faster convergence at a computational effort very similar to an individual optimizer. Our experimental results show that the hybrid multiple elite strategy ultimately produces neural networks which constitute the most dependable regression models for the aforementioned process. Artificial neural network ; Computer science ; Perceptron ; Artificial intelligence ; Machine learning ; Convergence (economics) ; Process (computing) ; Neuroevolution ; Algorithm Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Metaheuristic Optimization Algorithms Research
Authors: Wojtek Treyde | Kai Riedmiller | Frauke Gräter
Source: ChemRxiv (preprint, 2022-06-29)
Field: Chemistry — Organic Chemistry

Bond dissociation energies of X−H bonds in proteins Knowledge of reliable X−H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X−H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite- level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning. Isodesmic reaction ; Dissociation (chemistry) ; Bond-dissociation energy ; Chemistry ; Radical ; Computational chemistry ; Density functional theory ; Crystallography ; Physical chemistry ; Organic chemistry Free Radicals and Antioxidants ; Advanced Chemical Physics Studies ; Redox biology and oxidative stress
Authors: Animesh Basak Chowdhury | Benjamin Tan | Ryan M. Carey | Tushit Jain | Ramesh Karri | Siddharth Garg
Source: arXiv (Cornell University) (preprint, 2022-04-05)
Field: Materials Science — Materials Chemistry

Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis Generating sub-optimal synthesis transformation sequences ("synthesis recipe") is an important problem in logic synthesis. Manually crafted synthesis recipes have poor quality. State-of-the art machine learning (ML) works to generate synthesis recipes do not scale to large netlists as the models need to be trained from scratch, for which training data is collected using time consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes a pre-trained model on past synthesis data to accurately predict the quality of a synthesis recipe for an unseen netlist. This approach on achieves 2x-10x run-time improvement and better quality-of-result (QoR) than state-of-the-art machine learning approaches. Netlist ; Recipe ; Computer science ; Artificial intelligence ; Quality (philosophy) ; State (computer science) ; Machine learning ; Programming language ; Embedded system Machine Learning in Materials Science ; Software Engineering Research ; VLSI and Analog Circuit Testing
Authors: Ruiyuan Kang | Dimitrios C. Kyritsis | Panos Liatsis
Source: arXiv (Cornell University) (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling Physics-based inverse modeling techniques are typically restricted to particular research fields, whereas popular machine-learning-based ones are too data-dependent to guarantee the physical compatibility of the solution. In this paper, Self-Validated Physics-Embedding Network (SVPEN), a general neural network framework for inverse modeling is proposed. As its name suggests, the embedded physical forward model ensures that any solution that successfully passes its validation is physically reasonable. SVPEN operates in two modes: (a) the inverse function mode offers rapid state estimation as conventional supervised learning, and (b) the optimization mode offers a way to iteratively correct estimations that fail the validation process. Furthermore, the optimization mode provides SVPEN with reconfigurability i.e., replacing components like neural networks, physical models, and error calculations at will to solve a series of distinct inverse problems without pretraining. More than ten case studies in two highly nonlinear and entirely distinct applications: molecular absorption spectroscopy and Turbofan cycle analysis, demonstrate the generality, physical reliability, and reconfigurability of SVPEN. More importantly, SVPEN offers a solid foundation to use existing physical models within the context of AI, so as to striking a balance between data-driven and physics-driven models. Artificial neural network ; Inverse problem ; Computer science ; Embedding ; Reconfigurability ; Context (archaeology) ; Nonlinear system ; Inverse ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies
Authors: Yeonghun Kang | Hyunsoo Park | Berend Smit | Jihan Kim
Source: Research Square (preprint, 2022-11-17)
Field: Chemistry — Inorganic Chemistry

Moftransformer: a Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-organic Frameworks Abstract In this work, we introduce MOFTransformer, a multi-model Transformer encoder pre-trained with 1 million hypothetical MOFs. The multi-modal model uses an integrated atom-based graph and energy-grid embeddings to capture both the local and global features of the MOFs, respectively. By fine-tuning the pre-trained model with small datasets (from 5,000 to 20,000), our model outperforms all other machine learning models across various properties that include gas adsorption, diffusion, electronic properties, and even text mined data. Beyond its universal transfer learning capabilities, MOFTransformer generates chemical insight by analyzing feature importance from attention scores within the self-attention layers. As such, this model can serve as a bedrock platform for other MOF researchers that seek to develop new machine learning models for their work. Modal ; Metal-organic framework ; Transformer ; Business ; Computer science ; Materials science ; Engineering ; Electrical engineering ; Voltage ; Chemistry ; Composite material ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Andy S. Anker | Emil T. S. Kjær | Mikkel Juelsholt | Troels Lindahl Christiansen | Susanne Linn Skjærvø | Mads R. V. Jørgensen | I. Kantor | Daniel Risskov Sørensen | Simon J. L. Billinge | Raghavendra Selvan | Kirsten M. Ø. Jensen
Source: ChemRxiv (preprint, 2022-04-25)
Field: Materials Science — Materials Chemistry

Extracting Structural Motifs from Pair Distribution Function Data of Nanostructures using Explainable Machine Learning Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, new automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive, and analysing the output, i.e., extracting structural information from the resulting fits in a meaningful way is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems including disordered nanomaterials and clusters. ML-MotEx opens for a new type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML. Extractor ; Bottleneck ; Computer science ; Artificial intelligence ; Machine learning ; Feature (linguistics) ; Pattern recognition (psychology) ; Engineering Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Xiaoli Chen | Phoebus Rosakis | Zhizhang Wu | Zhiwen Zhang
Source: arXiv (Cornell University) (preprint, 2022-06-24)
Field: Materials Science — Materials Chemistry

A Deep Learning Approach to Nonconvex Energy Minimization for Martensitic Phase Transitions We propose a mesh-free method to solve nonconvex energy minimization problems for martensitic phase transitions and twinning in crystals, using the deep learning approach. These problems pose multiple challenges to both analysis and computation, as they involve multiwell gradient energies with large numbers of local minima, each involving a topologically complex microstructure of free boundaries with gradient jumps. We use the Deep Ritz method, whereby candidates for minimizers are represented by parameter-dependent deep neural networks, and the energy is minimized with respect to network parameters. The new essential ingredient is a novel activation function proposed here, which is a smoothened rectified linear unit we call SmReLU; this captures the structure of minimizers where usual activation functions fail. The method is mesh-free and thus can approximate free boundaries essential to this problem without any special treatment, and is extremely simple to implement. We show the results of many numerical computations demonstrating the success of our method. Maxima and minima ; Minification ; Energy minimization ; Computation ; Artificial neural network ; Computer science ; Simple (philosophy) ; Energy (signal processing) ; Phase (matter) ; Function (biology) ; Deep learning ; Martensite ; Algorithm ; Potential energy ; Mathematical optimization ; Applied mathematics ; Artificial intelligence ; Mathematics ; Mathematical analysis ; Physics ; Microstructure ; Materials science ; Classical mechanics Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Microstructure and Mechanical Properties of Steels
Authors: John Patterson | Chris Avery | Tyler Grear | Donald J. Jacobs
Source: arXiv (Cornell University) (preprint, 2022-01-03)
Field: Chemistry — Spectroscopy

Biased Hypothesis Formation From Projection Pursuit The effect of bias on hypothesis formation is characterized for an automated data-driven projection pursuit neural network to extract and select features for binary classification of data streams. This intelligent exploratory process partitions a complete vector state space into disjoint subspaces to create working hypotheses quantified by similarities and differences observed between two groups of labeled data streams. Data streams are typically time sequenced, and may exhibit complex spatio-temporal patterns. For example, given atomic trajectories from molecular dynamics simulation, the machine's task is to quantify dynamical mechanisms that promote function by comparing protein mutants, some known to function while others are nonfunctional. Utilizing synthetic two-dimensional molecules that mimic the dynamics of functional and nonfunctional proteins, biases are identified and controlled in both the machine learning model and selected training data under different contexts. The refinement of a working hypothesis converges to a statistically robust multivariate perception of the data based on a context-dependent perspective. Including diverse perspectives during data exploration enhances interpretability of the multivariate characterization of similarities and differences. Interpretability ; Computer science ; Disjoint sets ; Context (archaeology) ; Artificial intelligence ; Linear subspace ; Projection (relational algebra) ; Machine learning ; Data stream mining ; Projection pursuit ; Data mining ; Multivariate statistics ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Biology Mass Spectrometry Techniques and Applications
Authors: Osazuwa G. Agbonlahor | Manoharan Muruganathan | Amit Banerjee | Hiroshi Mizuta
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Identification of Atmospheric Gases by Mapping the Graphene-Molecule Van Der Waals Complex Bonding Evolution van der Waals force ; Graphene ; Molecule ; Identification (biology) ; Van der Waals surface ; Van der Waals strain ; Chemical physics ; Materials science ; Nanotechnology ; Physics ; Van der Waals radius ; Quantum mechanics Machine Learning in Materials Science
Authors: Katarina Batalović | Jana Radaković | Bojana Paskaš Mamula | Bojana Kuzmanović | Mirjana Medić Ilić
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting Heat of Hydride Formation by the Graph Neural Network – Exploring Structure-Property Relation for Metal Hydrides Relation (database) ; Hydride ; Artificial neural network ; Property (philosophy) ; Metal ; Materials science ; Computer science ; Data mining ; Artificial intelligence ; Metallurgy ; Philosophy ; Epistemology Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Nikolay Kondratyuk | R. E. Ryltsev | Vladimir Ankudinov | N. M. Chtchelkatchev
Source: arXiv (Cornell University) (preprint, 2022-11-07)
Field: Materials Science — Materials Chemistry

Can we accurately calculate viscosity in multicomponent metallic melts? Calculating viscosity in multicompoinent metallic melts is a challenging task for both classical and \textit{ab~initio} molecular dynamics simulations methods. The former may not to provide enough accuracy and the latter is too resources demanding. Machine learning potentials provide optimal balance between accuracy and computational efficiency and so seem very promising to solve this problem. Here we address simulating kinematic viscosity in ternary Al-Cu-Ni melts with using deep neural network potentials (DP) as implemented in the DeePMD-kit. We calculate both concentration and temperature dependencies of kinematic viscosity in Al-Cu-Ni and conclude that the developed potential allows one to simulate viscosity with high accuracy; the deviation from experimental data does not exceed 9\% and is close to the uncertainty interval of experimental data. More importantly, our simulations reproduce minimum on concentration dependency of the viscosity at the eutectic point. Thus, we conclude that DP-based MD simulations is highly promising way to calculate viscosity in multicomponent metallic melts. Viscosity ; Ternary operation ; Thermodynamics ; Eutectic system ; Materials science ; Interval (graph theory) ; Molecular dynamics ; Parameterized complexity ; Computer science ; Chemistry ; Statistical physics ; Physics ; Algorithm ; Mathematics ; Alloy ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: David Ramírez | Teodoro Córdova–Fraga | C. Gómez-Solís | Delia Irazú Hernández-Farías
Source: Applied Physics A (article, 2022-07-06)
Field: Materials Science — Materials Chemistry

Data analysis for nano-material’s design by using diffraction pattern Nanomaterials ; Shortest path problem ; Path (computing) ; Parametric statistics ; Variable (mathematics) ; Path analysis (statistics) ; Diffraction ; Nano- ; Topology (electrical circuits) ; Computer science ; Plane (geometry) ; Materials science ; Biological system ; Algorithm ; Nanotechnology ; Mathematics ; Physics ; Geometry ; Theoretical computer science ; Machine learning ; Combinatorics ; Optics ; Mathematical analysis ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shujing Zhao | N. Birbilis
Source: Research Square (preprint, 2022-09-01)
Field: Materials Science — Metals and Alloys

Searching for chromate replacements using natural language processing and machine learning algorithms Abstract The past few years has seen the application of machine learning utilised in the exploration of new materials. As in many fields of research – the vast majority of knowledge is published as text, which poses challenges in either a consolidated or statistical analysis across studies and reports. Such challenges include the inability to extract quantitative information, and in accessing the breadth of non-numerical information. To address this issue, the application of natural language processing (NLP) has been explored in several studies to date. In NLP, assignment of high-dimensional vectors, known as embeddings, to passages of text preserves the syntactic and semantic relationship between words. Embeddings rely on machine learning algorithms and in the present work, we have employed the Word2Vec model, previously explored by others, and the BERT model – applying them towards a unique challenge in materials engineering. That challenge is the search for chromate replacements in the field of corrosion protection. From a database of over 80 million records, a down-selection of 5990 papers focused on the topic of corrosion protection were examined using NLP. This study demonstrates it is possible to extract knowledge from the automated interpretation of the scientific literature and achieve expert human level insights. Computer science ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Word2vec ; Natural language processing ; Interpretation (philosophy) ; Domain (mathematical analysis) ; Programming language Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms
Authors: Dawid Rymarczyk | Daniel Dobrowolski | Tomasz Danel
Source: arXiv (Cornell University) (preprint, 2022-10-07)
Field: Materials Science — Materials Chemistry

ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks. In contrast to other works, our model can be used to address various challenging tasks, including compound property prediction. In ProGReST, the rationale is obtained along with prediction due to the model's built-in interpretability. Additionally, we introduce a new graph prototype projection to accelerate model training. Finally, we evaluate PRoGReST on a wide range of chemical datasets for molecular property prediction and perform in-depth analysis with chemical experts to evaluate obtained interpretations. Our method achieves competitive results against state-of-the-art methods. Interpretability ; Computer science ; Property (philosophy) ; Graph ; Machine learning ; Regression ; Decision tree ; Artificial intelligence ; Range (aeronautics) ; Data mining ; Theoretical computer science ; Mathematics ; Engineering ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks