jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huat Thart Chiang | Kiran Vaddi | Lilo D. Pozzo
Source: ChemRxiv (preprint, 2023-08-28)
Field: Materials Science — Materials Chemistry

Data-Driven Exploration of Silver Nanoplate Formation inMultidimensional Chemical Design Spaces We present an autonomous data-driven framework that iteratively explores the experimental design space of silver nanoparticle synthesis to obtain control over the formation of a desired morphology and size. The objective of the method is to autonomously identify design rules such as the effects of the design variables on the structure of the nanoparticle. The framework balances multimodal characterization methods (i.e. UV-Vis spectroscopy, SAXS, TEM), taking into account the cost of performing a measurement and the quality of information gained. By integrating with an AI agent, we identify important design variables in the synthesis of plate-like silver particles and outline how each variable affects plate thickness, radius, and polydispersity. Our findings are consistent with the literature, demonstrating that the automation framework could be further applied to new systems that have not been well characterized and understood. The framework is generalizable and allows tangible knowledge extraction from the high-throughput experimental runs while still taking inherent stochasticity into consideration. Dispersity ; Automation ; Computer science ; Characterization (materials science) ; Nanotechnology ; Nanoparticle ; Variable (mathematics) ; Quality by Design ; Process engineering ; Throughput ; Data-driven ; Materials science ; Artificial intelligence ; Mechanical engineering ; Particle size ; Engineering ; Mathematics ; Chemical engineering Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Joshua Schrier | Alexander J. Norquist | Tonio Buonassisi | Jakoah Brgoch
Source: ChemRxiv (preprint, 2023-05-19)
Field: Materials Science — Materials Chemistry

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science Exceptional molecules and materials with one (or more) extraordinary properties are both technologically valuable and fundamentally interesting because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for realizing new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data is limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it, and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows that should enable the discovery of exceptional molecules and materials. Serendipity ; Interpretability ; Workflow ; Computer science ; Chemical space ; Data science ; Identification (biology) ; Task (project management) ; Artificial intelligence ; Lead (geology) ; Focus (optics) ; Space (punctuation) ; Nanotechnology ; Systems engineering ; Drug discovery ; Engineering ; Chemistry ; Epistemology ; Materials science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Matthew R. Carbone | Hyeong Jin Kim | Chandima Fernando | Shinjae Yoo | Daniel Olds | Howie Joress | Brian DeCost | Bruce Ravel | Yugang Zhang | Phillip M. Maffettone
Source: arXiv (Cornell University) (preprint, 2023-07-26)
Field: Materials Science — Materials Chemistry

Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimental campaigns through the lens of maximizing some observable. This framing is insufficient for epistemic research goals that seek to comprehensively analyze a sample space, without an explicit scalar objective (e.g., the characterization of a wafer or sample library). In this work, we propose a flexible formulation of scientific value that recasts a dataset of input conditions and higher-dimensional observable data into a continuous, scalar metric. Intuitively, the scientific value function measures where observables change significantly, emulating the perspective of experts driving an experiment, and can be used in collaborative analysis tools or as an objective for optimization techniques. We demonstrate this technique by exploring simulated phase boundaries from different observables, autonomously driving a variable temperature measurement of a ferroelectric material, and providing feedback from a nanoparticle synthesis campaign. The method is seamlessly compatible with existing optimization tools, can be extended to multi-modal and multi-fidelity experiments, and can integrate existing models of an experimental system. Because of its flexibility, it can be deployed in a range of experimental settings for autonomous or accelerated experiments. Observable ; Bayesian optimization ; Computer science ; Framing (construction) ; Fidelity ; Design of experiments ; Sample (material) ; Machine learning ; Mathematics ; Physics ; Engineering Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Data Classification
Authors: Bowen Wang | Liang Chen | Jiaze Wang | Furui Liu | Shaogang Hao | Dong Li | Jianye Hao | Guangyong Chen | Xiaolong Zou | Pheng‐Ann Heng
Source: arXiv (Cornell University) (preprint, 2023-03-06)
Field: Materials Science — Materials Chemistry

DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with finer supervision strategies have been applied to boost and guide the relaxation process of an atomic system and better predict its properties at the equilibrium state. In this paper, we present a novel graph neural network (GNN) supervision and prediction strategy DR-Label. The method enhances the supervision signal, reduces the multiplicity of solutions in edge representation, and encourages the model to provide node predictions that are graph structural variation robust. DR-Label first Deconstructs finer-grained equilibrium state information to the model by projecting the node-level supervision signal to each edge. Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm. The DR-Label strategy was applied to three radically distinct models, each of which displayed consistent performance enhancements. Based on the DR-Label strategy, we further proposed DRFormer, which achieved a new state-of-the-art performance on the Open Catalyst 2020 (OC20) dataset and the Cu-based single-atom-alloyed CO adsorption (SAA) dataset. We expect that our work will highlight crucial steps for the development of a more accurate model in equilibrium state property prediction of a catalysis system. Computer science ; Artificial neural network ; Enhanced Data Rates for GSM Evolution ; Relaxation (psychology) ; Graph ; Artificial intelligence ; Algorithm ; Theoretical computer science Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Ming-Chiang Chang | Sebastian Ament | Maximilian Amsler | Duncan R. Sutherland | Lan Zhou | John M. Gregoire | Carla P. Gomes | R. B. van Dover | Michael O. Thompson
Source: arXiv (Cornell University) (preprint, 2023-08-15)
Field: Materials Science — Materials Chemistry

Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes. However, rapid, automated and reliable analysis method of XRD data matching the incoming data rate remains a major challenge. To address these issues, we present CrystalShift, an efficient algorithm for probabilistic XRD phase labeling that employs symmetry-constrained pseudo-refinement optimization, best-first tree search, and Bayesian model comparison to estimate probabilities for phase combinations without requiring phase space information or training. We demonstrate that CrystalShift provides robust probability estimates, outperforming existing methods on synthetic and experimental datasets, and can be readily integrated into high-throughput experimental workflows. In addition to efficient phase-mapping, CrystalShift offers quantitative insights into materials' structural parameters, which facilitate both expert evaluation and AI-based modeling of the phase space, ultimately accelerating materials identification and discovery. Computer science ; Probabilistic logic ; Workflow ; Bayesian probability ; Matching (statistics) ; Data mining ; Algorithm ; Tree (set theory) ; Phase (matter) ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Markus J. Buehler
Source: MRS Bulletin (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Multiscale materials innovation from the bottom up, at the nexus of biology, engineering, and computation Nexus (standard) ; Computation ; Top-down and bottom-up design ; Materials science ; Engineering physics ; Nanotechnology ; Engineering ; Computer science ; Software engineering ; Algorithm Machine Learning in Materials Science ; Nanotechnology research and applications
Authors: Stefano A. Cerri
Source: Chemical Synthesis (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Information, knowledge, and human learning for chemistry: the visionary contribution of Professor Alain Krief This is not a paper about the current state of chemistry but rather the author’s perspective on a strategy for the future of chemistry and other scientific and technical disciplines. This future vision is based on synergies of natural sciences and technical disciplines on one side and the science of information, i.e., informatics, on the other. It relates the author’s interactions during 2003-2023 in professional collaborations with Alain Krief, now Emeritus Professor at the University of Namur. It is argued that the scientific value of Prof. Krief’s work - particularly related to his well-known interest in “chemical knowledge transfer” - extends beyond the scientific-technical domain of chemistry to include a much broader area of natural sciences and associated technologies. Prof. Krief’s signal contribution has resulted from his profound interest in information, knowledge, and human learning, which has been at the core of our exchanges during the last 20 years. Informatics ; Natural (archaeology) ; Natural science ; Perspective (graphical) ; Engineering ethics ; Sociology of scientific knowledge ; Domain (mathematical analysis) ; Chemistry ; Computer science ; Epistemology ; Engineering ; Philosophy ; Artificial intelligence ; Mathematics ; Biology History and advancements in chemistry ; Semantic Web and Ontologies ; Advanced Text Analysis Techniques
Authors: Viswambari R Devi | Michael Keidar
Source: Preprints.org (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally. Leverage (statistics) ; Transformative learning ; Plasma medicine ; Personalized medicine ; Precision medicine ; Computer science ; Cancer therapy ; Artificial intelligence ; Cancer treatment ; Reinforcement learning ; Machine learning ; Cancer ; Plasma ; Bioinformatics ; Medicine ; Atmospheric-pressure plasma ; Psychology ; Internal medicine ; Physics ; Biology Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Air Quality Monitoring and Forecasting
Authors: Vasileios Boglou | Dimosthenis Verginadis | Athanasios Karlis
Source: Preprints.org (preprint, 2023-09-15)
Field: Chemistry — Analytical Chemistry

Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation The flour milling industry, a vital component of global food production, is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality, and sustainability in flour production. Accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient, and cost-effective method for measuring the aforementioned essential parameters in wheat and flour, by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the under-study parameters. Context (archaeology) ; Process engineering ; Water content ; Environmental science ; Spectrometer ; Raw material ; Moisture ; Wheat flour ; Pulp and paper industry ; Computer science ; Agricultural engineering ; Food science ; Materials science ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses
Authors: Hugo Jair Escalante | Aleksandra Kruchinina
Source: arXiv (Cornell University) (preprint, 2023-12-01)
Field: Materials Science — Materials Chemistry

Academic competitions Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed. Popularity ; Academic community ; Context (archaeology) ; Closing (real estate) ; Data science ; State (computer science) ; Engineering ethics ; Computer science ; Political science ; Engineering ; Geography ; Library science Machine Learning in Materials Science ; Machine Learning and Data Classification
Authors: Yi Ren | Samuel Lavoie | Mikhail Galkin | Danica J. Sutherland | Aaron Courville
Source: arXiv (Cornell University) (preprint, 2023-10-28)
Field: Materials Science — Materials Chemistry

Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of research in cognitive science has hypothesized a process, ``iterated learning,'' to help explain how human language developed this ability; the theory rests on simultaneous pressures towards compressibility (when an ignorant agent learns from an informed one) and expressivity (when it uses the representation for downstream tasks). Inspired by this process, we propose to improve the compositional generalization of deep networks by using iterated learning on models with simplicial embeddings, which can approximately discretize representations. This approach is further motivated by an analysis of compositionality based on Kolmogorov complexity. We show that this combination of changes improves compositional generalization over other approaches, demonstrating these improvements both on vision tasks with well-understood latent factors and on real molecular graph prediction tasks where the latent structure is unknown. Generalization ; Computer science ; Iterated function ; Principle of compositionality ; Artificial intelligence ; Representation (politics) ; Graph ; Theoretical computer science ; Process (computing) ; Machine learning ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Bing Huang | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2023-04-06)
Field: Materials Science — Materials Chemistry

Towards self-driving laboratories: The central role of density functional theory in the AI age Density functional theory (DFT) plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and computational efficiency. We review recent progress in machine learning model developments which has relied heavily on density functional theory for synthetic data generation and for the design of model architectures. The general relevance of these developments is placed in some broader context for the chemical and materials sciences. Resulting in DFT based machine learning models with high efficiency, accuracy, scalability, and transferability (EAST), recent progress indicates probable ways for the routine use of successful experimental planning software within self-driving laboratories. Density functional theory ; Transferability ; Context (archaeology) ; Relevance (law) ; Computer science ; Scalability ; Artificial intelligence ; Data science ; Functional theory ; Machine learning ; Management science ; Engineering ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Monica Allen | Kwabena Bediako | William J. Bowman | Michelle A. Calabrese | Lucas Caretta | Rose K. Cersonsky | Wen Chen | Santiago Correa | Rachel D. Davidson | Leora E. Dresselhaus‐Marais | Carissa N. Eisler | Ariel L. Furst | Ting Ge | Andrew L. Hook | Yi‐Ting Hsu | Chunjing Jia | Jianfeng Lu | Alessandro Lunghi | Marco S. Messina | Ivan A. Moreno‐Hernandez | Eva M. Nichols | Reshma R. Rao | Martin Seifrid | Katherine E. Shulenberger | Alexandr N. Simonov | Xiao Su | Dayne F. Swearer | Evelyn Ting‐Hsuan Tang | Mercedes K. Taylor | Helen Tran | Gustavo F. Trindade | Ryan L. Truby | Hendrik Utzat | Ying Yang | Daryl W. Yee | Shenlong Zhao | Steve Cranford
Source: Matter (article, 2023-08-01)
Field: Chemistry — Inorganic Chemistry

35+1 challenges in materials science being tackled by PIs under 35(ish) in 2023 Materials science ; Engineering ethics ; Nanotechnology ; Engineering Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Nathan J. Szymanski | Pragnay Nevatia | Christopher J. Bartel | Yan Zeng | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2023-04-19)
Field: Materials Science — Materials Chemistry

Autonomous decision making for solid-state synthesis of inorganic materials To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target phase, ARROWS3 iteratively proposes experiments and learns from their outcomes to identify an optimal set of precursors that leads to maximal yield of that target. Initial experiments are selected based on thermochemical data collected from first principles calculations, which enable the identification of precursors exhibiting large thermodynamic force to form the desired target. Should the initial experiments fail, their associated reaction paths are determined by sampling a range of synthesis temperatures and identifying their products. ARROWS3 then uses this information to pinpoint which intermediate reactions consume most of the available free energy associated with the starting materials. In subsequent experimental iterations, precursors are selected to avoid such unfavorable reactions and therefore maintain a strong driving force to form the target. We validate this approach on three experimental datasets containing results from more than 200 distinct synthesis procedures. When compared to several black-box optimization algorithms, ARROWS3 identifies the most effective set of precursors for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of using domain knowledge in the design of optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms. Computer science ; Set (abstract data type) ; Identification (biology) ; Automation ; Range (aeronautics) ; Domain (mathematical analysis) ; Selection (genetic algorithm) ; Solid-state ; Yield (engineering) ; Algorithm ; Machine learning ; Materials science ; Chemistry ; Mathematics ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions