Introduction.
Modern science faces a problem that previous generations of researchers rarely encountered at the same scale: there is simply too much information.
Genomic databases contain enormous numbers of DNA sequences. Chemistry produces vast libraries of molecules and reactions. Physics experiments generate complex datasets from particle detectors, simulations, and instruments. Astronomy continuously produces observations containing billions of stars, galaxies, and other celestial objects.
Human researchers cannot manually examine all of these possibilities.
This is where artificial intelligence is becoming increasingly important.
AI can process enormous datasets, identify patterns, estimate relationships, generate hypotheses, predict molecular or physical properties, and help researchers decide which experiments deserve further investigation.
The transformation is already measurable. According to the Stanford Institute for Human-Centered AI's 2026 AI Index, approximately 80,150 AI-related publications appeared in the natural sciences in 2025, a 26% increase from 2024. Depending on the field, AI-related work represented approximately 5.8% to 8.8% of scientific research output, compared with less than 1% in 2010.
The important question is therefore no longer whether AI can be used in science.
The more important question is how researchers can use it responsibly to discover things that would otherwise be extremely difficult, expensive, or time-consuming to find.
This article examines that transformation across four major disciplines: biology, chemistry, physics, and astronomy.
What Is AI for Scientific Discovery?
AI for scientific discovery refers to the use of machine learning, deep learning, neural networks, generative models, scientific foundation models, optimization algorithms, and related computational techniques to assist researchers in understanding natural phenomena and discovering new knowledge.
Traditional scientific research generally follows a process such as
Observation → Hypothesis → Experiment → Analysis → Conclusion
AI can add several computational stages:
Data → Pattern Detection → Prediction → Hypothesis Generation → Experiment Selection → Validation
The critical distinction is that AI does not automatically turn a prediction into a scientific fact.
A model may identify a promising pattern, but researchers still need to test whether that pattern reflects a genuine physical, chemical, or biological phenomenon.
This distinction is especially important because the 2026 Stanford AI Index reports that leading AI systems can perform strongly on some scientific benchmarks while still struggling with complete research workflows and reproducibility. On one end-to-end research benchmark, the strongest AI agent scored 38.8%, compared with an 83.5% PhD-expert baseline.
AI is therefore best understood as a powerful research instrument rather than a replacement for scientific validation.
How AI Is Changing the Scientific Method
AI changes scientific research in several interconnected ways.
1. Faster Data Analysis
Scientists can use machine learning to analyze datasets containing millions or billions of observations.
2. Pattern Recognition
Neural networks can detect relationships that may be difficult to identify manually.
3. Prediction
Models can estimate properties before researchers perform expensive experiments.
4. Hypothesis Generation
AI systems can identify possible relationships that researchers can investigate experimentally.
5. Experiment Prioritization
Researchers can use predictive models to select the most promising experiments from a very large search space.
6. Automation
Robotic laboratories can combine computational predictions with automated experimentation.
The emerging model is increasingly a closed loop:
Research Question
↓
Scientific Data
↓
AI Model
↓
Prediction
↓
Experimental Test
↓
New Data
↓
Improved Model
↓
New Hypothesis
AI in Biology
Biology is one of the clearest examples of AI-assisted scientific discovery.
Biological systems contain enormous amounts of information, from DNA sequences and proteins to cells, tissues, organisms, ecosystems, and populations.
AI can help researchers work across these different levels.
Protein Structure Prediction
Proteins are biological molecules whose three-dimensional structures strongly influence their functions.
For decades, determining protein structures experimentally was a difficult scientific challenge.
AlphaFold changed this landscape dramatically.
In 2024, the Nobel Prize in Chemistry recognized Demis Hassabis and John Jumper for protein structure prediction and David Baker for computational protein design. The Nobel Committee described AlphaFold's work as solving a problem that researchers had worked on for approximately 50 years.
AlphaFold2 demonstrated that deep learning could predict many protein structures with remarkable accuracy.
The AlphaFold Protein Structure Database now provides access to more than 200 million protein structure predictions.
This matters because researchers can use predicted structures to investigate:
- disease mechanisms
- protein interactions
- drug targets
- enzyme functions
- biological pathways
- molecular evolution
- protein engineering
Genomics
AI is also being applied to DNA.
In 2025, Google DeepMind introduced AlphaGenome, a model designed to predict how DNA sequences and genetic variants affect molecular processes involved in gene regulation. The model can process DNA sequences of up to one million base pairs and predict thousands of molecular properties.
In September 2026, DeepMind introduced AlphaGenome Atlas, a resource containing predictions for the molecular effects of approximately 9 billion possible single-letter changes in the human genome.
These developments illustrate an important transition.
Instead of examining genetic variants one by one, researchers can use computational models to prioritize enormous numbers of possibilities before laboratory testing.
AI in Chemistry.
Chemistry is fundamentally a search problem.
Researchers may need to identify a molecule with a particular property, discover an efficient reaction pathway, develop a new material, or determine which experimental conditions are most likely to work.
The number of possible molecular combinations is enormous.
AI can reduce this search space.
Molecular Property Prediction.
Machine learning models can estimate properties such as:
- molecular stability
- solubility
- toxicity
- reactivity
- electronic properties
- binding affinity
- material performance
These predictions can help researchers prioritize candidates before laboratory testing.
Drug Discovery.
Drug discovery traditionally requires extensive screening and experimental testing.
AI can assist by identifying potential drug candidates, predicting molecular interactions, and suggesting compounds for further investigation.
However, computational prediction does not establish clinical effectiveness.
Laboratory studies, toxicology, pharmacological research, clinical trials, and regulatory review remain essential.
Chemical Reaction Planning
AI can also help researchers plan synthetic routes.
A 2025 perspective in Nature Computational Science described opportunities for large language models in chemical research, including planning, optimization, data analysis, automation, and knowledge management.
More specialized systems are also emerging.
For example, research published in Nature Machine Intelligence in 2025 described a reaction representation approach designed to help language models reason about chemical transformations and support retrosynthesis and experimental procedure recommendation.
In 2026, researchers reported MOSAIC, a framework using specialized computational chemical agents to support experimental protocol generation. Its reported experimental validation included more than 35 new compounds and an overall 71% success rate in the study's tested setting.
These results should be interpreted as research findings from specific experimental settings, not as a universal success rate for AI chemistry.
AI in Physics
Physics has a particularly interesting relationship with AI.
Modern physics depends on mathematical models, simulations, experiments, and enormous datasets.
Machine learning can assist at several points in this process.
Physics Simulations
Many physical systems are difficult to simulate because conventional numerical calculations can be computationally expensive.
Machine learning can sometimes provide approximate models that are much faster for particular tasks.
Potential applications include:
- fluid dynamics
- plasma physics
- materials physics
- particle physics
- climate-related physical systems
- quantum systems
- astrophysics
The goal is not necessarily to eliminate fundamental physical equations.
Instead, machine learning can complement existing mathematical models.
Particle Physics
Large particle-physics experiments generate huge volumes of detector data.
Machine learning can assist with:
- event classification
- particle identification
- anomaly detection
- detector calibration
- simulation
- reconstruction
This can help researchers locate unusual events within extremely large datasets.
A Remarkable Historical Connection
Physics also has an unusual connection to modern machine learning.
The 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton for foundational discoveries and inventions enabling machine learning with artificial neural networks. Their work drew directly on physics ideas.
This creates an important scientific feedback loop:
Physics helped inspire modern machine learning → machine learning is now helping physicists investigate complex physical systems.
AI in Astronomy
Astronomy may be one of the most naturally suited disciplines for machine learning.
Modern telescopes continuously produce enormous amounts of observational data.
Researchers must identify objects, classify galaxies, detect transient events, analyze spectra, and distinguish meaningful signals from noise.
Galaxy Classification
Machine learning can classify astronomical objects based on their observed properties.
This can accelerate studies of:
- galaxy morphology
- stellar populations
- quasars
- supernovae
- gravitational phenomena
- transient events
Exoplanet Detection
AI can analyze light curves from stars and identify patterns that may indicate planets passing in front of them.
Human scientists then examine promising candidates and apply additional validation methods.
Astronomical Foundation Models
A major development occurred in 2025 with AION-1, a multimodal foundation model for astronomical science.
The research describes models trained using data from five major surveys, representing more than 200 million observations of stars, galaxies, and quasars. The system was evaluated on tasks including galaxy and stellar property estimation, morphology classification, image segmentation, retrieval, and spectral super-resolution.
This represents a shift from highly specialized models toward broader scientific models capable of working across multiple astronomical data types.
How AI-Powered Scientific Discovery Works
A typical AI-assisted research workflow can be represented as follows.
Workflow Diagram
Scientific Question
↓
Data Collection
↓
Data Cleaning
↓
Feature Extraction or Representation
↓
Model Training
↓
Prediction
↓
Hypothesis Generation
↓
Experimental or Observational Testing
↓
Validation
↓
Scientific Interpretation
↓
Publication and Reproducibility
The most important stage is validation.
A model may produce a highly convincing prediction that is nevertheless wrong.
Scientific discovery requires evidence.
Academic Diagram: The AI Scientific Discovery Loop
┌───────────────────────┐
│ Scientific Question │
└───────────┬───────────┘
↓
┌───────────────────────┐
│ Experimental / │
│ Observational Data │
└───────────┬───────────┘
↓
┌───────────────────────┐
│ AI / Machine Learning │
│ Model │
└───────────┬───────────┘
↓
┌───────────────────────┐
│ Prediction & Pattern │
│ Discovery │
└───────────┬───────────┘
↓
┌───────────────────────┐
│ New Hypothesis │
└───────────┬───────────┘
↓
┌───────────────────────┐
│ Experiment / Test │
└───────────┬───────────┘
↓
Scientific
Validation
│
└──────────→ New DataKey Components of AI-Driven Science
AI-based scientific systems generally depend on several components.
1. High-Quality Data
Poor data can produce unreliable models.
2. Scientific Domain Knowledge
A model trained without sufficient scientific context may identify correlations without understanding their physical meaning.
3. Computational Infrastructure
Large models require substantial computing resources.
4. Specialized Algorithms
Different scientific problems require different approaches.
5. Experimental Validation
Predictions must be tested.
6. Human Scientific Judgment
Researchers remain responsible for interpreting results and deciding whether conclusions are scientifically justified.
Comparison: AI Across Four Scientific Disciplines
| Discipline | Major AI Applications | Typical Data | Scientific Benefit |
|---|---|---|---|
| Biology | Genomics, protein structures, cell analysis | DNA, proteins, microscopy, clinical and biological datasets | Faster biological interpretation |
| Chemistry | Molecular design, reaction prediction, materials discovery | Molecules, reactions, spectra, experiments | Reduced search space |
| Physics | Simulation, particle classification, anomaly detection | Detector data, simulations, measurements | Faster analysis and modeling |
| Astronomy | Object classification, spectroscopy, transient detection | Images, spectra, catalogs | Rapid analysis of massive observations |
A Second Comparison: Traditional and AI-Assisted Research
| Research Stage | Traditional Approach | AI-Assisted Approach |
|---|---|---|
| Data analysis | Manual/statistical processing | Automated pattern analysis |
| Candidate selection | Expert filtering | Predictive ranking |
| Simulation | Numerical models | Numerical + learned models |
| Hypothesis generation | Primarily human-driven | Human + computational suggestions |
| Experiment selection | Expert judgment | Optimization-assisted |
| Validation | Laboratory/observational testing | Laboratory/observational testing remains essential. |
The final row is crucial.
AI does not remove the need for empirical science.
Advantages of AI for Scientific Research
Faster Discovery
AI can examine enormous datasets far faster than manual analysis.
Larger Search Spaces
Researchers can investigate possibilities that would be impractical to evaluate individually.
Better Prioritization
Computational predictions can help determine which experiments deserve attention first.
Multimodal Analysis
Modern scientific models increasingly combine different data types.
For example, astronomy models can combine images and spectra, while biological systems can combine DNA sequence information with molecular measurements.
Reproducible Computational Workflows
Well-designed computational pipelines can make analytical procedures repeatable.
Discovery of Hidden Patterns
Machine learning can identify relationships that are difficult to detect using conventional methods.
Limitations and Challenges
The rapid progress of AI does not mean scientific problems have become easy.
1. Hallucinated or Incorrect Results
Generative systems can produce scientifically plausible but incorrect information.
2. Lack of Explainability
Some models can predict an outcome without providing a scientifically satisfying explanation.
3. Data Bias
If training data are incomplete or biased, predictions can inherit those limitations.
4. Reproducibility
A prediction is not a discovery unless other researchers can independently evaluate it.
5. Computational Cost
Large scientific models can require significant computing infrastructure.
6. Experimental Bottlenecks
AI can generate thousands of predictions, but laboratories cannot necessarily test thousands of hypotheses immediately.
7. Benchmark Limitations
Strong performance on a benchmark does not necessarily translate into successful real-world scientific research.
The Stanford 2026 AI Index specifically highlights this gap. Its science chapter reports that frontier models can perform strongly on some chemistry questions while still struggling with basic tasks and with reproducing published research.
Common Mistakes in AI-Assisted Scientific Research
Researchers should avoid several common mistakes.
Mistake 1: Treating Prediction as Fact
A model prediction is a hypothesis until independently validated.
Mistake 2: Ignoring Data Quality
Large datasets are not automatically good datasets.
Mistake 3: Optimizing Only for Benchmark Scores
A model can perform well on a benchmark without solving the actual scientific problem.
Mistake 4: Removing Human Oversight
Scientific interpretation requires domain expertise.
Mistake 5: Failing to Document Computational Methods
Researchers should report datasets, model versions, parameters, evaluation methods, and relevant computational procedures.
Best Practices
A responsible AI-assisted scientific workflow should include:
- Clearly define the scientific question.
- Identify appropriate datasets.
- Verify data provenance.
- Select an appropriate model.
- Establish strong baseline methods.
- Evaluate uncertainty.
- Test predictions on independent data.
- Perform experimental validation where possible.
- Document computational procedures.
- Make code and data available when appropriate.
- Reproduce important findings.
- Clearly distinguish predictions from experimentally established results.
Case Study: Protein Structure Prediction
The AlphaFold story provides a particularly important example.
Protein structure prediction had challenged scientists for decades.
AlphaFold2 demonstrated in 2020 that deep learning could achieve a major breakthrough in predicting protein structures. The significance of this work was formally recognized with the 2024 Nobel Prize in Chemistry.
The subsequent development of AlphaFold3 expanded the modeling task to interactions involving proteins, nucleic acids, and small molecules.
This illustrates a broader principle:
Scientific AI becomes particularly powerful when it converts an extremely difficult search problem into a computationally manageable prediction problem.
Case Study: Genomic Variant Interpretation
Genomics contains an enormous search space.
Researchers want to know which genetic variations influence gene regulation, disease, or biological traits.
AlphaGenome was introduced in 2025 to predict multiple regulatory properties from long DNA sequences. Its developers reported that it outperformed or matched leading external methods across many genomic benchmarks.
The later AlphaGenome Atlas extended this approach by precomputing predictions for approximately 9 billion possible single-letter DNA changes.
This is a good example of AI's role in prioritization.
Rather than experimentally testing every theoretical possibility, researchers can use computational predictions to narrow the search.
Case Study: Astronomy Foundation Models
Astronomy increasingly requires models capable of working with different observational modalities.
AION-1 was introduced as a multimodal foundation model trained using more than 200 million observations from major astronomical surveys. It was evaluated on tasks ranging from galaxy morphology classification to stellar-property estimation and spectral analysis.
This suggests a future in which scientific models are not built only for one narrow task.
Instead, researchers may use general scientific foundation models as computational infrastructure and adapt them to specific research questions.
Latest Research and Industry Trends
The 2026 AI Index identifies several major trends in scientific AI.
Natural-science AI publications reached approximately 80,150 in 2025, up 26% from 2024.
Astronomy saw the emergence of foundation-model infrastructure, including AION-1 and large multimodal datasets.
Chemistry is moving toward AI systems that can combine literature understanding, molecular reasoning, reaction planning, and experimental workflows.
Biology continues to expand beyond protein structure prediction toward genome regulation and variant interpretation.
These developments indicate that scientific AI is moving from isolated prediction models toward integrated research systems.
Five Scientific Charts to Add to the Blog
Because these should be based on verifiable data rather than invented numbers, the following chart specifications are recommended.
Chart 1: Growth of AI Publications in Natural Sciences
Data point:
- 2024: approximately 63,600
- 2025: approximately 80,150
Chart type: Line or column chart.
Source: Stanford AI Index 2026.
Chart 2: AI's Share of Scientific Research Output
Display the Stanford AI Index 2026 range:
- Approximately 5.8%–8.8%, depending on the field, in 2025
- Below 1% in 2010
Chart type: Range/column visualization.
Source: Stanford AI Index 2026.
Chart 3: AlphaGenome Benchmark Improvements
Use the published relative improvements reported by DeepMind, including:
- RNA expression: +17.4%
- selected variant-effect tasks: up to +25.5%
Chart type: Horizontal bar chart.
Source: Google DeepMind, 2025.
Chart 4: Astronomy Foundation-Model Dataset Scale
Show:
- More than 200 million astronomical observations
- Five major surveys
- AION-1 model variants from 300M to 3.1B parameters
Chart type: Infographic/column chart.
Source: AION-1 research paper, 2025.
Chart 5: AI Scientific Research Performance Gap
Compare the reported PaperArena results:
- AI agent: 38.8%
- PhD expert baseline: 83.5%
Chart type: Two-column comparison.
Important: label this as a specific benchmark rather than a general measure of scientific intelligence.
Timeline: AI and Scientific Discovery
1980s
Neural-network research incorporated ideas from statistical physics. John Hopfield developed associative-memory networks, while Geoffrey Hinton developed the Boltzmann machine.
2010s
Deep learning began producing major improvements in scientific image, sequence, and pattern-analysis tasks.
2020
AlphaFold2 demonstrated a major breakthrough in protein structure prediction.
2024
The Nobel Prize in Chemistry recognized computational protein design and protein structure prediction. The Nobel Prize in Physics recognized foundational work behind neural-network machine learning.
2025
AI scientific research expanded rapidly across biology, chemistry, physics, astronomy, and Earth sciences. The Stanford AI Index recorded approximately 80,150 AI-related natural-science publications.
2026
Scientific AI increasingly incorporates foundation models, multimodal systems, computational agents, and large-scale precomputed scientific datasets.
Ethical Issues
AI-driven scientific discovery raises important ethical questions.
Data Ownership
Researchers must understand whether datasets can legally and ethically be reused.
Scientific Attribution
AI-assisted research creates new questions about authorship and intellectual contribution.
Reproducibility
Important findings should remain independently testable.
Dual-Use Research
Some scientific discoveries may have beneficial as well as harmful applications.
Transparency
Researchers should disclose meaningful computational assistance and explain how models contributed to the work.
Access Inequality
Advanced computational infrastructure may be concentrated among wealthy institutions.
This creates a risk that scientific benefits could become unevenly distributed between countries and institutions.
The Future of AI for Scientific Discovery
The next stage of scientific AI is likely to move beyond prediction toward scientific agents and autonomous research workflows.
A future system could potentially:
- Read scientific literature.
- Identify an unresolved research question.
- Search databases.
- Generate competing hypotheses.
- Design computational experiments.
- Run simulations.
- Rank experimental possibilities.
- Send promising candidates to automated laboratory systems.
- Analyze experimental results.
- Update its hypotheses.
This is sometimes described as a closed-loop or self-driving laboratory.
However, autonomous research does not eliminate the need for scientists.
Instead, scientific expertise may shift toward:
- asking better questions
- designing validation strategies
- evaluating evidence
- interpreting mechanisms
- establishing causal explanations
- identifying limitations
- deciding which discoveries matter
The most productive future may therefore be collaborative rather than purely autonomous.
Academic Diagram: The Future Scientific Laboratory
┌─────────────────────┐
│ Scientific Question │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Scientific AI Model │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Hypothesis & Design │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Simulation / Robot │
│ Experiment │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Experimental Data │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Human Scientific │
│ Validation │
└──────────┬──────────┘
↓
New Knowledge
│
└──────→ New QuestionFrequently Asked Questions
1. What is AI for scientific discovery?
AI for scientific discovery is the use of machine learning and related computational methods to analyze scientific data, generate predictions, identify patterns, and assist researchers in developing and testing hypotheses.
2. How is AI used in biology?
AI is used in protein structure prediction, genomics, microscopy, drug discovery, biological sequence analysis, and interpretation of genetic variants.
3. How is AI changing chemistry?
AI can help predict molecular properties, design molecules, plan chemical reactions, discover materials, and prioritize laboratory experiments.
4. How is AI used in physics?
Applications include simulation, particle identification, anomaly detection, experimental analysis, materials research, and modeling complex physical systems.
5. How is AI used in astronomy?
AI helps classify galaxies and stars, analyze spectra, detect transient objects, identify possible exoplanets, and process massive astronomical datasets.
6. Can AI independently make scientific discoveries?
AI can generate useful predictions and hypotheses, but scientific discoveries generally require validation through appropriate computational, experimental, observational, or theoretical methods.
7. What is the biggest limitation of AI in science?
One major limitation is the gap between producing a plausible prediction and establishing a reliable scientific result. Reproducibility, data quality, interpretability, and experimental validation remain essential.
Key Takeaways
- AI is increasingly becoming part of the scientific research infrastructure.
- Natural-science AI publications grew substantially in 2025.
- Protein structure prediction demonstrated the transformative potential of scientific AI.
- Genomics is moving toward large-scale computational interpretation of genetic variation.
- Chemistry is increasingly combining AI with molecular design and automated experimentation.
- Physics benefits from machine learning for simulation and complex data analysis.
- Astronomy is adopting multimodal foundation models for massive observational datasets.
- AI predictions should not automatically be treated as scientific facts.
- Experimental and observational validation remain fundamental.
- The future of scientific AI is likely to emphasize human-AI collaboration and increasingly integrated research workflows.
Conclusion.
The rise of AI in science is not simply a story about faster computers or larger datasets.
It represents a change in how researchers can explore scientific possibility.
Biology can now use computational systems to investigate enormous genomic and protein spaces. Chemistry can use predictive models to search for molecules and reactions. Physics can apply machine learning to complex simulations and experimental data. Astronomy can use foundation models to analyze observations on scales that would be extremely difficult to process manually.
The most important lesson is that AI does not replace the scientific method.
It expands the range of questions scientists can investigate.
The strongest results emerge when computational models, scientific theory, experimental evidence, and human judgment work together.
The 2026 scientific landscape already demonstrates both sides of this transformation: AI is producing increasingly impressive results, while significant gaps remain in reliability, reproducibility, and end-to-end scientific reasoning.
Scientific discovery therefore remains a human intellectual enterprise, but the instruments available to scientists are changing rapidly.
The next major breakthroughs may come not from AI working alone, but from researchers learning how to combine scientific expertise with increasingly capable computational systems.
Your Next Step.
For students and researchers, the most valuable response to this transformation is to develop both scientific knowledge and computational literacy.
Learning statistics, programming, data analysis, machine learning, scientific visualization, and research methodology can help the next generation participate in this emerging era of computational science. External Reference Links.
Stanford AI Index 2026 — Science
https://hai.stanford.edu/ai-index/2026-ai-index-reportNobel Prize in Chemistry 2024
https://www.nobelprize.org/prizes/chemistry/2024/summary/Nobel Prize in Physics 2024
https://www.nobelprize.org/prizes/physics/2024/summary/Google DeepMind—AlphaGenome
https://deepmind.google/en/blog/alphagenome-ai-for-better-understanding-the-genome/Google DeepMind — AlphaGenome Atlas
https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/Google DeepMind — AlphaFold
https://deepmind.google/en/science/alphafold/Nature — Chemical Research and Large Language Models
https://www.nature.com/search?q=chemical+research+large+language+modelsNature—AI and Chemical Reaction Representation
https://www.nature.com/search?q=AI+chemical+reaction+representationNature — AI-Assisted Chemical Synthesis
https://www.nature.com/search?q=AI-assisted+chemical+synthesisAION-1 Astronomy Foundation Model
https://proceedings.neurips.cc/paper_files/paper/2025/hash/893df77404832e974b097b361ef49623-Abstract-Conference.html
#ArtificialIntelligence#ScientificInnovation#ScientificDiscovery#AIResearch#Astronomy#ScienceAndTechnology#MachineLearning#Biology#Chemistry#Physics#ComputationalScience#AIinScience
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